A Rolling Method for Complete Coverage Path Planning in Uncertain Environments

Motion planning with obstacles avoidance in uncertain environments is an essential issue in robotics. Complete coverage path planning of a mobile robot requires the robot to pass through every area in the workspace with collision-free, which has many applications, e.g., various cleaning robots, painter robots, automated harvesters, land mine detectors and so on. A novel planning method integrating rolling windows and biologically inspired neural networks is proposed in this paper. The real-time environmental information can be represented by the dynamic activity landscape of the biological neural network. The rolling window approach is used to detect the local environments. Thus, a heuristic planning algorithm is performed on-line in rolling strategy. Three cases on complete coverage path planning in uncertain environments and the comparison of the proposed method and the planning approach based on the biologically inspired neural networks are studied by simulations. Simulation results show that the proposed method is capable of planning collision-free complete coverage robot motion path

[1]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1990 .

[2]  Dewi I. Jones,et al.  A tangent based method for robot path planning , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[3]  Max Q.-H. Meng,et al.  An efficient neural network approach to dynamic robot motion planning , 2000, Neural Networks.

[4]  Jean-Claude Latombe,et al.  Robot motion planning , 1970, The Kluwer international series in engineering and computer science.

[5]  Xi Yugeng,et al.  Robot path planning in globally unknown environments based on rolling windows , 2001 .

[6]  Zhang Chun,et al.  Robot Rolling Path Planning Based on Locally Detected Information , 2003 .

[7]  S.X. Yang,et al.  A neural network approach to complete coverage path planning , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[8]  Stephen Grossberg,et al.  Nonlinear neural networks: Principles, mechanisms, and architectures , 1988, Neural Networks.

[9]  Max Q.-H. Meng,et al.  Neural network approaches to dynamic collision-free trajectory generation , 2001, IEEE Trans. Syst. Man Cybern. Part B.