NEURAL COMPUTATION FOR COLLISION-FREE PATH PLANNING

Automatic path planning plays an essential role in planning of assembly or disassembly of products, motions of robot manipulators handling part, and material transfer by mobile robots in an intelligent and flexible manufacturing environment. The conventional methodologies based on geometric reasoning suffer not only from the algorithmic difficulty but also from the excessive time complexity in dealing with 3-D path planning. This paper presents a massively parallel, connectionist algorithm for collision-free path planning. The path planning algorithm is based on representing a path as a series of via points or beads connected by elastic strings which are subject to displacement due to a potential field or a collision penalty function generated by polyhedral obstacles. Mathematically, this is equivalent to optimizing a cost function, defined in terms of the total path length and the collision penalty function, by moving the via points simultaneously but individually in the direction that minimizes the cost function. Massive parallelism comes mainly from: (1) the connectionist model representation of obstacles and (2) the parallel computation of individual via-point motions with only local information. The algorithm has power to deal effectively with path planning of three-dimensional objects with translational and rotational motions. Finally, the algorithm incorporates simulated annealing to solve a local minimum problem. Simulation results are shown.