Answers.Body: <p>The Mars Rover is a highly successful example of the 'New AI' that emerged from work by Rodney Brooks in the 1990s.</p>
  
  <p>In a <a href="https://www.flinders.edu.au/alumni/alumni-community/prominent-alumni/rod-brooks.cfm" rel="nofollow noreferrer">quote</a> from Brooks: </p>
  
  <blockquote>
    <p>In 1984 I joined the faculty at MIT where I have been ever since. I set up a mobile robot group there and started developing robots that led to the Mars planetary rovers. </p>
  </blockquote>
  
  <p>Together with the <a href="http://www.freelug.net/IMG/pdf/A_Robust_Layered_Control_System_-_Brooks_AI_Memo864.pdf" rel="nofollow noreferrer">'Allen' paper</a>, the foundational AI articles in this area are:</p>
  
  <ul>
  <li><a href="http://people.csail.mit.edu/brooks/papers/elephants.pdf" rel="nofollow noreferrer">"Elephants don't play chess"</a></li>
  <li><a href="https://www.cs.nyu.edu/courses/fall01/G22.3033-012/readings/representation.ps" rel="nofollow noreferrer">"Intelligence without representation"</a></li>
  </ul>
  
  <p>Although Brooks initially had difficulty getting this work published, preprints were widely circulated within the AI community. Brook's "Physical Grounding Hypothesis" (essentially: "intelligence requires a body") has now largely supplanted the preceding symbolist approach.</p>
  
  <p>The capabilities of the MARS Rover are organized in a <a href="https://en.wikipedia.org/wiki/Subsumption_architecture" rel="nofollow noreferrer">Subsumption Architecture</a>. Rather than maintaining an integrated and complex 'world model', increasingly sophisticated behaviors are stacked in hierarchical layers. For example, 'walking' is a relatively low-level competence, with 'avoiding obstacles' and 'wandering around' being higher-level ones. </p>
  
  <p><a href="https://i.sstatic.net/5VhJR.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/5VhJR.png" alt="Layers in a subsumption architecture"></a></p>
  
  <p>Each layer is represented by a Finite State Machine that reacts to stimuli appropriate to that level. The activity of lower levels can be suppressed ('subsumed') by higher level ones.</p>
  
  <p>Here is a schematic of the bottom two layers of <a href="http://www.freelug.net/IMG/pdf/A_Robust_Layered_Control_System_-_Brooks_AI_Memo864.pdf" rel="nofollow noreferrer">'Allen'</a>, Brook's first subsumption robot:</p>
  
  <p><a href="https://i.sstatic.net/mJIV6.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/mJIV6.png" alt="Layers for obstacle avoidance and wandering"></a></p>
  
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Answers.Text: You should update the link to the quote, which is now broken.
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Answers.Body: <p>There is an interview (link see below) with David E. Smith, a  senior  researcher  in  the  Intelligent  Systems Division  at  NASA  Ames  Research Center. In this interview, he talks about the application of AI and AI planning in particular in his work at NASA. He also (just shortly) mentions the Mars Exploration Rover and cites related scientific papers (just search for "Mars").</p>
  
  <p>Link to the official publication at Springer:<br>
  <a href="http://link.springer.com/article/10.1007%2Fs13218-015-0403-y" rel="nofollow noreferrer">http://link.springer.com/article/10.1007%2Fs13218-015-0403-y</a></p>
  
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Body: <p>The Mars Exploration Rover (MER) <em><a href="http://www.nasa.gov/mp4/618340main_mer20120124-320-jpl.mp4" rel="nofollow">Opportunity</a></em> landed on Mars on January 25, 2004. The rover was originally designed for a 90 <strong>Sol mission</strong> (a Sol, one Martian day, is slightly longer than an Earth day at 24 hours and 37 minutes). Its mission has been extended several times, the machine is still trekking after 11 years on the Red Planet.</p>
  
  <p>How it has been working for 11 years? Can anyone please explain how smart this rover is? What AI concepts are behind this?</p>
  
Tags: <control-problem><robotics><nasa>
Title: What AI concept is behind the Mars Exploration Rover (MER)?
UserDisplayName: Abhishek T.
ThreadText: # What AI concept is behind the Mars Exploration Rover (MER)?
  
  (Asked by: username_0 on 2016-09-13)
  
  The Mars Exploration Rover (MER) *[Opportunity](http://www.nasa.gov/mp4/618340main_mer20120124-320-jpl.mp4)* landed on Mars on January 25, 2004. The rover was originally designed for a 90 **Sol mission** (a Sol, one Martian day, is slightly longer than an Earth day at 24 hours and 37 minutes). Its mission has been extended several times, the machine is still trekking after 11 years on the Red Planet.
  
  How it has been working for 11 years? Can anyone please explain how smart this rover is? What AI concepts are behind this?
  
  **Tags:** control-problem, robotics, nasa
  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2016-09-13. Score: 2
  
  The Mars Rover is a highly successful example of the 'New AI' that emerged from work by Rodney Brooks in the 1990s.
  
  In a [quote](https://www.flinders.edu.au/alumni/alumni-community/prominent-alumni/rod-brooks.cfm) from Brooks:
  
  > In 1984 I joined the faculty at MIT where I have been ever since. I set up a mobile robot group there and started developing robots that led to the Mars planetary rovers.
  
  Together with the ['Allen' paper](http://www.freelug.net/IMG/pdf/A_Robust_Layered_Control_System_-_Brooks_AI_Memo864.pdf), the foundational AI articles in this area are:
  
  * ["Elephants don't play chess"](http://people.csail.mit.edu/brooks/papers/elephants.pdf)
  * ["Intelligence without representation"](https://www.cs.nyu.edu/courses/fall01/G22.3033-012/readings/representation.ps)
  
  Although Brooks initially had difficulty getting this work published, preprints were widely circulated within the AI community. Brook's "Physical Grounding Hypothesis" (essentially: "intelligence requires a body") has now largely supplanted the preceding symbolist approach.
  
  The capabilities of the MARS Rover are organized in a [Subsumption Architecture](https://en.wikipedia.org/wiki/Subsumption_architecture). Rather than maintaining an integrated and complex 'world model', increasingly sophisticated behaviors are stacked in hierarchical layers. For example, 'walking' is a relatively low-level competence, with 'avoiding obstacles' and 'wandering around' being higher-level ones.
  
  [![Layers in a subsumption architecture](https://i.sstatic.net/5VhJR.png)](https://i.sstatic.net/5VhJR.png)
  
  Each layer is represented by a Finite State Machine that reacts to stimuli appropriate to that level. The activity of lower levels can be suppressed ('subsumed') by higher level ones.
  
  Here is a schematic of the bottom two layers of ['Allen'](http://www.freelug.net/IMG/pdf/A_Robust_Layered_Control_System_-_Brooks_AI_Memo864.pdf), Brook's first subsumption robot:
  
  [![Layers for obstacle avoidance and wandering](https://i.sstatic.net/mJIV6.png)](https://i.sstatic.net/mJIV6.png)
  
  > **Comments:**
  > **username_2**: You should update the link to the quote, which is now broken.  
  
  ---
  
  ### Answer by username_3 on 2017-03-09. Score: 0
  
  There is an interview (link see below) with David E. Smith, a senior researcher in the Intelligent Systems Division at NASA Ames Research Center. In this interview, he talks about the application of AI and AI planning in particular in his work at NASA. He also (just shortly) mentions the Mars Exploration Rover and cites related scientific papers (just search for "Mars").
  
  Link to the official publication at Springer:  
  <http://link.springer.com/article/10.1007%2Fs13218-015-0403-y>
  
  
  ---
  
  