AI 中文总结
本文提出RDG算法，一种基于网格分解的渐近近最优动力学运动规划方法，通过常数时间复杂度的快速迭代生成高质量解，并在10自由度环境下相比SST和DIRT显著提升解质量与成功率。
AI 中文摘要
本文开发了快速迭代动力学网格（RDG）算法，这是一种渐近近最优的动力学运动规划算法，通过快速迭代产生高质量解。该算法利用状态空间网格分解来执行节点选择、动力学传播和图修订，其时间复杂度相对于轨迹树中的节点数量为常数。通过覆盖球序列归纳证明，该算法被证明是渐近近最优且概率完备的。算法的不同子系统在生成探索偏差方面与常见的基于最近邻搜索的方法进行了评估。RDG算法在高达10自由度的复杂动力学运动规划问题环境中，通过与具有最优性保证的类似稀疏动力学规划算法SST和DIRT进行模拟试验评估。RDG算法在平均最终解质量上分别优于SST和DIRT高达104%和40%。此外，RDG保持了100%的成功率，即使在SST和DIRT均未成功的10自由度测试案例中也是如此。
英文摘要
This paper develops the Rapidly-iterating kinoDynamic Grid (RDG) algorithm, an asymptotically near-optimal kinodynamic motion planning algorithm that produces high quality solutions through rapid iteration. The algorithm leverages a state space grid decomposition to perform node selection, dynamics propagation, and graph revision in constant time complexity with respect to the number of nodes in the trajectory tree. Through a covering ball sequence induction proof, the algorithm is shown to be asymptotically near-optimal and probabilistically complete. Different subsystems of the algorithm are evaluated against common nearest-neighbor search-based methods at generating exploration bias. The RDG algorithm is evaluated through simulated trials in complex, kinodynamic motion planning problem environments up to 10 DOF relative to similar sparse, kinodynamic planning algorithms with optimality guarantees, SST and DIRT. The RDG algorithm outperforms both SST and DIRT in mean final solution quality by up to 104% and 40% respectively. Additionally, RDG maintained a 100% success rate, even on a 10-DOF test case where both SST and DIRT did not.
CommentsSubmitted to IEEE Transactions on Robotics
