Probabilistic completeness of RRT for geometric and kinodynamic planning with forward propagation
Michal Kleinbort, Kiril Solovey, Zakary Littlefield, Kostas E. Bekris, and Dan Halperin

TL;DR
This paper provides simplified proofs of the probabilistic completeness of RRT algorithms for geometric and kinodynamic planning, establishing foundational guarantees under mild assumptions and correcting previous errors in the analysis.
Contribution
It offers new, less assumption-dependent proofs of RRT's probabilistic completeness and rectifies a prior proof error in kinodynamic cases, strengthening theoretical foundations.
Findings
Proof of probabilistic completeness for geometric RRT with obstacle clearance
Proof of probabilistic completeness for kinodynamic RRT with Lipschitz conditions
Correction of previous proof error using increasing radii in the analysis
Abstract
The Rapidly-exploring Random Tree (RRT) algorithm has been one of the most prevalent and popular motion-planning techniques for two decades now. Surprisingly, in spite of its centrality, there has been an active debate under which conditions RRT is probabilistically complete. We provide two new proofs of probabilistic completeness (PC) of RRT with a reduced set of assumptions. The first one for the purely geometric setting, where we only require that the solution path has a certain clearance from the obstacles. For the kinodynamic case with forward propagation of random controls and duration, we only consider in addition mild Lipschitz-continuity conditions. These proofs fill a gap in the study of RRT itself. They also lay sound foundations for a variety of more recent and alternative sampling-based methods, whose PC property relies on that of RRT. Our original publication contains an…
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Taxonomy
TopicsRobotic Path Planning Algorithms · AI-based Problem Solving and Planning · Robotics and Sensor-Based Localization
