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Co-evolving controller and sensing abilities in a simulated Mars Rover explorer 21 st  May, 2009 Trondheim, Norway
Introduction: Mars Robots ,[object Object],[object Object],[object Object]
Introduction: Mars Robots ,[object Object],[object Object],[object Object]
Rover model: predecessors
Rover model: overview ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Rover model: closer look
Rover model: “brain” & “senses”
Rover model: threshold 40cm
Rover model: threshold
Rover model: sensors in action
Experimental setup: ,[object Object],[object Object],[object Object],... 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
Environment
Results ,[object Object]
 
Co-evolving controller and sensing ability ,[object Object]
Co-evolving controller and sensing ability ,[object Object]
Co-evolving controller and sensing ability ,[object Object]
Tests: evaluating robustness ,[object Object],[object Object],[object Object]
Tests: evaluating robustness ,[object Object]
Current research on active vision pan tilt zoom speed steering pan tilt speed steering visual neurons
Preliminary results
Conclusion ,[object Object],[object Object],[object Object],[object Object],[object Object]
 

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Co-evolving controller and sensing abilities in a simulated Mars Rover explorer

Notas del editor

  1. 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.
  2. 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
  3. ANNs as a simplified model of a brain and GA to simulate natural evolution