Co-evolving controller and sensing abilities in a simulated Mars Rover explorer 21 st  May, 2009 Trondheim, Norway
Introduction: Mars Robots <ul><li>Fundamental requirement of all planetary robots is a high degree of autonomy and safety ...
Introduction: Mars Robots <ul><li>Spirit and Opportunity rovers travel slower than 1cm/s when in autonomous mode </li></ul...
Rover model: predecessors
Rover model: overview <ul><li>Rocker-bogie  suspension </li></ul><ul><li>Open dynamics engine  </li></ul><ul><li>Open GL <...
Rover model: closer look
Rover model: “brain” & “senses”
Rover model: threshold 40cm
Rover model: threshold
Rover model: sensors in action
Experimental setup: <ul><ul><li>Elitism </li></ul></ul><ul><ul><li>Fitness function: </li></ul></ul><ul><ul><li>Genotype <...
Environment
Results <ul><li>Simulations results show that the robot, at the end of the evolutionary process, is able to avoid rocks, h...
 
Co-evolving controller and sensing ability <ul><li>Fitness starts to increase once the threshold value settles </li></ul>
Co-evolving controller and sensing ability <ul><li>Fitness starts to increase after sudden change of threshold value </li>...
Co-evolving controller and sensing ability <ul><li>Early presence of good obstacle avoidance behaviour with unsuitable thr...
Tests: evaluating robustness <ul><li>Best rover from last generation was tested on two other terrains. </li></ul><ul><ul><...
Tests: evaluating robustness <ul><li>Exploration ability depends on fitness and on type of the terrain </li></ul>
Current research on active vision pan tilt zoom speed steering pan tilt speed steering visual neurons
Preliminary results
Conclusion <ul><li>Evolutionary robotics applied in space research domain </li></ul><ul><li>A model of a Mars rover robot ...
 
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Co-evolving controller and sensing abilities in a simulated Mars Rover explorer

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M. Peniak, D.Marocco, A. Cangelosi (2009). Co-evolving controller and sensing abilities in a simulated Mars Rover explorer. IEEE Congress on Evolutionary Computation (CEC) 2009. Trondheim, Norway, 18th-21nd May

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  • Theoretically if we were able to communicate with planetary rovers instantaneously we might not really need a robot to be autonomous. Because it takes so long for radio waves to propagate it is absolutely vital for a planetary robot to be fully autonomous. Moreover, such missions are normally prepared and planned for a very long time and therefore we must be absolutely Sure that all systems are reliable and safe.
  • The fundamental requirement of an autonomous planetary rover is the ability to avoid obstacles. Currently the obstacle avoidance on both Spirit and Opportunity rovers is done by means of heavy processing of stereo images obtained through stereo cameras. We aim to develop a fast lightweight obstacle avoidance system that would minimize processing power needed
  • ANNs as a simplified model of a brain and GA to simulate natural evolution
  • Co-evolving controller and sensing abilities in a simulated Mars Rover explorer

    1. 1. Co-evolving controller and sensing abilities in a simulated Mars Rover explorer 21 st May, 2009 Trondheim, Norway
    2. 2. Introduction: Mars Robots <ul><li>Fundamental requirement of all planetary robots is a high degree of autonomy and safety </li></ul><ul><ul><li>It takes 4.3 to 21 minutes for radio signal to reach Mars from Earth </li></ul></ul><ul><ul><li>Planetary robotics mission are extremely expensive, e.g. NASA spent $800 million to build and launch Spirit and Opportunity rovers to Mars </li></ul></ul>
    3. 3. Introduction: Mars Robots <ul><li>Spirit and Opportunity rovers travel slower than 1cm/s when in autonomous mode </li></ul><ul><li>Obstacle avoidance is based on stereo cameras producing 3D representation of surrounding environment </li></ul><ul><ul><li>This solution is effective but rather slow as lot of processing needs to be done </li></ul></ul>
    4. 4. Rover model: predecessors
    5. 5. Rover model: overview <ul><li>Rocker-bogie suspension </li></ul><ul><li>Open dynamics engine </li></ul><ul><li>Open GL </li></ul><ul><li>MSL rover </li></ul><ul><li>2.9 x 2.7 x 2.2m </li></ul><ul><li>775kg </li></ul>
    6. 6. Rover model: closer look
    7. 7. Rover model: “brain” & “senses”
    8. 8. Rover model: threshold 40cm
    9. 9. Rover model: threshold
    10. 10. Rover model: sensors in action
    11. 11. Experimental setup: <ul><ul><li>Elitism </li></ul></ul><ul><ul><li>Fitness function: </li></ul></ul><ul><ul><li>Genotype </li></ul></ul>... connection weights threshold w 1 w 2 w 3 w 4 w 5 w 6 w 7 w 8 w 9 w 10 w 11 w 80 t
    12. 12. Environment
    13. 13. Results <ul><li>Simulations results show that the robot, at the end of the evolutionary process, is able to avoid rocks, holes and steep slopes based purely on the information provided by the infrared sensors </li></ul>
    14. 15. Co-evolving controller and sensing ability <ul><li>Fitness starts to increase once the threshold value settles </li></ul>
    15. 16. Co-evolving controller and sensing ability <ul><li>Fitness starts to increase after sudden change of threshold value </li></ul>
    16. 17. Co-evolving controller and sensing ability <ul><li>Early presence of good obstacle avoidance behaviour with unsuitable threshold </li></ul>
    17. 18. Tests: evaluating robustness <ul><li>Best rover from last generation was tested on two other terrains. </li></ul><ul><ul><li>Terrain with more obstacles </li></ul></ul><ul><ul><li>Terrain with increased surface roughness </li></ul></ul>
    18. 19. Tests: evaluating robustness <ul><li>Exploration ability depends on fitness and on type of the terrain </li></ul>
    19. 20. Current research on active vision pan tilt zoom speed steering pan tilt speed steering visual neurons
    20. 21. Preliminary results
    21. 22. Conclusion <ul><li>Evolutionary robotics applied in space research domain </li></ul><ul><li>A model of a Mars rover robot autonomously avoiding obstacles in different environments </li></ul><ul><li>Different co-evolutionary scenarios </li></ul><ul><li>Tests of robustness of the evolved controllers </li></ul><ul><li>Current research on active vision </li></ul>

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