Evolution of visual controllers for obstacle avoidance in mobile robotics

Abstract The purpose of this work is to automatically design vision algorithms for a mobile robot, adapted to its current visual context. In this paper we address the par-ticular task of obstacle avoidance using monocular vision. Starting from a set of primitives composed of the different techniques found in the literature, we propose a generic structure to represent the algorithms, using standard reso-lution video sequences as an input, and velocity commands to control a wheel robot as an output. Grammar rules are then used to construct correct instances of algorithms, that are then evaluated using different protocols: evaluation of trajectories performed in a goal reaching task, or imitation of a hand-guided trajectory. A genetic program is applied to evolve populations of algorithms in order to optimize the performances of the controllers. The first results obtained in a simulated environment show that the evolution pro-duces algorithms that can be easily interpreted and which are clearly adapted to the visual context. However, the resulting trajectories are often erratic, and the generaliza-tion capacities are poor. To improve the results, we propose to use a two-phase evolution combining imitation and goal reaching evaluations, and to add some constraints in the grammar rules to enforce a more generic behavior. The results obtained in simulation show that the evolved algo-rithms are more efficient and more generic. Finally, we apply the imitation based evolution on real sequences and test the evolved algorithms on a real robot. Though sim-plified by dropping the goal reaching constraint, the resulting algorithms behave well in a corridor centering task, and show certain generalization capacities