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

21st May, 2009 Trondheim, Norway

Introduction: Mars Robots

Fundamental requirement of all planetary robots is a high degree of autonomy and safety It takes 4.3 to 21 minutes for radio

signal to reach Mars from Earth Planetary robotics mission are extremely

expensive, e.g. NASA spent $800 million to build and launch Spirit and Opportunity rovers to Mars

Introduction: Mars Robots

Spirit and Opportunity rovers travel slower than 1cm/s when in autonomous mode

Obstacle avoidance is based on stereo cameras producing 3D representation of surrounding environment This solution is effective but rather slow as lot of

processing needs to be done

Rover model: predecessors

Rover model: overview

Rocker-bogie suspension Open dynamics engine Open GL

MSL rover 2.9 x 2.7 x 2.2m 775kg

Rover model: closer look

Rover model: “brain” & “senses”

Rover model: threshold

40cm

Rover model: threshold

Rover model: sensors in action

Experimental setup:

Elitism Fitness function:

Genotype

...

connection weights threshold

w1 w2 w3 w4 w5 w6 w7 w8 w9 w10 w11 w80 t

Environment

Results

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

Co-evolving controller and sensing ability Fitness starts to increase once the threshold

value settles

Co-evolving controller and sensing ability Fitness starts to increase after sudden change

of threshold value

Co-evolving controller and sensing ability Early presence of good obstacle avoidance

behaviour with unsuitable threshold

Tests: evaluating robustness

Best rover from last generation was tested on two other terrains. Terrain with more obstacles Terrain with increased surface

roughness

Tests: evaluating robustness

Exploration ability depends on fitness and on type of the terrain

Current research on active vision

pan

tilt

zoom

speed

steering

pan

tilt

speed

steering

visual neurons

Preliminary results

Conclusion

Evolutionary robotics applied in space research domain

A model of a Mars rover robot autonomously avoiding obstacles in different environments

Different co-evolutionary scenarios Tests of robustness of the evolved

controllers Current research on active vision