Neural network application to the obstacle avoidance path planning for CIM computer integrated manufacturing

Path planning is an important task for optimal motion of a robot in a structured or unstructured environment. The paper shows how to plan the shortest collision-free path in 3D, when a robot is navigated to pick up some tools or to repair some parts from various locations. A path coordinator is proposed having the capabilities of an obstacle avoidance strategy and a traveling salesman problem (TSP) strategy. The obstacle avoidance strategy is to plan the shortest collision-free path between each pair of n locations in 2D or in 3D. The TSP strategy is to compute a minimal system cost of a tour that is defined as a closed path navigating each location exactly once. The TSP strategy can be implemented by the neural network. The obstacle avoidance strategy can be implemented by the optimal edges by the modified genetic algorithm and computes the optimal nodes along the optimal edges by the recursive compensation algorithm