# Asymptotically Optimal Sampling-based Planners

arXiv: https://arxiv.org/abs/1911.04044 · HTML: https://mlsanity.com/paper/1911.04044/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [A Survey on the Integration of Machine Learning with Sampling-based Motion Planning](https://mlsanity.com/paper/2211.08368/) (2022)
- [CLAMGen: Closed-Loop Arm Motion Generation via Multi-view Vision-Based RL](https://mlsanity.com/paper/2103.13267/) (2021)

## Abstract (arXiv)

An asymptotically optimal sampling-based planner employs sampling to solve robot motion planning problems and returns paths with a cost that converges to the optimal solution cost, as the number of samples approaches infinity. This comprehensive article covers the theoretical characteristics of asymptotic optimality of motion planning algorithms, and traces its origins, analysis models, practical performance, extensions, and applications.