Achieving Asymptotic Near-Optimality Without $\delta$-Similarity

Abstract:Sampling-based motion planning algorithms are a popular class of trajectory planning algorithm due to their speed in complex, high-dimensional environments and ability to handle kinodynamic constraints, specifically through the use of forward dynamics propagation. Many such planners claim to achieve asymptotic near-optimality by proving the almost sure sampling of trajectories that are close to an optimal trajectory in the state space, known as $\delta$-similar trajectories. This paper shows that the proof behind asymptotic $\delta$-similarity relies on an unstated assumption that $\delta$-similar trajectory segments will always be kept once sampled. This assumption does not hold in general. A problematic case, referred to as ``crowding out,'' is described, where locally low-cost paths prevent trajectories that are $\delta$-similar to the optimal trajectory from being added to the tree. It is shown, however, that asymptotic near-optimality guarantees can still be achieved without guarantees of $\delta$-similar solution trajectories when crowding out is properly accounted for. An example environment and system are provided where crowding out is shown to occur, demonstrating a scenario where inductively sampling a $\delta$-similar solution trajectory is impossible.

Robotics (cs.RO)

arXiv:2609.04464 [cs.RO]

(or arXiv:2609.04464v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.04464 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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