Sampling-based planning algorithms like Rapidly-exploring Random Tree (RRT) are versatile in solving path planning problems. RRT* offers asymptotical optimality but requires growing the tree uniformly over the free space, which leaves room for efficiency improvement. To accelerate convergence, informed approaches sample states in an ellipsoidal subset of the search space determined by current path cost during iteration. Learning-based alternatives model the topology of the search space and infer the states close to the optimal path to guide planning. We combine the strengths from both sides and propose Neural Informed RRT* with Point-based Network Guidance. We introduce Point-based Network to infer the guidance states, and integrate the network into Informed RRT* for guidance state refinement. We use Neural Connect to build connectivity of the guidance state set and further boost performance in challenging planning problems. Our method surpasses previous works in path planning benchmarks while preserving probabilistic completeness and asymptotical optimality. We demonstrate the deployment of our method on mobile robot navigation in the real world.

翻译：采样型规划算法（如快速探索随机树 RRT）在解决路径规划问题中具有广泛适用性。RRT* 可实现渐近最优性，但需要在自由空间中均匀扩展树结构，这限制了效率提升的空间。为加速收敛，受信息引导的方法在迭代过程中根据当前路径代价在搜索空间的椭球子集中采样状态。基于学习的替代方案通过建模搜索空间拓扑结构并推断接近最优路径的状态来引导规划。我们融合双方优势，提出基于点网络引导的神经 Informed RRT*。引入点网络推断引导状态，并将该网络集成至 Informed RRT* 中用于引导状态优化。采用 Neural Connect 构建引导状态集的连通性，进一步在复杂规划问题中提升性能。我们的方法在路径规划基准测试中超越先前工作，同时保持概率完备性与渐近最优性。我们展示了该方法在真实场景移动机器人导航中的部署应用。