A dynamic risk level based bioinspired neural network approach for robot path planning

Abstract

Path planning problem is one of the most important and challenging issue in robot control field. In this paper, an improved bioinspired neural network approach is proposed for real-time path planning of robots. In the proposed approach, a new function is used to calculate the connection weight of the bioinspired neural network, to reduce the fluctuation of the path produced by the general bioinspired neural network. Furthermore, a dynamic risk level is introduced into the proposed approach, to improve the performance of the proposed approach in dynamic obstacle avoidance task. In comparison to the general bioinspired neural network based method, experimental results show that the trajectories of robot produced by the proposed approach is optimized, and the proposed approach can deal with the path planning task in dynamic environment efficiently.