An Evolved, Vision-Based Model of Obstacle Avoidance Behavior

Using a simple computational model of visual perception and locomotion, obstacle avoidance behavior can emerge from evolution under selection pressure from an appropriate fitness measure. The Genetic Programming paradigm is used to model evolution. Both the structure of the visual sensor array, and the mapping from sensor data to motor action is determined by an evolved control program. The motor model assumes an innate constant forward velocity and limited steering. The agent can avoid collisions only by effective steering. Fitness is based on the number of simulation steps the agent can run before colliding with an obstacle.