# Sampling-Based Motion Planning: A Comparative Review

arXiv: https://arxiv.org/abs/2309.13119 · HTML: https://mlsanity.com/paper/2309.13119/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2023.

## Built on by (most cited first, 21 of 21)

- [SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End Autonomous Driving](https://mlsanity.com/paper/2404.06892/) (2024)
- [A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches](https://mlsanity.com/paper/2404.02817/) (2024)
- [Generalizing Multi-Objective Search via Objective-Aggregation Functions](https://mlsanity.com/paper/2509.22085/) (2025)
- [Global Tensor Motion Planning](https://mlsanity.com/paper/2411.19393/) (2024)
- [Diffusion-based Planning with Learned Viability Filters](https://mlsanity.com/paper/2502.19564/) (2025)
- [X-Safe: Embodiment-Agnostic Safeguarding for Robot Arm Manipulation Policies via Action Masking](https://mlsanity.com/paper/2606.22278/) (2026)
- [CoverLib: Classifiers-equipped Experience Library by Iterative Problem Distribution Coverage Maximization for Domain-tuned Motion Planning](https://mlsanity.com/paper/2405.02968/) (2024)
- [LLM-guided Task and Motion Planning using Knowledge-based Reasoning](https://mlsanity.com/paper/2412.07493/) (2024)
- [Certified Guidance for Planning with Deep Generative Models](https://mlsanity.com/paper/2501.12815/) (2025)
- [Search-Based Robot Motion Planning With Distance-Based Adaptive Motion Primitives](https://mlsanity.com/paper/2507.01198/) (2025)
- [ACDC: Adaptive Curriculum Planning with Dynamic Contrastive Control for Goal-Conditioned Reinforcement Learning in Robotic Manipulation](https://mlsanity.com/paper/2603.02104/) (2026)
- [Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunities](https://mlsanity.com/paper/2603.24318/) (2026)
- [ActivePusher: Active Learning and Planning with Residual Physics for Nonprehensile Manipulation](https://mlsanity.com/paper/2506.04646/) (2025)
- [Real-Time Adaptive Motion Planning via Point Cloud-Guided, Energy-Based Diffusion and Potential Fields](https://mlsanity.com/paper/2507.09383/) (2025)
- [Obstacle Avoidance using Dynamic Movement Primitives and Reinforcement Learning](https://mlsanity.com/paper/2510.09254/) (2025)
- [Multi Graph Search for High-Dimensional Robot Motion Planning](https://mlsanity.com/paper/2602.12096/) (2026)
- [Learning Motion Feasibility from Point Clouds in Cluttered Environments](https://mlsanity.com/paper/2606.26700/) (2026)
- [Conflict-Based Lazy Search for Fast Multi-Manipulator Planning](https://mlsanity.com/paper/2607.04124/) (2026)
- [Interventional Causal Circuits for Safe Robot Action Testing and Failure Recovery](https://mlsanity.com/paper/2607.14826/) (2026)
- [Learning-Based Motion Planning for Dynamic Environments: From Foundational Algorithms to Emerging Paradigms](https://mlsanity.com/paper/2608.00625/) (2026)
- [Leveraging Inter-object Affordances for Efficient Planning in Contact-rich Tasks](https://mlsanity.com/paper/2608.25641/) (2026)

## Abstract (arXiv)

Sampling-based motion planning is one of the fundamental paradigms to generate robot motions, and a cornerstone of robotics research. This comparative review provides an up-to-date guideline and reference manual for the use of sampling-based motion planning algorithms. This includes a history of motion planning, an overview about the most successful planners, and a discussion on their properties. It is also shown how planners can handle special cases and how extensions of motion planning can be accommodated. To put sampling-based motion planning into a larger context, a discussion of alternative motion generation frameworks is presented which highlights their respective differences to sampling-based motion planning. Finally, a set of sampling-based motion planners are compared on 24 challenging planning problems. This evaluation gives insights into which planners perform well in which situations and where future research would be required. This comparative review thereby provides not only a useful reference manual for researchers in the field, but also a guideline for practitioners to make informed algorithmic decisions.