Jianfeng Zhang, Houyong Lv, Dongjian He, Lvwen Huang, Yuan Dai, and Zhiyong Zhang
Robot, complete coverage path planning, bioinspired neural network,discretization
Complete coverage path planning (CCPP) is a special type of path planning, which requires the robot to cover every part of the workspace except obstacles reasonably and efficiently. Bioinspired neural network is one of the hotspots in the research of path planning. In this paper, the discrete bioinspired neural network (D-BINN) is proposed to reduce the computational redundancy of the neural activities. The stability of the proposed approach is analysed, and the reasonable sampling period is also given. Finally, it is applied to CCPP of an autonomous mobile robot. The simulation results show that the D-BINN could reduce efficiently the computational burden of the neural activities and improve the real-time performance and effectiveness of path planning.
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