Multi-artificial fish-swarm algorithm and a rule library based dynamic collision avoidance algorithm for robot path planning in a dynamic environment

In order to improve the convergence speed and the environmental adaptability of the path planning algorithm, a robot path planning algorithm based on multi-artificial fish-swarm is proposed. We present also a dynamic obstacle avoidance algorithm based on the rule-base of collision avoidance in dynamic environment to avoid collisions with the moving obstacles. In our approach, the distance between a fish and a goal is defined as food concentration and the distance between two neighbor grids is defined as step length. The preying behavior of fishes is regard as default behavior and perform clusters act or rear-end act is activated in some certain condition. Then the optimal path in static environment is planned by the search mechanism of bi-directional fish-swarms. After that, the effective collision avoidance behavior is obtained, from the obtained dynamic obstacle information through sensors. Many simulation experiments have shown that the algorithm has a fast convergence speed and strong search capability. Even in complex environments which have static and dynamic obstacles, it can avoid collision safely and plan an optimal path rapidly at the same time.