Evolutionary Neural Network-Based Obstacle Avoidance for a Planetary Exploration Rover

During space missions, a planetary exploration rover is subject to two main communication issues being the distance and orbital difference between Earth and the target planet, the communication transmission delay and the limited transmission window. The proposed autonomous path planning system presents a set of genetically evolved neural network controllers for local path planning of a mobile robot. Each evolved network is adopted to direct the rover travelling in one category of partitioned environments achieving a sequence of targets with obstacle avoidance. With a set of pre-learned networks, the rover would be adaptable to traverse in new environments of specific category. Genetic algorithm is used to obtain the evolved network by developing behavior strategies through evolutionary iterations. Simulation results indicate that evolved neural controller can adapt to novel environments and generate satisfying path for the rover in a computationally economic manner.