# Search-Based Robot Motion Planning With Distance-Based Adaptive Motion Primitives

arXiv: https://arxiv.org/abs/2507.01198 · HTML: https://mlsanity.com/paper/2507.01198/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 1 Jul 2025.

## Builds on (most cited first, 4 of 4)

- [Sampling-Based Motion Planning: A Comparative Review](https://mlsanity.com/paper/2309.13119/) (2023)
- [Multi-Resolution A*](https://mlsanity.com/paper/2004.06684/) (2020)
- [A Multi-Heuristic Search-based Motion Planning for Automated Parking](https://mlsanity.com/paper/2307.07857/) (2023)
- [Search-based versus Sampling-based Robot Motion Planning: A Comparative Study](https://mlsanity.com/paper/2406.09623/) (2024)

## Built on by (most cited first, 1 of 1)

- [Efficient Video Diffusion Models: Advancements and Challenges](https://mlsanity.com/paper/2604.15911/) (2026)

## Abstract (arXiv)

This work proposes a motion planning algorithm for robotic manipulators that combines sampling-based and search-based planning methods. The core contribution of the proposed approach is the usage of burs of free configuration space (C-space) as adaptive motion primitives within the graph search algorithm. Due to their feature to adaptively expand in free C-space, burs enable more efficient exploration of the configuration space compared to fixed-sized motion primitives, significantly reducing the time to find a valid path and the number of required expansions. The algorithm is implemented within the existing SMPL (Search-Based Motion Planning Library) library and evaluated through a series of different scenarios involving manipulators with varying number of degrees-of-freedom (DoF) and environment complexity. Results demonstrate that the bur-based approach outperforms fixed-primitive planning in complex scenarios, particularly for high DoF manipulators, while achieving comparable performance in simpler scenarios.