Improving robustness of robot programs generated by genetic programming for dynamic environments

This paper proposes a method to improve robustness of the robot programs generated by genetic programming. The main idea is to perturb the simulated environment during evolution of the solutions. The resulting robot programs are more robust because they have been evolved to tolerate the changes in their environment. We set out to test this idea using the problem of navigating a mobile robot from a starting point to a target point in an unknown cluttered environment where obstacles can be moved dynamically. The result shows the effectiveness of this scheme.