Adaptive path planning for flexible manufacturing

Path planning needs to be fast to facilitate real-time robot programming. Unfortunately, current planning techniques are still too slow to be effective, as they often require several minutes, if not hours of computation. To overcome this difficulty, the author presents an adaptive algorithm that uses past experience to speed up future performance. It is a learning algorithm suitable for automating flexible manufacturing in incrementally-changing environments. The algorithm allows the robot to adapt to its environment by having two manipulation schemes. For minor environmental change, the author uses an object-attached experience abstraction scheme to increase the flexibility of the learned experience; for major environmental change, the author uses an on-demand experience repair scheme to retain those experiences that remain valid and useful. Using this algorithm, one can effectively reduce the overall robot planning time by re-using the computation result for one task to plan a path for another