Problem
Research questions and friction points this paper is trying to address.
kinodynamic motion planning
near-optimal solutions
rapid iteration
high DOF
Rapidly-Iterating Grid-Based Near-Optimal Kinodynamic Motion Planning
🤖 AI Summary
本文提出了一种快速迭代网格动态(RDG)算法，通过状态空间网格分解实现近似最优解，解决了复杂环境下的运动规划问题，比现有方法SST和DIRT表现更优。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Rapidly-iterating kinoDynamic Grid (RDG) algorithm
asymptotically near-optimal
constant time complexity
covering ball sequence induction proof
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