Integrating asymptotically-optimal path planning with local optimization
This addresses the need for efficient online path planning in unpredictable environments for robotics, representing an incremental improvement over existing methods.
The paper tackles the problem of slow convergence in asymptotically optimal path planning for robots by integrating it with a local optimizer, resulting in a 31% faster path execution compared to the state-of-the-art RRTConnect* planner given 3 seconds of planning time.
Many robots operating in unpredictable environments require an online path planning algorithm that can quickly compute high quality paths. Asymptotically optimal planners are capable of finding the optimal path, but can be slow to converge. Local optimisation algorithms are capable of quickly improving a solution, but are not guaranteed to converge to the optimal solution. In this paper we develop a new way to integrate an asymptotically optimal planners with a local optimiser. We test our approach using RRTConnect* with a short-cutting local optimiser. Our approach results in a significant performance improvement when compared with the state-of-the-art RRTConnect* asymptotically optimal planner and computes paths that are 31\% faster to execute when both are given 3 seconds of planning time.