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History of Fort Johnson: Iron County Utah
Pioneer Magazine September 2, 2019
by L. Royce Griffin, Cotton Mission Chapter
For protection from Indian depredations during the Walker War of 1853-54, and later during the black hawk war, most communities in Iron County erected some type of fortification.Little remains of those forts today except that which has been preserved in the journals and histories of those who were there, and by stories and traditions of generations of family that followed.There is one little fort, though that seems to have captured not only the hearts of those many relatives who came later, but even by the community itself, and that was Fort Johnson.
Named after one of its chief architects and builders, Joel Hills Johnson, it provided the security and protection they needed during a period of uncertainty and conflict.In this narrative an attempt was made not only to paint a picture of what the fort looked like, and identify those who lived there during those early years, but also to give a sense of the seriousness of the Indian war itself and the concern those at the fort and in nearby communities had for their safety.
Even though no deaths occurred at the fort or in the nearby towns it nevertheless, was a costly conflict in terms of maintaining an active Militia, the movement and displacement of citizens like those at Fort Johnson and Paragonah and the construction of the forts themselves. History and information for this account was drawn not only from earlier writers such as Bell Armstrong, Estella Jones Grimshaw and Susannah Dalley Armstrong and others, but also from the extensive journal entries and writings of Joel Hills Johnson and his sons, Nephi and Seth.Military documents from that period were also reviewed as well as old newspaper articles.
This narrative is not intended to be a complete history of Enoch, leading up to modern times, as that has been thoroughly written about by other, but only covers the early years when the fort existed and on through the transition period of the late 1800s. Hopefully, this account will not only give one a sense of the dangers and challenges those early pioneers faced at Fort Johnson, but also give honor to such families as the Johnsons, Dalleys, Merrells, Grimshaws, Armstrongs, Jones and others. – L. Royce Griffin February, 2019
Up until the 1870s, pioneers and settlers in frontier America, including the Territory of Utah, were faced with the threat of hostile Indian activities, both in terms of personal safety and loss of livestock. That, of course, was also the case in the small communities of Southern Utah, where church leaders had sent groups of church members to colonize and develop the resources. To protect themselves, towns both small and large, constructed some kind of fortification. Generally the projects involved the combined efforts of a large group of local men, but in at least one case, a small, but significant structure was erected by just one man, his family and a handful of neighbors. That man, which history perhaps remembers best because of the poems and hymns he wrote, such as 'High on a Mountain Top', became, among other things, a fort builder.
Joel Hills Johnson, for which the fort was named, had been selected by Apostle George A. Smith in the fall of 1850 to assist in forming a settlement in what was originally referred to as the Little Salt Lake Valley. He sent two of his sons ahead with provisions as part of the settlement party then joined them, along with other members of his family in April of 1851. On the 15th of May, President Brigham Young and many of the brethren from Great Salt Lake City arrived on an exploring and visiting expedition. While there they organized the settlement which became known as Parowan, and among other actions, elected Joel Hills to be a member of the city council.
Later, he was sent out with a small company to evaluate the coal, timber and iron deposits in the area, around what would eventually be Cedar City. On his way back to Parowan, Joel Hills discovered several springs at a location he referred to as, 'at the edge of the Coal Creek Valley.' Feeling that it would be a favorable location for his farming operations and a place to build his home, he consulted with George A. Smith, upon his return to Parowan, who then gave him leave to survey all the land he pleased at the springs for him and his friends. Later on he was counseled by Elder Smith to build a house and corrals, herd the cattle belonging to the brethren from Parowan and Cedar City, and establish a sugar beet plantation and sugar manufacturing operation. 1
On the 17th of November he selected a site near the springs, which was approximately 12 miles south west of Parowan, and began the construction of a new house, completing enough of it by December 15th to move all of his family in. Over the following year he continued to develop his property, which by that time was generally referred to as johnson springs, and of course, manage, and help protect the herd of cattle he had been entrusted with.
Joel Hills Johnson, the namesake of Fort Johnson
]Joel hills Johnson was a prolific composer of poetry and music and it was while at Johnson Springs that he wrote what became his most famous hymn, one which even today is regularly sung by church congregations and choirs. In his journal he said,
"Sometime in November my health began to decline very much so that I was not able to do any labor, and I soon discovered that my complaint was dropsy (now understood as congestive heart failure) in the chest which brought me so low that I was only able to set up part of the day at a time. And while reflecting on the scenes of my past life, the sickness, persecution, and sorrows that has been my life to pass through from my youth up, and probably of my soon leaving this world of affliction for one more glorious, the words of the Apostle John upon the Isle of Patmos was continually sounding in my ears by night and by day, "And he said unto me, Write." So that I could not rest until I had obtained the necessary materials and commenced writing, when my mind was led to write songs and hymns upon the suffering of the saints , the principles that appertain to the building up of the Kingdom of God on the earth in the last days, etc., the spirit of which is like fire in my bones and I have no rest unless occupied in this way. Towards spring through the prayers of the saints and the blessings of my Heavenly Father, my health commenced to mend slowly and continued until I was able to attend to some business but not to labor."
And so it was that on February 18, 1853, during this period of trial, that he composed the hymn, 'High on a Mountain Top.'2
As with other pioneer settlements, the Johnsons, as well as other who made Johnson Springs or Fort Johnson their home, were not spared trials and hard times, whether it be sickness, death or something else. A complete record of such events, of those who comprised the early occupants of the fort, does not exist but the death of one of the Johnsons young children, 4 year old Joseph, was noted in the Deseret News on November 17, 1858. The Morrill family also had a little one named Edgar, who died there. But with the deaths there were also a number of children who were brought into the world at the fort including two born to the Johnsons, four in the Laban Morrill family, one to James and Lucinda Bay and five to the Dalley family. And there was at least one marriage, for on January 8, 1852, John L. Smith, who had been called by Brigham Young to preside over Iron County, and accompanied by a number of guests, made a visit to the Johnson home, where by request, he married James H. Martineau, a 23 year old school teacher from Parowan and Susan, the 15 year old daughter of Joel Hills Johnson. He said that it was the first ceremony of the kind he had performed and that they had an excellent dinner together and spent a pleasant day.3
During that period of time they did not have any type of fortification for their protection but they still had trouble with the Indians. In the spring of 1852 his son, Sixtus found that one of his oxen was missing. After a day long search, he determined that it had been driven off by two Indians. Twelve men from Parowan were recruited to aid in the search, but again without any success. On the fourth day Sixtus and three others again took up the trail, where after some twelve miles they found where the ox had been killed. Eventually they found the Indians in a camp where they were in the process of drying the meat. The raiding party consisted of two old Indians and two young boys, one about 12 years old and the other 5. They were taken prisoner but while bringing them back to town, they attempted to escape during which one of the older Indians was killed and the other who was wounded, got away along with the oldest boy. They brought the younger Indian boy back and Joel Hills took him into his family, giving him the name of Sam.4
Joel's wife, Janet was a very timid woman and was especially afraid of the Indians. When Joel was away and she thought Indians were near, she would stuff some pants and a shirt, put on a coat, shoes and a hat like a man asleep on a bed. Then she would stand the gun by the bed so it looked like Joel when he would lie down during the day.5
Fear of Indian attacks increased in July of 1853 with the outbreak of the Walker War. With the settler's occupation of lands, which the Indians had used for hunting and gathering, and the attempt by the church leaders to suppress the Indian slave trade with the Mexicans, tensions were already high when an incident occurred in Springville in which a Ute was killed. Seeking revenge, a period of violence then ensued between the Ute Indians, led by Chief Walker, and the Militia that lasted until early 1854.
An incident occurred in April, 1853 which highlighted the alarm felt by the pioneers in reference to Chief Walker and his band, and their subsequent actions to protect themselves. The Deseret News reported:
In April, 1853 a party of emigrants passed through Parowan on their way to Southern California and stole a very fine pair of horses from a Brother Brafli. Knowing that they would have to camp for water at the Iron Springs, about thirty miles distance, a party of eleven men organized to follow them and recover the animals, intending to come upon their camp the next morning about daylight. The bustle made in getting ready excited the curiosity of a couple of Utah Indians, members of a large force of several hundreds of warriors under the great Chief Walker camped at Summit Creek, seven miles distance. With customary caution they did not ask any grown person what was the cause of the stir, but inquired of a little boy. He told them the party was going out to fight Walker. The Utes hurried to Walker's camp, and told him of the intended attack, and he at once prepared to receive it.
In ignorance of all this the little party of eleven men armed with rifles, accompanied by Dr. P. Meeks and Samuel Gould, who were unarmed, started out about sunset, and coming in sight of the Indian camp dashed forward on the run, yelling in true boyish style. This convinced Walker that he was about to be attacked as he had been told and his strategy was worthy his great reputation as "King of the Mountain." He and Ammon, his brother, rode out to meet the advancing party and halted near a small ravine. He began to talk, and our little party clustered near to hear what was said. While this talk was going on Indians on horseback and on foot streamed forth on the right and left in an unconcerned manner, as if to gather up their horses, but soon began to circle around us. Even then we did not suspect anything, but thought that they, too, simply wished to listen as we did. But after a little while it was noticed that they were watching our party rather than Walker, and that they were drawn up to perfect order. Surrounding our party at a distance of about fifteen paces was a circle of about thirty warriors on horseback, each armed with a rifle, bow and quiver of arrows.
Beyond this circle was another, composed of men on foot armed with bows and arrows; and beyond them was still another circle of footmen armed with rifles, while the little ravine was lined with dusky faces and rifle barrels. Walker vehemently talked in Ute language, which Ammon, who could speak English fluently, interpreted from time to time. Walker asked why we were there at that time of day with guns in our hands. Lieut. Col. J. A. Little, our leader, told him we were going after some stolen horses. Walker said it was a lie–we had lost no horses–we had come to fight. He was told he was mistaken; we did not want to fight, but had lost horses and wanted to get them back from the Americanos who had stolen them. He said our place was to stay at our wickiups and dig the ground and work, and he became very much excited. Suddenly the Indians all leveled and cocked their guns , the bowmen stood with their murderous arrows ready to fly, and the outer circle kneeling upon one knee, leveled their rifles upon two sticks crossed to form a rest for their guns. We were helpless, not a man had his gun in position for use—we were all huddled together in a compact heap; and had they fired not a soul would have survived.
It was a very disagreeable thing to look down the muzzle of a loaded rifle with an angry Indian at the other end, expecting, as we did, every moment to feel the crash of a bullet, or, worse still, to be pierced through with arrows. Though expecting death my only fear was that of being wounded and then tortured. But when Ammon said again that the Americanos had not taken the horses, an inspiration came to Col. Little, and he answered, "Well, then, boys, we had better go home again," at the same time riding through the ranks, followed by all the party. The Indians seemed dazed for a time and did not oppose the movement. Once outside the trap some wanted to fight, but Col. Little said, "No, we must let the people at home know what is going on," and away we went followed by a party of mounted Indians in hot pursuit. The time we made was excellent—seldom beaten—and Parowan never seemed so beautiful as when we came near it and our pursuers turned back. 6 J. H. Martineau
Upon their return a council was called in which it was determined that an express rider should be sent to Governor Brigham Young to inform him of their state of affairs. The rider, Samuel Lewis, made what was one of the most epic rides ever recorded. Averaging almost seventy miles a day, he made it to Salt Lake City and back, a distance of 520 miles, in just eight days, with orders to avoid all trouble or conflict, as Parowan was to distant to receive any help.
On July 20th however, Elder Franklin D. Richards arrived in Parowan with word that an Indian war had commenced, and a man named Keel had been killed at Payson. Then on August 2nd Col. George A. Smith, who had been given command of all units south of Salt Lake by President Young and General Daniel H. Wells, along with Lieut. Col. W. H. Kimball and thirty-six men, arrived with orders that all settlers living outside of forts must move in to larger communities with secure forts; that Paragonah, four miles from Parowan and Johnson Springs must be abandoned.7
A statistical report of the forts in the Southern Military District in August of 1853 revealed the following:
Parowan: located on Center Creek; 59 men, 55 guns, 13 pistols and 56 families.
Paragonah: Located on Red Creek; 19 men, 19 guns, 0 pistols and 17 families.
Cedar City: Located on Coal Creek; 122 men, 94 guns, 26 pistols and 80 families
Johnson Springs did not have a fort at the time so no data was included.
With the assistance of the militia troops from Salt Lake City, Joel Hills Johnsons home was pulled down and moved to a new fort which had been constructed south of Coal Creek in Cedar City. After the communities were put under Martial Law and affairs arranged in a satisfactory condition, the detachment returned to Salt Lake City.
Under the direction of Elders Erastus Snow and Franklin D. Richards of the Quorum of the Twelve, a conference was held in November of 1853 in which Joel Hills was appointed to return to The Springs, take care of the herd grounds once again and establish a missionary station to teach the Indians the gospel and art of farming and civilization, and construct a fort. To assist in the work he was authorized to select seven or eight families to go with him, five of which included: Laban Morrel, Samuel White, William and James Dalley and James W. Bay.8
In June of 1854 he laid out a fort, about ten rods square, or a little over 0 .6 acres and hired men to make twenty thousand adobe bricks. Made from clay found in the local area, the bricks were formed in wooden molds measuring 14 by 18 inches in size. By September they had enough bricks to begin laying up one of the eight foot walls. Constructed on a foundation of large stones, some of which can still be seen today, the adobe bricks measured 2 ½ feet thick at the base and 18 inches at the top.
In his autobiography, Joel's son, Nephi, gave a little different account, describing the walls as being 4 feet thick and 12 feet high, but adding, "It was a good solid fort when built and was calculated to keep out Indians." By January of 1855 the enclosure was complete. A large wooden gate, made of logs, was constructed on the north side which opened into a lane going west which proceeded to the northwest corner of the field, then turned to the south. At some point, a smaller opening, measuring 6X4 feet was also constructed on the south side. A two story bastion occupied the northwest corner of the fort with another in the southeast corner. A set of stairs led to the upper level of each where a lookout could watch the countryside. At intervals around the walls of the fort were port-holes for defenders to shoot if an attack was ever made on the fort.
To provide a secure and convenient source of water, a well was dug in the center of the enclosure with a sweep for pulling the water from the well in buckets. A second source of water came from the ditch that ran along the south side of the fort which could be accessed through the small opening just referred to. Apple orchards were planted on the outside of the fort, both on the east and the west. There was also a vegetable garden on the west. A picket fence was erected inside the walls to hold the cattle, when necessary. Cottonwood trees were also planted along the east and south sides to provide shade. The remains of some of those enormous cottonwoods can still be seen today. The blacksmith shop, grainery and chicken coop were located on the inside along the west and north walls.
Several houses were constructed in the fort along the south and west walls, the largest being a two story structure near the south wall which was occupied by Joel Hills and his family. James and Emma Dalley's home was in the southeast corner. All were constructed of adobe about eighteen inches thick with roofs of poles and willows covered with dirt. Small windows in the dwellings looked inward toward the central square.9
The series of springs located between the fort and the low lying hills to the east, along with the meadowlands associated with them, were what first attracted Joel Hills to the area. Residents of the surrounding towns were also aware of the lush grazing land and took their cattle herds there which, of course, Joel Hills Johnson had been assigned to manage. A ditch was dug, and a reservoir also constructed by those at the fort to irrigate fields of hay and grain fields located to the south.
Expresses constantly arrived from the north with news of Indian depredations and orders not to relax in their readiness to meet the enemy at any time. Consequently, every community resembled an armed camp; all men capable of bearing arms met for roll call morning and evening, and every man kept on hand a supply of crackers or biscuits in readiness for any sudden expedition or scout. Even though they were militia soldiers, they were expected to fulfill their duty, with action taken against them if they did not, as illustrated in the following document.10
Col. W. M. Dame, Fort Parowan Nov. 19, 1854
Sir, According to your order of the 18th there was a Court Marshal held in this fort, and Privates S. Hoyt and E. Ward were brought before said court and were charged with neglect of duty which charges were, sustained, and the court fined them $3.00 each. – Elijah Johnson, Capt., Company B, Infantry Regiment
With abandonment of Paragonah, a fort was also constructed in Parowan. Made of damp earth pounded in a box with brush and straw, it measured six feet at the base and tapered to two and a half feet at the top. The twelve foot high wall was a mile long, and enclosed the fort which was a quarter of a mile square. During the day a lookout was kept, a man being stationed in a kind of belfry on top the meeting house with a spy glass who scanned the area for any suspicious activity. As an extra precaution, orders were even given to fill in any holes and move any fences that were close to the fort so as to deny hiding places for any Indians that might attack. Other regulations were also received such as Order #4 issued on August 5, 1853, which read.11
To the Commanding officers of Posts. You will immediately change the order of the guard by dropping the counter sigh and the usual handling. Let the sentinels secret themselves and act in silence and caution, giving them such instructions as may be deemed proper. Be careful or men will be shot on guard before we are aware of danger. By order of Lieut. Col. L. A. Settle, Commanding.
By the middle of July a peace was finally made with Chief Walker and hostilities subsided, but overall trouble with the Indians never ceased in Utah until the early 1870s when the government removed all the Ute Indians to the Uintah and Ouray Reservations reservation in the Uintah Basin. Before that happened however, another war ensued, referred to as the Black Hawk War, during the years 1865 and 1866. Although no settlers were killed in Southern Utah by the Indians during the Walker War, the cost was never the less, high, both in terms of time devoted by the militia, the destruction of property such as the removal of structures like those in Paragonah and Johnson Springs, and the cost of building fortifications. Joel Hills Johnson said he personally went into debt several hundred dollars to construct Fort Johnson.
Although there was no direct attack on Fort Johnson by the Indians, they did have a serious and potentially fatal confrontation with them. In 1849 during the Indian War in Utah County, a young girl was captured and taken in by Joel Hills and treated in all respects as a member of his family. The young Indian girl, whom they named Viragua, thrived and did well. When she was about twelve of thirteen years old, an old Indian chief whom the settlers called Squash Head, came to Fort Johnson intending to take her with him, as he claimed she was his sister. Thinking she was alone, he was evidently going to put her on his horse and dash away, but two of Joel Hills sons, Nephi and Seth were in the house at the time working on a couple of garden tools. Armed with a grubbing hoe and an ax, the two brothers confronted the Indian who stood in the doorway armed with a rifle and knife, and full of rage. Nephi said the girl did not want to go with him and that she was not his sister. They stood some minutes staring each other in the eye; the one seeking an opportunity to use his knife of rifle—the other warily guarding against any such move, but never flinching for a moment. Seeing that Nephi could bash him over the head before he could use his weapons, Squash Head jumped on his horse, joined a couple of other braves outside, and dashed away yelling defiance and future revenge. The poor girl, terrified at her narrow escape, had no peace of mind until Joel Hills sent her to Salt Lake to live with his sister, Julia Babbitt.12
Not all raiders were dispensed of by means of rifles and cavalry however, as recorded in a humorous event that occurred over in Summit, located about five miles east of Fort Johnson. One night Thomas Price Smith was awakened by a noise in the sheep corral. He wore a long white night shirt and night cap to sleep in. On going out to find the cause of the disturbance he saw a number of Indians in the act of carrying off some of his sheep. In his big gruff voice he called out, "YOU Vagabonds." His loud voice and ghostly appearance gave the Indians such a fright that they dropped the sheep and scurried off through the field.13
Thomas Smith had also lived at fort Johnson before moving to Summit in 1859.Two of his children: Thomas Alma (1855) and Emanuel Driver (1858) were born there. An English convert, he was one of a group called The United Brethren, who were converted by Wilford Woodruff. He died in Parowan at the age of ninety.
Several hundred Indians actually lived in that area and were generally considered friendly although raids on the settlers livestock was an ongoing problem they had to contend with, and not just during the Walker and Black Hawk wars. One of them, who lived there, and helped manage the cattle herd, was an intelligent Piute by the name of Donooguivce, who taught two of Joel Hill's sons, Seth and Nephi, his native tongue, a skill those two brothers were called upon to use numerous times by exploring parties and other who dealt with the Indians and needed an interpreter. In May of 1856 one of those occasions arose which Nephi wrote about in his history.
"There was a party of Chief Walkers Band of Indians came by way of Cedar City & Parowan from across the Colorado where they had been trading with the Navajos. I saw them at Cedar and talked with them. At Cedar City they told me they were going to camp up at Parowan for 2 or three days. They went up there and camped on the south side of town by some little springs and the first night they were there about 20 head of their horses got into the fields and tramped down some of the grain. The men who owned the field got out early in the morning, gathered up the horses and put them in the big corral inside the fort and locked the gate so when the Indians came after them they could not get them for the white men were guarding them. The Indians went back to camp and got their guns and the white men still kept the horses and would not let them go until they paid for the damage.
The town was considerably alarmed, everyone got their guns. The Utes told Major John Steele to send a man to Cedar City and get Piute Amsaugauts, that was what the Utes called me, because I learned to talk their language from the Piutes. They said he was the only man in the country that would talk straight. I was away from home when the man Major Steele sent, arrived. He told my wife he had come after me to settle a difficulty between the Indians and the people of Parowan. He also told her that they were about to fight when he came away and thought they would be fighting before he could get back there. My wife got excited and held on to me and wept and said I would be killed. I tried to comfort her and assured her that I would soon be back again, but when I took my gun down to start, she commenced weeping again and held on to my gun when I sweetly assured her that I would return all right . At the earliest moment possible I started as soon as I could and went as fast as my horse could carry me.
The young man did not go back with me but stayed to rest his horse. The Indians met me some distance out from Parowan so I was able to talk the matter over with them and understood the matter very well. Before I got to Parowan the Indians offered the men where the crops were damaged a few buckskins but the men wanted more. I told the men they had better take what they offered and let them go instead of struggling with them and have a fight with them and getting somebody killed. The Indians brought the buckskin, got their horses and left for the north that evening. The Indians pleased me for coming in with a little least talk and getting the matter settled so easy. I returned home that evening having ridden 40 miles that day, and had my wife terribly frightened, all for nothing."14
As indicted, those at Fort Johnson, Parowan and other towns in Southern Utah were put on alert once again in the mid 1860s during the Black Hawk War. Although there are no recorded reports of raids on the cattle herds at Fort Johnson during that conflict, there were raids elsewhere. In July of 1867 a raid was made near Parowan by some fifty Indians who rounded up about 700 head of horses and cattle and attempted to drive them through the mountains to the east. A running gun battle ensued in Little Creek Canyon, east of Paragonah, between the local Militia and the Indians in which the stock were all recovered without the loss of any the pioneer's lives. After that, Joseph Fish said he, as well as others, kept their horses, saddles and arms where they could get them and be off on ten minutes warning, day and night.15
Marauding Indians were not the only enemy the settlers had to contend with. From time to time other unwanted intruders also made their appearance at both Fort Johnson and in the surrounding communities. Most people in Utah are familiar with the story of the crickets and seagulls in the Salt Lake Valley in 1848, but might not be aware that those in the southern region of the state also had to contend with a similar foe from time to time. An article in the Deseret News, dated June 27, 1855, reported,
"Hon. Calvin C. Pendleton, arrived in this city from Iron Co.; general [good] health among the people; the grasshoppers have destroyed all the grain at Paragonah, nine-tenths at Parowan; all the wheat at Fort Johnson, and about one-tenth of the grain at Cedar City; the grain at Harmony is uninjured… The fields look like a desert and every separate bench appears to be hatching out fresh crops of grasshoppers. Several companies have started to the 'Panguitch' lake, on fishing excursions. The water is lower than has ever been known before, and but a small portion of the land re-sown can possibly be watered. A small party has also started to the Santa Clara mission to plant corn. The people of Iron Co. are in good spirits. The Public Square at Parowan city (10 acres) had been planted with potatoes in the hopes that the united efforts of men, women, and children, chickens, ducks, turkeys, &, may save a sufficiency to have occasionally a little potato soup next winter."
And it was not just a one time event that insects invaded their fields at the fort for a second article appeared in the Deseret News, dated August 24, 1859 stating that, "Crops look, thus far, better than for years previous , excepting at Fort Johnson where the grasshoppers have almost entirely destroyed them.
In the latter part of 1860, Joel Hills returned to the fort, after being away for an extended period of time on church business, to find it abandoned and in disarray. The orchard and fences were broken down and scattered abroad, his house stripped of windows, locks, hinges, latches and etc. and very much mutilated and torn to pieces. And when he went to look after his stock he found them reduced to a small number and himself in debt, all of which had been brought about through the grasshoppers and worms which had destroyed the crops for two or three years in succession and which had forced his family, as well as others, to abandon the place. He soon concluded, however, that there was no other course for him to pursue but to go to work, repair his house and move his family back as soon as possible, so as to prepare for winter.16
Another critter they had to contend with was the jackrabbit, as illustrates in a news article in the Salt Lake Democrat, dated Nov. 27, 1885.
"The rabbits at Cedar City, Utah, have taken it upon themselves to play the part of Destroying Angels. About four miles north of Cedar City at what is known as Cedar Bottoms, they have destroyed all the vegetation, even eating the greasewood to the level of the ground. At Fort Johnson {about 45} miles south of Beaver, they destroyed fifteen tons of grain."
In the summer of 1889, yet one more plague descended upon the area as described by another able historian. "Throughout July the settlement of Johnson's Springs was assaulted by millions of sticky flies that crawled into food containers, pestered the citizens, and spread panic to those who remembered the plague these ugly creatures spread. And then came the grasshoppers- three miles of Cedar Bottoms covered in a thick green cloud of living pests, their long, grotesque legs catching in hair, their bulging eyes hauntingly turning dreams into nightmares. There seemed to be no way mere humans could defeat them. The children beat at them with rags and branches, the adults drove them into huge fires, but the horrible little "critters" hopped merrily away to enjoy another feast of carefully guarded crops and tender young vegetables."17
The soil was fertile however and when the pests did leave them alone, they raised good crops. In the spring of 1862, Seth Johnson said he planted a rather large amount of grain from which he was able to harvest five hundred bushels.
Because of the marshy nature of the soil around the fort, there was a limited amount of land suitable for farming and as a consequence, the number of families who resided there was relatively small. In time as other communities were settled and water and land developed, and in part, and also because of the problem of having to deal with grasshoppers and other pests, many of the men and their families moved away. As mentioned, Thomas Smith was one of them. Another family was that of William Dalley and his wife Mandana, converts from England, who had volunteered to help settle Cedar City before moving to Fort Johnson in 1853 to help build the fort and take care of the cattle herd. Two of their children were born there. He was a tailor by trade but was also a good farmer and gardener. In 1858 they also moved to the new town of Summit as did his brother James, where they lived the remainder of their days. Samuel Dennis and Mary Burton White was another family that was called to labor at the fort, but again, as difficulties with the Indians subsided and greener pastures appeared elsewhere, they also packed up their wagon and moved, making their new home up in Beaver.
The Dalley's friends, Laban and Parmelia Morrill, whom they had met while crossing the plains, left Fort Johnson some time before 1860 and moved back to the Payson- Springville area where they resided for a few years before returning once again to Southern Utah. They also lived in Summit for a few years before eventually settling down in Junction, located over in Piute County.
Another family that was part of the original group called to assist Joel Hills at the fort was that of James Willard and Lucinda Bay. James supported his family by farming and freighting, and two of their children were born while there, but in time they moved south to the small community of Bellview, which was located in the valley south of the black ridge, then went on to Virgin where they lived for18 years. Finally they ended up in Junction with the Morrill family.18
In 1858 Nephi Johnson was called by the Stake President to explore the Upper Valley of the Rio Virgin and look for possible new settlements. After returning with a report that 2 or 3 small settlements could be made, he was instructed to gather up a company and build a road into the area. After constructing a road, later referred to as the 'Johnson Twist', up over the Hurricane Cliffs, they laid out the town of Virgin after which he and his family took up residence there. In 1861 his father, Joel Hills Johnson, sold his interest in Johnson Fort and joined his son in the Virgin Valley. As part of his legacy, that historic little fort would always retain his name and many would think of him as they visited the place in years to come, but there is no record that Joel Hills, himself, ever returned to the area.19
Joel Hill's son Seth did not move to Virgin with the rest of that family until 1864, but continued farming, along with teaching school in the winter. Education was always a high priority for pioneer families of that era, which in many cases they made arrangements for, even before adequate housing for their families was provided. For those at the fort, it would be reasonable to believe that they gather in the large Johnson home for instruction in writing, literature and etc. Seth, himself, was the teacher for four of those years. One of his students was a young 14 year old girl by the name of Lydia Ann, daughter of Thomas Price Smith. Because of the hard life she had, Seth took compassion upon her, proposed marriage, and they were wed over in Parowan in November of 1861. That frail little girl went on to have 14 children.
In later years after the Armstrong's moved to what would be called the co-op ranch, school was again held in one of the ranch houses with Catherine G. Bell and Lucy Jones serving as school teachers during the years 1877-79.
Initially, Joel Hills served as presiding Elder at Fort Johnson. He was succeeded by Laban D. Morrell, who in turn was succeeded in the spring of 1857 by William Dalley who served until 1859 when the fort was essentially abandoned. Once when they saw some of the church authorities and their wives coming, some of Joel Hills wives didn't know what to do as they all had been eating onions, but Joel solved their problem, "just feed them all more onions," he said.20 When there was a larger group of families at the fort, they probably held their own church services, but at least part of the time, Janet Johnson said they would go to church in Cedar City in a wagon drawn by oxen. She would take along a big basket of eggs and crock of fresh butter to the bishop for tithing.
As each of the original builders and owners of the Fort departed, they sold their interest to others including Joseph Jones and Joseph H. Armstrong. Susannah Dalley Armstrong, who married joseph smith Armstrong, oldest son of Joseph H. and Mary Ann Armstrong, said Joseph H. built a house in 1883 near where the south bastion of the fort had been located. Using salvaged bricks from the bastion, he constructed a spacious house with seven rooms, two porches and an attic. It also contained three fireplaces: one in the parlor, one in the dining room and one in a bedroom. It appeared to also have served as a hotel, as they would feed passengers traveling between the mines at Silver Reef and the Milford area.21 During the period of 1880-81 it also served as a stop for the mail run between Beaver and Cedar City. Along with a convenient place to change drivers and horses, board and room was also provided for any passengers and the drivers.
Even in the later years when hostilities had subsided, the Indians continued to be a challenge. Mary Ann Armstrong said that the large group of Piute Indians that lived in the vicinity of the fort claimed that the meadows and pastures belonged to them. It was the place they would gather each spring for their pow-wows, and where they would hold their dances and rituals, chanting songs to the Great Spirit for rain and other things they desired. They would collect great piles of brush during the day, and as soon as it was dark they would make a big circle of fire. In the center of the circle they would form a circle, locking arms with a buck, them a squaw all around. They would dance in a shuffling gait, chanting their songs for hours. The Indians were allowed to turn their horses into the pastures while they were camped there and the Armstrong's found themselves pestered constantly by their begging for flour, bacon, bread, sugar and many other things. As Mary Ann had learned their language, the squaws would come to her with their troubles and talk for hours.22
John Pidding Jones and his family moved to the fort from Cedar City around 1869. They lived there for about a year before purchasing some property one half mile to the east, which had belonged to Joel Hills Johnson and Laban Morrell. He did some farming but also constructed a blast furnace and coke oven to produce fire grates, cog wheels, skillets, flat irons and other iron products. He even produced rollers for the molasses mills in Dixie. John, his sons and their families, along with extended family members, became the nucleus of what would be known as the town of Enoch. In 1890 when an application was made to establish a post office, they wanted to give it the official name of Johnson Springs, but were informed by the government that another town in Utah already carried the title, and thus it was proposed that it be called Enoch, by which it is known today.23
Other families who lived at the fort for short periods of time included those of Roswell Clark and William O. Orton. In 1878, six families were living there with John P. Jones serving as presiding Elder. According to Andrew Jensen, Assistant Church Historian, there were 9 families, comprising 66 souls residing there in August of 1892.
With time and one rain storm after another, the adobe bricks of the old fort began to crumble and return to mother earth from whence they came. Part of the walls or at least one wall was still standing in 1886, however, for in his history John Pidding Jones recorded that in an attempt to allude the U.S. marshals who were after him because he had more than one wife, he would sometimes take his 8 year old son, Orson Pratt and they would sleep down by the fort wall. Little remains today to remind a visitor that a historic fortification or community existed there at one time, with the exception of two weather beaten and crumbling old log cabins and the remains of those magnificent and ancient cottonwood trees. In years past many a celebration was held at the fort on the grass under the shade of those old cottonwood trees. People came from all the surrounding settlements of Cedar City, Parowan, Summit and Hamilton's Fort to spend the day and celebrate the fourth and twenty-fourth of July. Big swings in the highest trees and sports of all kinds helped to make the fun, along with picnics.24 Huge gray and weathered logs, testifying to the size of those stately old cottonwoods, can still be found lying around the perimeter of the property where the current owners live.
That small plot of land now stands, almost like an oasis, in an area north of the city of Enoch proper. There is still a sense of pride in that historic fort among those who live in the area, including the Grimshaw family who have a lovely home on what was essentially the site of the fort, as well as those who had ancestors that once slept within its walls of that fortification, and yes, it still is a special place to go to celebrate holidays, where, through music and stories, the spirit, memory and traditions of Fort Johnson are kept alive.
That fort, or at least Johnson Springs, made one more lasting contribution, not only to Enoch and the people of Iron County, but to the State of Utah and beyond, the effects of which continues to this day. In 1898 the people of Cedar City completed the first building for the Branch Normal School, which eventually would become Southern Utah University, but no funds were left to install a heating plant. The state, however, would not accept it, or approve it as an institution of higher learning without the heating facilities. The people had donated to put up that building and were hard pressed, but the stock holders of the Cedar City Co-op who owned the ranch at Johnson Springs, came to the rescue. They sold the ranch and put up the money to complete the building. And so it was that the spirit of that old Fort had survived and the battle had been won. Joel Hills and the rest of those who had made Fort Johnson a home would have been pleased with that.
Joel Hills Johnson's Journal, Family Search, Memories.
Ibid
Deseret Evening News, July 21, 1894; Utah Digital Newspapers
Ibid, Fort Johnson, April 17, 1897.
History of Janet Fife Johnson, Family Search, Memories.
At The Indians Mercy, Deseret Weekly News, September 19, 1896.
Ibid; Elder Franklin D. Richards.
Joel Hills Johnson's Journal; Family Search.
Johnson's Fort by Susannah Dalley Armstrong; Family Search; Joel Hills Johnson History.
Utah Territorial Military Records, 1849-1877; Family Search, Image #7.
Ibid; Image #27.
Indian Reminiscences, Deseret Weekly News, May 23, 1896, Utah Digital Newspapers.
Thomas Price Smith; Stories of Persecution and Pioneer Stories by Amelia D. Green; Family Search, James Dalley file.
Nephi Johnson, Biographical Sketch, hand written, Church History Catalog, P. 8-9. Salt Lake City.
The Life and Times of Joseph Fish Mormon Pioneer, p. 115-17, Google Search.
Diary of Joel Hills Johnson, Washington Co. Library, Special Collections.
History of Sylvester Frazer Jones and Susannah Merric, by Agnes J. Hunt and Paul L. Hunt, p.61.
James Willard Bay History, Family Search.
Nephi Johnson Autobiographical Sketch, Church History Catalog, MS 23835, Salt Lake City.
Johnsons Fort; Special Collections, Gerald R. Sherratt Library; Southern Utah University.
Johnson Fort by Susannah Dalley Armstrong; Family Search.
History of Mary Ann Smith Armstrong, Family Search, Memories.
The Jones family; John Pidding Jones; Family Search.
Johnson's Fort (Enoch), Bell Armstrong and Estella Jones Grimshaw; Special Collections, Gerald R. Sherratt Library, Southern Utah University, MS 13, B2, F16.
Kaysville: Davis County's Beloved Fourth Settlement
History Minute: Fort Douglas
Tagged: fort johnson ut, george a smith, high on the mountain top, joel hills johnson, johnson springs


The park boasts a 700 m tall granite monolith, a world renowned big-wall climbing mecca and bouldering destination. Or take a day hike to one of the three main peaks, 2 to 5 hours on steep terrain rewards you with sweeping views of Howe Sound and the surrounding mountains, as well as a great place to watch for climbers, kite boarders and the rare Peregrine falcons.
Campground with 75 sites (per person registration), 18 sites are drive in, small vehicle only, 57 walk in tent pads;drinking water, pit toilets, day use area. No campfires in this park.


text:
\section{Introduction}\label{sec:introduction}

Collective motion of animal groups such as flocks of birds is an awe-inspiring natural phenomenon that has profound implications for the field of aerial swarm robotics \cite{floreano_science_2015,zufferey_aerial_2011}.
Animal groups in nature operate in a completely self-organized manner since the interactions between them are purely local and decisions are made by the animals themselves.
By taking inspiration from decentralization in biological systems, we can develop powerful robotic swarms that are 1) robust to failure, and 2) highly scalable since the number of agents can be increased or decreased depending on the workload.

One of the most appealing characteristics of collective animal behavior for robotics is that decisions are made based on local information such as visual perception.
As of today, however, most multi-agent robotic systems rely on entirely centralized control \cite{mellinger_minimum_2011,kushleyev_towards_2013,preiss_crazyswarm_2017,weinstein_visual_2018} or wireless communication of positions \cite{vasarhelyi_outdoor_2014,viragh_flocking_2014,vasarhelyi_optimized_2018}, either from a motion capture system or global navigation satellite system (GNSS).
The main drawback of these approaches is the introduction of a single point of failure, as well as the use of unreliable data links, respectively.
Relying on centralized control bears a significant risk since the agents lack the autonomy to make their own decisions in failure cases such as a communication outage.
The possibility of failure is even higher in dense urban environments, where GNSS measurements are often unreliable and imprecise.

\begin{figure}[t!]
    \centering
    \includegraphics[width=\columnwidth]{overview}
    \caption{%
        Vision-based flock of nine drones during migration.
        Our visual swarm controller operates fully decentralized and provides collision-free, coherent collective motion without the need to share positions among agents.
        The behavior of an agent depends only on the omnidirectional visual inputs (colored rectangle, see Fig.~\ref{fig:camera-config} for details) and the migration point (blue circle and arrows).
        Collision avoidance (red arrows) and coherence (green arrows) between flock members are learned entirely from visual inputs.
    }\label{fig:overview}
\end{figure}

Vision is arguably the most promising sensory modality to achieve a maximum level of autonomy for robotic systems, particularly considering the recent advances in computer vision and deep learning \cite{krizhevsky_imagenet_2012,lecun_deep_2015,he_deep_2016}.
Apart from being light-weight and having relatively low power consumption, even cheap commodity cameras provide an unparalleled information density with respect to sensors of similar cost.
Their characteristics are specifically desirable for the deployment of an aerial multi-robot system.
The difficulty when using cameras for robot control is the interpretation of the visual information which is a hard problem that this paper addresses directly.

In this work, we propose a reactive control strategy based \textit{only} on local visual information.
We formulate the swarm interactions as a regression problem in which we predict control commands as a nonlinear function of the visual input of a single agent.
To the best of our knowledge, this is the first successful attempt to learn vision-based swarm behaviors such as collision-free navigation in an end-to-end manner directly from raw images.

\begin{figure*}[t]
    \centering
    \begin{subfigure}[b]{0.31\textwidth}
        \centering
        \includegraphics[width=\textwidth]{method/dataset-generation}
        \caption{Dataset generation}\label{fig:dataset-generation}
    \end{subfigure}
    \hfill
    \begin{subfigure}[b]{0.31\textwidth}
        \centering
        \includegraphics[width=\textwidth]{method/training-phase}
        \caption{Training phase}\label{fig:training-phase}
    \end{subfigure}
    \hfill
    \begin{subfigure}[b]{0.31\textwidth}
        \centering
        \includegraphics[width=\textwidth]{method/visual-flocking}
        \caption{Vision-based control}\label{fig:visual-flocking}
    \end{subfigure}
    \caption{%
        Overview of our method.
        (\ref{fig:dataset-generation}) The dataset is generated using a simple flocking algorithm (see Sec.~\ref{sec:flocking-algorithm}) deployed on simulated quadrotor drones (see Sec.~\ref{sec:drone-model}).
        The agents are initialized in random non-overlapping positions and assigned random linear trajectories while computing velocity targets and acquiring images from all mounted cameras (see Sec.~\ref{sec:dataset-generation}).
        (\ref{fig:training-phase}) During the training phase (see Sec.~\ref{sec:training-phase}), we employ a convolutional neural network to learn the mapping between visual inputs and control outputs by minimizing the error between predicted and target velocity commands.
        (\ref{fig:visual-flocking}) During the last phase (see Sec.~\ref{sec:vision-based-control}), we use the trained network as a reactive controller for emulating vision-based swarm behavior.
        The behavior of each agent is entirely vision-based and only depends on its visual input at a specific time step.
        Initial flight experiments have confirmed that by using PX4 and ROS, we can use the exact same software infrastructure for both simulation and reality.
        }\label{fig:flowchart}
\end{figure*}

\section{Related Work}\label{sec:related-work}

We classify the related work into three main categories.
Sec.~\ref{sec:decentralized-flocking} considers literature in which a flock of drones is controlled in a fully decentralized manner.
Sec.~\ref{sec:vision-based} is comprised of recent data-driven advances in vision-based drone control.
Finally, Sec.~\ref{sec:vision-flocking} combines ideas from the previous sections into approaches that are both vision-based and decentralized.

\subsection{Decentralized flocking with drones}\label{sec:decentralized-flocking}

Flocks of autonomous drones such as quadrotors and fixed-wings are the focus of recent research in swarm robotics.
Early work presents ten fixed-wing drones deployed in an outdoor environment \cite{hauert_reynolds_2011}.
Their collective motion is based on Reynolds flocking \cite{reynolds_flocks_1987} with a migration term that allows the flock to navigate towards the desired goal.
Arising from the use of a nonholonomic platform, the authors study the interplay of the communication range and the maximum turning rate of the agents.

Thus far, the largest decentralized quadrotor flock consisted of 30 autonomous agents flying in an outdoor environment \cite{vasarhelyi_optimized_2018}.
The underlying algorithm has many free parameters which require the use of optimization methods.
To this end, the authors employ an evolutionary algorithm to find the best flocking parameters according to a fitness function that relies on several order parameters.
The swarm can operate in pre-defined confined space by incorporating repulsive virtual agents.

The commonality of all mentioned approaches and others, for example, \cite{vasarhelyi_outdoor_2014,dousse_human-comfortable_2017}, is the ability to share GNSS positions wirelessly among flock members.
However, there are many situations in which wireless communication is unreliable or GNSS positions are too imprecise.
We may not be able to tolerate position imprecisions in situations where the environment requires a small inter-agent distance, for example when traversing narrow passages in urban environments.
In these situations, tall buildings may deflect the signal and communication outages occur due to the wireless bands being over-utilized.

\subsection{Vision-based single drone control}\label{sec:vision-based}

Vision-based control of a single flying robot is facilitated by several recent advances in the field of machine learning.
In particular, the controllers are based on three types of learning methods: imitation learning, supervised learning, and reinforcement learning.

Imitation learning is used in \cite{ross_learning_2013} to control a drone in a forest environment based on human pilot demonstrations.
The authors motivate the importance of following suboptimal control policies in order to cover more of the state space.
The reactive controller can avoid trees by adapting the heading of the drone; the limiting factor is ultimately the field of view of a single front-facing camera.

A supervised learning approach \cite{loquercio_dronet_2018} features a convolutional network that is used to predict a steering angle and a collision probability for drone navigation in urban environments.
Steering angle prediction is formulated as a regression problem by minimizing the mean-squared-error between predicted and ground truth annotated examples from a dataset geared for autonomous driving research.
The probability of collision is learned by minimizing the binary cross-entropy of labeled images collected while riding a bicycle through urban environments.
The drone is controlled directly by the steering angle, whereas its forward velocity is modulated by the collision probability.

An approach based on reinforcement learning \cite{sadeghi_cad2rl_2017} shows that a neural network trained entirely in a simulated environment can generalize to flights in the real world.
In contrast with the previous methods based only on supervised learning, the authors additionally employ a reinforcement learning approach to derive a robust control policy from simulated data.
A 3D modeling suite is used to render various hallway configurations with randomized visual conditions.
Surprisingly, the control policy trained entirely in the simulated environment is able to navigate real-world corridors and rarely leads to collisions.

The work described above and other similar methods, for instance, \cite{giusti_machine_2016,gandhi_learning_2017,smolyanskiy_toward_2017}, use a data-driven approach to control a flying robot in real-world environments.
A shortcoming of these methods is that the learned controllers operate only in two-dimensional space which bears similar characteristics to navigation with ground robots.
Moreover, the approaches do not show the ability of the controllers to coordinate a multi-agent system.

\subsection{Vision-based multi-drone control}\label{sec:vision-flocking}

The control of multiple agents based on visual inputs is achieved with relative localization techniques \cite{saska_system_2017} for a group of three quadrotors.
Each agent is equipped with a camera and a circular marker that enables the detection of other agents and the estimation of relative distance.
The system relies only on local information obtained from the onboard cameras in near real-time.

Thus far, decentralized vision-based drone control has been realized by mounting visual markers on the drones \cite{saska_autonomous_2014,faigl_low-cost_2013,krajnik_practical_2014}.
Although this simplifies the relative localization problem significantly, the marker-based approach would not be desirable for real-world deployment of flying robots.
The used visual markers are relatively large and bulky which unnecessarily adds weight and drag to the platform; this is especially detrimental in real-world conditions.

\section{Method}\label{sec:method}

At the core of our method lies the prediction of a velocity command for each agent that matches the velocity command computed by a flocking algorithm as closely as possible.
For the remainder of the section, we consider the velocity command from the flocking algorithm as the target for a supervised learning problem.
The fundamental idea is to eliminate the dependence on the knowledge of the positions of other other agents by processing only local visual information.
Fig.~\ref{fig:flowchart} provides an overview of our method.

\subsection{Flocking algorithm}\label{sec:flocking-algorithm}

We use an adaptation of Reynolds flocking \cite{reynolds_flocks_1987} to generate targets for our learning algorithm.
In particular, we only consider the \tit{collision avoidance} and \tit{flock centering} terms from the original formulation since they only depend on relative positions.
We omit the \tit{velocity matching} term since estimating the velocities of other agents is an extremely difficult task given only a single snapshot in time (see Sec.~\ref{sec:dataset-generation}).
One would have to rely on estimating the orientation and heading with relatively high precision in order to infer velocities from a single image.

In our formulation of the flocking algorithm, we use the terms \tit{separation} and \tit{cohesion} to denote collision avoidance and flock centering, respectively \cite{saska_swarms_2014}.
We further add an optional \tit{migration} term that enables the agents to navigate towards a goal.

The first consideration when modeling the desired behavior of the flock is the notion of neighbor selection.
It is reasonable to assume that each agent can only perceive its neighbors in a limited range.
We therefore only consider agents as neighbors if they are closer than the desired cutoff distance $r^\txt{max}$ which corresponds to only selecting agents in a sphere with a given radius.
Therefore, we denote the set of neighbors of an agent $i$ as the set

\begin{equation}\label{eq:neighbor-set}
    \set{N}_i = \left\{ \txt{agents}~j : j \neq i \wedge \norm{\Vector{r}_{ij}} < r^\txt{max} \right\}
\end{equation}
where $\Vector{r}_{ij} \in \mbb{R}^3$ denotes the relative position of agent $j$ with respect to agent $i$ and $\norm{\cdot}$ the Euclidean norm.
We compute $\Vector{r}_{ij} = \norm{\Vector{p}_j - \Vector{p}_i}$ where $\Vector{p}_i \in \mbb{R}^3$ denotes the absolute position of agent $i$.

The separation term steers an agent away from its neighbors in order to avoid collisions.
The separation velocity command for the $i$th agent can thus be formalized as

\begin{equation}\label{eq:separation}
    \Vector{v}_i^\txt{sep} = -\frac{k^\txt{sep}}{|\set{N}_i|} \sum_{j \in \set{N}_i} \frac{\Vector{r}_{ij}}{\norm{\Vector{r}_{ij}}^2}
\end{equation}
where $k^\txt{sep}$ is the separation gain which modulates the strength of the separation between agents.

The cohesion term can be seen as the antagonistic inverse of the separation term since its purpose is to steer an agent towards its neighbors to provide cohesiveness to the group.
The cohesion velocity command for the $i$th agent can be written as

\begin{equation}\label{eq:cohesion}
    \Vector{v}_i^\txt{coh} = \frac{k^\txt{coh}}{|\set{N}_i|} \sum_{j \in \set{N}_i} \Vector{r}_{ij}
\end{equation}
where $k^\text{coh}$ is called the cohesion gain and modulates the tendency for the agents to be drawn towards the center of the neighboring agents.

For our implementation, the separation and cohesion terms are sufficient to generate a collision-free flock in which agents remain together, given that the separation and cohesion gains are chosen carefully.
We denote the combination of the two terms as the Reynolds velocity command $\Vector{v}_i^\txt{rey} = \Vector{v}_i^\txt{sep} + \Vector{v}_i^\txt{coh}$ which is later predicted by the neural network.

Moreover, the addition of the migration term provides the possibility to give a uniform navigation goal to all agents.
The corresponding migration velocity command is given by

\begin{equation}
    \Vector{v}_i^\txt{mig} = k^\txt{mig} \frac{\Vector{r}_i^\txt{mig}}{\norm{\Vector{r}_i^\txt{mig}}}
\end{equation}
where $k^\text{mig}$ denotes the migration gain and $\Vector{r}_i^\txt{mig} \in \mbb{R}^3$ denotes the relative position of the migration point with respect to agent $i$.
We compute $\Vector{r}_i^\txt{mig} = \Vector{p}^\txt{mig} - \Vector{p}_{i}$ where $\Vector{p}^\txt{mig} \in \mbb{R}^3$ is the absolute position of the migration point.

The velocity command for an agent $i$ is computed as a sum of the Reynolds terms, which is a combination of separation and cohesion, as well as the migration term, as $\tilde{\Vector{v}}_i = \Vector{v}_i^\txt{rey} + \Vector{v}_i^\txt{mig}$.
In general, we assume a homogeneous flock, which means that all agents are given the same gains for separation, cohesion, and migration.

A final parameter to adjust the behavior of the flock is the cutoff of the maximum speed.
The final velocity command that steers an agent is given by

\begin{equation}\label{eq:final}
    \Vector{v}_i = \frac{\tilde{\Vector{v}}_i}{\norm{\tilde{\Vector{v}}_i}} \min \left( \norm{\tilde{\Vector{v}}_i}, v^\txt{max}\right)
\end{equation}

where $v^{\txt{max}}$ denotes the desired maximum speed of an agent during flocking.

\subsection{Drone model}\label{sec:drone-model}

\begin{figure}[t]
\centering
\begin{subfigure}[b]{\columnwidth}
    \centering
    \includegraphics[width=0.96\textwidth]{drone-model/cubemap}
    \caption{Visual input of an agent: concatenation of six camera images}\label{fig:field-of-view}
\end{subfigure}
\par\bigskip
\begin{subfigure}[b]{0.23\columnwidth}
    \centering
    \includegraphics[width=\textwidth]{drone-model/top-ortho}
    \caption{Top view}\label{fig:top-ortho}
\end{subfigure}
\quad
\begin{subfigure}[b]{0.23\columnwidth}
    \centering
    \includegraphics[width=\textwidth]{drone-model/side-ortho}
    \caption{Side view}\label{fig:side-ortho}
\end{subfigure}
\quad
\begin{subfigure}[b]{0.42\columnwidth}
    \centering
    \includegraphics[width=\textwidth]{drone-model/drone-hardware}
    \caption{Drone hardware}\label{fig:drone-hardware}
\end{subfigure}
\caption{%
    Camera configuration and resulting visual input for a simulated agent.
    The cameras are positioned such that the visual field of an agent corresponds to a cube map, i.e. each camera is pointing at a different face of a cube as seen from within the cube itself.
    (\ref{fig:field-of-view}) The concatenation of the six camera images into the complete visual field.
    (\ref{fig:top-ortho}) and (\ref{fig:side-ortho}) Orthographic projection of the camera configuration as seen from the top and side, respectively.
    The schematic does not correspond to the exact geometry used in the simulation (see text for details).
    The color shades of the individual camera images (\ref{fig:field-of-view}) correspond to the field of view in the top (\ref{fig:top-ortho}) and side (\ref{fig:side-ortho}) orthographic projections.
    (\ref{fig:drone-hardware}) Example hardware implementation of the drone used in simulation based on \cite{dousse_human-comfortable_2017}.
    It uses six OpenMV Cam M7 for image acquisition, an Odroid XU4 for image processing, and a Pixracer autopilot for state estimation and control.
    }\label{fig:camera-config}
\end{figure}

Simulations were performed in Gazebo with a group of nine quadrotor drones.
Each drone is equipped with six simulated cameras which provide omnidirectional vision.
The cameras are positioned $15$\si{\cm} away from the center of gravity of the drone in order to have an unobstructed view of the surrounding environment, including the propellers (see Fig.~\ref{fig:field-of-view}).

Each camera has a $100^\circ$ horizontal and vertical field of view and takes a grayscale image of $64 \times 64$ pixels with a refresh rate of $10$ Hz.
We concatenate the images from all six cameras to a $64 \times 384 $ pixels grayscale image.

We use the PX4 autopilot \cite{meier_px4_2015} and provide it with velocity commands as raw setpoints via a ROS node. The heading of the drone is always aligned with the given velocity command.

\subsection{Dataset generation}\label{sec:dataset-generation}

We generate the dataset used for training the regressor entirely in a physics-based simulation environment.
Since our objective is to cover the maximum possible state and command space encountered during flocking, we generate our dataset with random trajectories as opposed to trajectories generated by the flocking algorithm itself.
In other words, we acquire an image $\Vector{x}_i$ and compute a ground truth velocity command $\target{\Vector{v}}_i$ from our flocking algorithm while following a random linear trajectory.
This was explicitly done to ensure that the dataset contains agent states which the flocking algorithm would not generate in order to improve robustness to unseen agent configurations.
Note that initial experiments with agents following trajectories generated by the flocking algorithm resulted in collisions between agents.
This observation is in line with the finding in \cite{ross_learning_2013} that situations not encountered during the training phase cannot possibly be learned by the controller.
A sample of the dataset is thus a tuple $\left( \Vector{x}_i, \target{\Vector{v}}_i \right)$ that is acquired for each agent $i$ every $10^{-1}$\si{\second}.
The dataset is generated in multiple \tit{runs}, each of which contains images and ground truth velocity commands generated by following a randomized linear trajectory as described below.
We generate $500$\si{\kilo} samples for training, $60$\si{\kilo} samples for validation, and $60$\si{\kilo} samples for testing.

The agents are spawned at random non-overlapping positions around the origin in a cube with side length of $4$\si{\meter} and a minimum distance to any other agent $1.5$\si{\meter}.
The side length and minimum distance were chosen to resemble a real-world deployment scenario of a drone swarm in a confined environment such as a narrow passage between adjacent buildings.
Each agent $i$ is then assigned a linear trajectory by following a velocity command which is drawn uniformly inside a unit cone with an angle of $15^\circ$.
The mean velocity command is thus facing directly in the direction of the migration point as seen from the origin.
The velocity command is distinct for each agent and kept constant during the entire run.
The random velocity commands were chosen such that collisions and dispersions are encouraged.

A run is considered complete as soon as a) the migration point is reached by at least one agent, or b) any pair of agents become either too close or c) too dispersed, all while following their linear trajectory.
We consider the migration point reached as soon as an agent comes within a radius $1$\si{\meter} of the migration point.
We consider the agents too close if any pair of drones falls below a collision threshold of $1$\si{\meter}.
Similarly, we regard agents as too dispersed when the distance between any two drones exceeds the dispersion threshold of $7$\si{\meter}.
The collision threshold follows the constraints of the drone model used in simulation and the dispersion threshold stems from the diminishing size of other agents in the field of view.

\subsection{Training phase}\label{sec:training-phase}

We formulate the imitation of the flocking algorithm as a regression problem which takes an image (see Fig.~\ref{fig:field-of-view}) as an input and predicts a velocity command which matches the ground truth velocity command as closely as possible.
To produce the desired velocities, we consider a state-of-the-art convolutional neural network \cite{loquercio_dronet_2018} that is used for drone navigation.
The model is composed of several convolutional layers with ReLU activations \cite{he_delving_2015} and finally a fully connected layer with dropout \cite{srivastava_dropout_2014} to avoid overfitting.
Unlike \cite{loquercio_dronet_2018} we opt for a single-head regression architecture to avoid convergence problems caused by different gradient magnitudes from an additional classification objective during training.
This simplifies the optimization problem and the model architecture and thus the resulting controller.

We use mini-batch stochastic gradient descent (SGD) to minimize the regularized mean squared error (MSE) loss between velocity predictions and targets as

\begin{equation}\label{eq:loss}
    \mcl{L}^\txt{MSE} = \frac{1}{\mcl{B}} \sum_{i = 1}^\mcl{B} (\target{\Vector{v}}_i - \pred{\Vector{v}}_i)^2 + \frac{\lambda}{2} \norm{\Vector{w}}^2
\end{equation}
where $\target{\Vector{v}}_i$ is the target velocity and $\pred{\Vector{v}}_i$ the predicted velocity of the $i$th agent.
We denote the mini-batch size by $\mcl{B}$, the weight decay factor by $\lambda$, and the neural network weights -- excluding the biases -- by $\Vector{w}$.
We employ variance-preserving parameter initialization by drawing the initial weights from a truncated normal distribution according to \cite{he_delving_2015}.
The biases of the model are initialized to zero.

The objective function is minimized using SGD with momentum $\mu = 0.9$ \cite{sutskever_importance_2013} and an initial learning rate of $\eta = 5 \cdot 10^{-3}$ which is decayed by a factor of $k = 0.5$ after $10$ consecutive epochs without improvement on the hold-out validation set.
We train the network using a mini-batch size $\mcl{B} = 128$, a weight decay factor $\lambda = 5 \cdot 10^{-4}$, and a dropout probability of $p = 0.5$.
We stop the training process as soon as the validation loss plateaus for more than ten consecutive epochs.

The raw images and velocity targets are pre-processed as follows.
Before feeding the images into the neural network, we employ global feature standardization such that the entire dataset has a mean of zero and a standard deviation of one.
For the velocity targets from the flocking algorithm, we perform a frame transformation from the world frame $\mcl{W}$ into the drone's body frame $\mcl{B}$ as $\target{\Vector{v}_i} = {\mbf{R}^\mcl{B}_\mcl{W}}_i \Vector{v}^\txt{rey}_i$ where ${\mbf{R}_\mcl{W}^\mcl{B}}_i \in \SO{3}$ denotes the rotation matrix from world to body frame for robot $i$ and $\Vector{v}^\txt{rey}_i$ corresponds to the target velocity command.
We perform the inverse rotation to transform the predicted velocity commands from the neural network back into the world frame.

\subsection{Vision-based control}\label{sec:vision-based-control}

Once the convolutional network is finished training, we can use its predictions to modulate the velocity of the drone.
The same pre-processing steps apply to the vision-based control scenario, namely the standardization of raw images and the frame transformation of velocity predictions.
Although it is not mandatory for the implementation of this algorithm, one can optionally use a low-pass filter to smooth the velocity predictions, and as a result the trajectories of the agents.

\section{Results}\label{sec:results}

\newcommand{0.04\textwidth}{0.04\textwidth}
\newcommand{0.45\textwidth}{0.45\textwidth}

\begin{figure*}[t]
    \begin{subfigure}{0.04\textwidth}
        \caption{}\label{fig:common-position}
    \end{subfigure}
    \begin{subfigure}{0.45\textwidth}
        \includegraphics[width=\textwidth]{results/migration/common/position}
    \end{subfigure}
    \hfill
    \begin{subfigure}{0.45\textwidth}
        \includegraphics[width=\textwidth]{results/migration/opposing/position}
    \end{subfigure}
    \begin{subfigure}{0.04\textwidth}
        \caption{}\label{fig:opposing-position}
    \end{subfigure}
    \\
    \begin{subfigure}{0.04\textwidth}
        \caption{}\label{fig:common-vision}
    \end{subfigure}
    \begin{subfigure}{0.45\textwidth}
        \includegraphics[width=\textwidth]{results/migration/common/vision}
    \end{subfigure}
    \hfill
    \begin{subfigure}{0.45\textwidth}
        \includegraphics[width=\textwidth]{results/migration/opposing/vision}
    \end{subfigure}
    \begin{subfigure}{0.04\textwidth}
        \caption{}\label{fig:opposing-vision}
    \end{subfigure}
    \\
    \begin{subfigure}{0.04\textwidth}
        \caption{}\label{fig:common-distances}
    \end{subfigure}
    \begin{subfigure}{0.45\textwidth}
        \includegraphics[width=\textwidth]{results/migration/common/distances}
    \end{subfigure}
    \hfill
    \begin{subfigure}{0.45\textwidth}
        \includegraphics[width=\textwidth]{results/migration/opposing/distances}
    \end{subfigure}
    \begin{subfigure}{0.04\textwidth}
        \caption{}\label{fig:opposing-distances}
    \end{subfigure}
    \\
    \begin{subfigure}{0.04\textwidth}
        \caption{}\label{fig:common-order}
    \end{subfigure}
    \begin{subfigure}{0.45\textwidth}
        \includegraphics[width=\textwidth]{results/migration/common/order}
    \end{subfigure}
    \hfill
    \begin{subfigure}{0.45\textwidth}
        \includegraphics[width=\textwidth]{results/migration/opposing/order}
    \end{subfigure}
    \begin{subfigure}{0.04\textwidth}
        \caption{}\label{fig:opposing-order}
    \end{subfigure}
    \caption{%
        Flocking with common migration goal (left column) and opposing migration goals (right column).
        \textbf{First two rows}: Top view of a swarm migrating using the \tit{position-based} (\ref{fig:common-position} and \ref{fig:opposing-position}) and \tit{vision-based} (\ref{fig:common-vision} and \ref{fig:opposing-vision}) controller.
        The trajectory of each agent is shown in a different color.
        The colored squares, triangles, and circles show the agent configuration during the first, middle, and last time step, respectively.
        The gray square and gray circle denote the spawn area and the migration point, respectively.
        For the flock with opposing migration goal (\ref{fig:opposing-position} and \ref{fig:opposing-vision}), the waypoint on the right is given to a subset of five agents (solid lines), whereas the waypoint on the left is given to a subset of four agents (dotted lines).
        \textbf{Third row}: Inter-agent minimum and maximum distances from (\ref{eq:min-max-distance}) over time (\ref{fig:common-distances} and \ref{fig:opposing-distances}) while using the \tit{position-based} and \tit{vision-based} controller.
        The mean minimum distance between any pair of agents is denoted by a solid line, whereas mean maximum distances are shown as a dashed line.
        The colored shaded regions show the minimum and maximum distance between any pair of agents.
        \textbf{Fourth row}: Order parameter from (\ref{eq:order-parameter}) over time (\ref{fig:common-order} and \ref{fig:opposing-order}) during flocking.
        Note that the order parameter for the \tit{position-based} flock does not continue until the last time step since the \tit{vision-based} flock takes longer to reach the migration point.
    }\label{fig:migration-trajectories}
\end{figure*}
This section presents an evaluation of the learned vision-based swarm controller as a comparison to the behavior of the position-based flocking algorithm.
The results show that the proposed controller represents a robust alternative to communication-based systems in which the positions of other agents are shared with other members of the group.
We refer to the swarm operating on only visual inputs as \tit{vision-based} and the swarm operating on shared positions as \tit{position-based}.

The experiments are performed using the Gazebo simulator \cite{koenig_design_2004} in combination with the PX4 autopilot \cite{meier_px4_2015} for state estimation and control.
We employ the same set of flocking parameters used during the training phase throughout the following experiments (see Tab.~\ref{tab:flocking-params}).
Unless otherwise stated in the text, those parameters remain constant for the remainder of the experimental analysis.
The neural network is implemented in PyTorch \cite{paszke_automatic_2017}.
All simulations use the same random seed for repeatability.

\begin{table}
    \centering
    \caption{Parameters used for the flocking algorithm.}\label{tab:flocking-params}
    \begin{tabular}{@{}lllr@{}} \toprule
        Parameter       & Unit             & Description                & Value \\ \midrule
        $N$             & -                & Number of agents           & $9$   \\
        $r^\txt{max}$   & \si{\m}          & Maximum perception radius  & $20.0$\\
        $v^\txt{max}$   & \si{\m\per\s}    & Maximum flock speed        & $2.0$ \\
        $k^\txt{sep}$   & \si{\m\per\s}    & Separation gain            & $7.0$ \\
        $k^\txt{coh}$   & \si{\m\per\s}    & Cohesion gain              & $1.0$ \\
        $k^\txt{mig}$   & \si{\m\per\s}    & Migration gain             & $1.0$ \\ \bottomrule
    \end{tabular}
\end{table}

\subsection{Flocking metrics}\label{sec:flocking-metrics}

We report our results in terms of three flocking metrics that describe the state of the swarm at a given time step.
The  measures are best described as \tit{distance} and \tit{alignment}-based.

The two most important metrics are the minimum and maximum inter-agent \tit{distance} within the entire flock

\begin{equation}\label{eq:min-max-distance}
    d^\txt{min} = \min_{\substack{i, j \in \set{A} \\ i < j}} \norm{\Vector{r}_{ij}} \quad \txt{and} \quad
    d^\txt{max} = \max_{\substack{i, j \in \set{A} \\ i < j}} \norm{\Vector{r}_{ij}}
\end{equation}
where we let $\set{A}$ denote the set of all agents, and we have $i < j$ because of symmetry.
The minimum and maximum inter-agent distance are direct indicators for successful collision avoidance, as well as general segregation of the flock, respectively.
For instance, a collision occurs if the distance between any pair of agents falls below a threshold.
Similarly, we consider the flock too dispersed if the pairwise distance becomes too large.

We also measure the overall \tit{alignment} of the flock using an order parameter based on the cosine similarity

\begin{equation}\label{eq:order-parameter}
    \omega^\txt{ord} = \frac{1}{|\set{A}| (|\set{A}| - 1)} \sum_{i \in \set{A}} \sum_{\substack{j \in \set{A} \\ j \neq i}} \frac{{\Vector{v}_i}^\top \Vector{v}_j}{\norm{\Vector{v}_i} \norm{\Vector{v}_j}}
\end{equation}
which measures the degree to which the heading of the agents agree with each other \cite{viragh_flocking_2014}.
If the headings are aligned, we have $\omega^\txt{ord} \approx 1$ and in a disordered state, we have $\omega^\txt{ord} \approx 0$.
Recall that the agent's body frame is always aligned with the direction of motion.

\subsection{Common migration goal}\label{sec:common-migration-goal}

In the first experiment, we give all agents the same migration goal and show that the swarm remains collision-free during navigation.
Both the \tit{vision-based} and the \tit{position-based} swarm exhibit remarkably similar behavior while migrating (see Figs.~\ref{fig:common-position} and \ref{fig:common-vision}).
For the \tit{vision-based} controller, one should notice that the velocity commands predicted by the neural network are sent to the agents in their raw form without any further processing.

We are especially interested in the minimum and maximum inter-agent distances, i.e. the extreme distances between any pair of agents, during migration.
The minimum distance can be used as a direct measure for collision avoidance, whereas the maximum distance is a helpful metric when deciding whether or not a swarm is coherent.
The \tit{vision-based} controller matches the \tit{position-based} one very well since they do not deviate significantly over the course of the entire trajectory (see Fig.~\ref{fig:common-distances}).
If the neural controller had not learned to keep a minimum inter-agent distance, we would observe collisions in this case.

\subsection{Opposing migration goals}\label{sec:opposing-migration-goals}

In this experiment, we assign different migration goals to two subsets of agents.
The first group, consisting of five agents, is assigned the same waypoint as in Sec.~\ref{sec:common-migration-goal}.
The second group, consisting of the remaining four agents, is assigned a migration point on the opposite side with respect to the first group.
The \tit{position-based} and \tit{vision-based} flock exhibit very similar migration behaviors (see Figs.~\ref{fig:opposing-position} and \ref{fig:opposing-vision}).
In both cases, the swarm cohesion is strong enough to keep the agents together despite the diverging navigational preferences.
This is the first sign that the neural network learns the non-trivial difference between agents that are too close or too far away.

We can observe slightly different behaviors between the two modalities when measuring the alignment within the flock.
The \tit{position-based} flock tends to be ordered to a greater extent than its \tit{vision-based} counterpart (see Fig.~\ref{fig:opposing-order}).
This periodicity in the order of the flock stems from circular motion exhibited by the \tit{position-based} agents (see Fig.~\ref{fig:opposing-position}).
There is less regularity in the \tit{vision-based} flock, in which agents tend to be less predictable in their trajectories, albeit remaining collision-free and coherent.
Note that the \tit{vision-based} flock tends to be less well aligned and also reaches its migration goal far later than the \tit{position-based} flock.

\subsection{Generalization of the neural controller}\label{sec:generalization}

We performed a series of ablation studies to show that the learned controller generalizes to previously unseen scenarios.
These experiments can be seen as perturbations to the conditions that the agents were exposed to during the training phase.
This allows us to highlight failure cases of the controller as well as show its robustness towards changing conditions.
We change parameters such as the number of agents $N = \{3, 12\}$ in the swarm or the maximum flock speed $v^\mtx{max} = \{ 2.0, 4.0\}$\si{\meter\per\second}.
We also remove the external migration point $\Vector{p}^\mtx{mig}$ and the corresponding term entirely to show how the flock behaves when it is self-propelled.

The swarm remains collision-free and coherent without migration point or when the number of agents and the flock speed is changed.
We could not observe a noticeable difference in the inter-agent distance or the order parameter during the experiments.

\subsection{Attribution study}\label{sec:attribution-study}

Since the \tit{vision-based} controller provides a very tight coupling between perception and control, the need for interpretation of the learned behavior arises.
To this end, we employ a state-of-the-art attribution method \cite{selvaraju_grad-cam_2017}, which shows how much influence each pixel in the input image has on the predicted velocity command (see Fig.~\ref{fig:heatmap}).
More specifically, we compute the gradients for the heat map by backpropagating with respect to the penultimate strided convolutional layer of the neural network in which the individual feature maps retain a spatial size of $4 \times 24$ pixels.
We then employ bilinear upsampling to increase the resolution of the resulting saliency map before we blend it with the original input image.
We attribute the relatively poor localization performance of some of the agents to the low spatial acuity of the generated heat map.

We can observe a non-uniform distribution of importance that seems to concentrate on a single agent that is located in the field of view of the front-facing camera (see Fig.~\ref{fig:field-of-view}).
The image is taken from an early time step during migration where the magnitudes of the predicted velocity commands are relatively large.
We notice that the network is effectively localizing the other agents spatially in the visual input, albeit having not been explicitly given the positions as targets.
The saliency map is generated very efficiently by computing the backward pass until the target convolutional layer and could therefore serve as a valuable input to a real-time detection algorithm.

\begin{figure}
    \centering
    \includegraphics[width=\columnwidth]{results/attribution/heatmap.pdf}
    \caption{%
        Heat map visualization of the relative importance of each pixel in the visual input of an agent towards the velocity command.
        Red regions have the most influence on the control command, whereas blue regions contribute the least.
    }\label{fig:heatmap}
\end{figure}

\section{Conclusions and Future Work}\label{sec:conclusions}

This paper presented a machine learning approach to the problem of collision-free and coherent motion of a dense swarm of quadcopters.
The agents learn to coordinate themselves \tit{only} via visual inputs in 3D space by mimicking a flocking algorithm.
The learned controller removes the need for communication of positions among agents and thus presents the first step towards a fully decentralized vision-based swarm of drones.
The trajectories of the flock are relatively smooth even though the controller is based on \tit{raw} neural network predictions.
We show that our method is robust to perturbations such as changes in the number of agents the or maximum speed of the flock.
Our algorithm naturally handles navigation tasks by adding a migration term to the predicted velocity of the neural controller.

We are actively working on transferring the flock of quadrotors from a simulated environment into the real world.
A motion capture system is used to generate the dataset of ground truth positions and real camera images, similar to the simulation setup described in this paper.
A natural subsequent step will be the transfer of the learned controller to outdoor scenarios where ground truth positions will be obtained using a GNSS.
To reduce the need for large amounts of labeled data, we are exploring recent advances in deep domain adaptation \cite{ganin_domain-adversarial_2016,rozantsev_beyond_2018} to aid generalization of the neural controller to environments with background clutter.
Another challenge is the addition of obstacles to the environment in which the agents operate.
To this end, we will opt for more sophisticated flocking algorithms which allow the direct modeling of obstacles \cite{viragh_flocking_2014,vasarhelyi_optimized_2018}, as well as pre-defining the desired distance between agents \cite{olfati-saber_flocking_2006}.

\section{Acknowledgements}\label{sec:acknowledgements}

\ifblind
    Intentionally left blank for double-blind review purposes.
\else
    \blind{We thank Enrica Soria for the feedback and helpful discussions, as well as Olexandr Gudozhnik and Przemyslaw Kornatowski for their contributions to the drone hardware.}
    \blind{This research was supported by the Swiss National Science Foundation (SNF) with grant number 200021\_155907 and the Swiss National Center of Competence Research (NCCR).}
\fi

\fontsize{9.0pt}{10.0pt}\selectfont

redpajama_set_name: RedPajamaArXiv


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