PPNet: A Two-Stage Neural Network for End-to-end Path Planning

arXiv:2401.09819v2 Announce Type: replace-cross
Abstract: The classical path planners, such as sampling-based path planners, can provide probabilistic completeness guarantees in the sense that the probability that the planner fails to return a solution if one exists, decays to zero as the number of samples approaches infinity. However, finding a near-optimal feasible solution in a given period is challenging in many applications such as the autonomous vehicle. To achieve an end-to-end near-optimal path planner, we first divide the path planning problem …

abstract arxiv cs.ai cs.lg cs.ro however near network neural network path planning probability samples sampling sense solution stage type