The present invention relates in general to the field of fuzzy logic and more particularly to the use of fuzzy logic to help navigate a semi-autonomous vehicle such as the Mars Rover.
It is envisioned that certain exploration functions--planetary, space, undersea--will best be performed by unmanned vehicles capable of operating with minimal supervision by a remote human operator. Such vehicles are termed semi-autonomous as they must operate for periods of time in a completely autonomous manner. During these periods of the autonomous behavior, an on-board subsystem must navigate the vehicle through a potentially hazardous environment to a selected destination and guarantee the safety of the vehicle during transit. This navigation system must be provided information about the features of the terrain or space to be traversed so as to choose a best path and avoid any hazards. It is anticipated that such data will be provided by both external sources and on-board sensor subsystems. In addition to acquiring gross information about the terrain or space to be traversed from an external source, such as an orbiting satellite for planetary exploration, it is anticipated that more fine grained data will be acquired from on-board sensor subsystems such as optical vision modules. Data from these disparate sources must be integrated to describe the terrain or space to be traversed. However, the inputs which it receives about the environment from these various sources--image understanding software, maps, satellites--will most probably be uncertain and incomplete. This information can be uncertain because there could be evidence which supports the data but there also could be evidence which refutes the data. For the example of a planetary semi-autonomous vehicle, the image understanding software could determine that a certain region is composed of packed gravel, which would be safe but the degree of belief might not be 100% and the region in question could also contain loose gravel, which is unsafe. The information may be incomplete as certain sections of the terrain may not be accessible to the sensors or may contain features which can not be distinguished by the sensors.
In order to navigate in an environment which is not completely known and understood, the navigation module needs to reason with the uncertain information and compute the best path--criteria such as shortest distance, minimum time, and least power consumed--which also guarantees the safety of the vehicle.
Existing systems are essentially deterministic in that they follow a fixed set of rules and assume complete and accurate data about the environment to be traversed. When that assumption fails, the navigation system typically fails.