Real-time Obstacle Avoidance Strategy for Mobile Robot Based On Improved Coordinating Potential Field with Genetic Algorithm

摘要

To overcome the problems during navigation of mobile robots in dynamic environment using the traditional artificial potential field (APF) method, a novel improved method called coordinating potential field (CPF) is proposed. The local potential field is constructed by using local subgoals, which obtained by updating dynamic windows. The questions of local minima, oscillation between multiple obstacles and real-time dynamic obstacle avoidance are solved. At last multi-objective parameter optimization is implemented by using adaptive genetic algorithm. Simulation results indicate that this strategy is practicable and effective.