Collision-free path planning with neural networks

We present an efficient path planning approach which represents a path by a series of via points connected by elastic strings that are subject to displacement due to collisions with obstacles as well as constraints pertaining to the domain to which path planning is applied. Obstacle regions are represented by a potential field created by a multilayered neural network. A fast simulated annealing approach is used for local minima problems from the potential field. The automatic generation and removal of via points is incorporated in the path planning approach to ensure collision-free planning regardless of the complexity of the environment (e.g., convex, concave or complicated obstacle regions). Our path planning approach is flexible due to the automatic generation and removal of via points based on the complexity of the domain of the path planning process, efficient due to the use of the necessary via points for the path representation at all times, and massively parallel due to the parallel computation of motions of via points with only local information