# An Extensible Benchmarking Infrastructure for Motion Planning Algorithms

arXiv: https://arxiv.org/abs/1412.6673 · HTML: https://mlsanity.com/paper/1412.6673/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2014.

## Built on by (most cited first, 9 of 9)

- [RobotArena ∞: Scalable Robot Benchmarking via Real-to-Sim Translation](https://mlsanity.com/paper/2510.23571/) (2025)
- [Correct-by-Construction Runtime Enforcement in AI -- A Survey](https://mlsanity.com/paper/2208.14426/) (2022)
- [PathBench: A Benchmarking Platform for Classical and Learned Path Planning Algorithms](https://mlsanity.com/paper/2105.01777/) (2021)
- [Arena 4.0: A Comprehensive ROS2 Development and Benchmarking Platform for Human-centric Navigation Using Generative-Model-based Environment Generation](https://mlsanity.com/paper/2409.12471/) (2024)
- [Arena-Rosnav 2.0: A Development and Benchmarking Platform for Robot Navigation in Highly Dynamic Environments](https://mlsanity.com/paper/2302.10023/) (2023)
- [Robot Active Neural Sensing and Planning in Unknown Cluttered Environments](https://mlsanity.com/paper/2208.11079/) (2022)
- [Systematic Comparison of Path Planning Algorithms using PathBench](https://mlsanity.com/paper/2203.03092/) (2022)
- [RoboArm-NMP: a Learning Environment for Neural Motion Planning](https://mlsanity.com/paper/2405.16335/) (2024)
- [Bridging the Evaluation Gap: Standardized Benchmarks for Multi-Objective Search](https://mlsanity.com/paper/2603.24084/) (2026)

## Abstract (arXiv)

Sampling-based planning algorithms are the most common probabilistically complete algorithms and are widely used on many robot platforms. Within this class of algorithms, many variants have been proposed over the last 20 years, yet there is still no characterization of which algorithms are well-suited for which classes of problems. This has motivated us to develop a benchmarking infrastructure for motion planning algorithms. It consists of three main components. First, we have created an extensive benchmarking software framework that is included with the Open Motion Planning Library (OMPL), a C++ library that contains implementations of many sampling-based algorithms. Second, we have defined extensible formats for storing benchmark results. The formats are fairly straightforward so that other planning libraries could easily produce compatible output. Finally, we have created an interactive, versatile visualization tool for compact presentation of collected benchmark data. The tool and underlying database facilitate the analysis of performance across benchmark problems and planners.