The Open Motion Planning Library
Abstract
The open motion planning library (OMPL) is a new library for sampling-based motion planning, which contains implementations of many state-of-the-art planning algorithms. The library is designed in a way that it allows the user to easily solve a variety of complex motion planning problems with minimal input. OMPL facilitates the addition of new motion planning algorithms, and it can be conveniently interfaced with other software components. A simple graphical user interface (GUI) built on top of the library, a number of tutorials, demos, and programming assignments are designed to teach students about sampling-based motion planning. The library is also available for use through Robot Operating System (ROS).
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Sampling-based motion planning faces performance degradation with increasing occluders as the probability of valid sampling rapidly diminishes.
“Moreover, sampling-based motion planning [19, 35], a kind of commonly used approach for planning and manipulation, also faces performance degradation with increasing occluders [38] as the probability of valid sampling rapidly diminishes.”
From Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusions (2023) · cites this paper as [35]
Point-level affordance may be more suitable than sampling-based planners for manipulating articulated objects within complex constraints, because it directly provides per-point actionable priors.
“Comparisons between RTT-CA and affordance methods show that, while sampling-based planners may face difficulty in complicated scenes, point-level affordance may be more suitable by directly providing per-point actionable priors for manipulating articulated objects within complex constraints.”
From Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusions (2023) · cites this paper as [35]
Cites
Probabilistic roadmaps for path planning in high-dimensional configuration spaces
Kavraki, L. E., Svestka, P., Latombe, J.-C., & Overmars, M. H. (1996) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.508439 · Cited by 6,343 · cited 3× · see citing works
“This paper describes the Open Motion Planning Library (OMPL, http://ompl.kavrakilab.org), an open source C++ implementation of many sampling-based algorithms (such as the Probabilistic Roadmap Method (PRM) [14], Rapidly-expanding Random Trees (RRT) [15], Kinodynamic Planning by Interior-Exterior Cell Exploration (KPIE…”
MethodSelf-citationCyberbotics Ltd. Webots <sup>™</sup> : Professional Mobile Robot Simulation
Michel, O. (2004) · International Journal of Advanced Robotic Systems · doi:10.5772/5618 · Cited by 1,261 · see citing works
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The YCB object and model set: Towards common benchmarks for manipulation research
Calli, B., Singh, A., Walsman, A., Srinivasa, S., Abbeel, P., & Dollar, A. M. (2015) · 2015 International Conference on Advanced Robotics (ICAR) · doi:10.1109/icar.2015.7251504 · Cited by 838 · see citing works
CommonRoad: Composable benchmarks for motion planning on roads
Althoff, M., Koschi, M., & Manzinger, S. (2017) · 2017 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2017.7995802 · Cited by 443 · see citing works
Analysis and Observations From the First Amazon Picking Challenge
Correll, N., Bekris, K. E., Berenson, D., Brock, O., Causo, A., Hauser, K., Okada, K., Rodriguez, A., Romano, J. M., & Wurman, P. R. (2018) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2016.2600527 · Cited by 425 · see citing works
“Other tools mentioned by the teams include “trajopt” [22], ROS’ JT Cartesian Controller [23], and the OMPL library [24], which was interfaced through MoveIt! or stand-alone in the teams’ custom implementations.”
MethodRLBench: The Robot Learning Benchmark & Learning Environment
James, S., Ma, Z., Arrojo, D. R., & Davison, A. J. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2974707 · Cited by 405 · see citing works
Folding clothes autonomously: A complete pipeline
Doumanoglou, A., Stria, J., Peleka, G., Mariolis, I., Petrik, V., Kargakos, A., Wagner, L., Hlavac, V., Kim, T.-K., & Malassiotis, S. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2602376 · Cited by 122 · see citing works
Automatic assembly planning based on digital product descriptions
Sierla, S., Kyrki, V., Aarnio, P., & Vyatkin, V. (2018) · Computers in Industry · doi:10.1016/j.compind.2018.01.013 · Cited by 95 · see citing works
Open Source Integrated Planner for Autonomous Navigation in Highly Dynamic Environments
Darweesh, H., Takeuchi, E., Takeda, K., Ninomiya, Y., Sujiwo, A., Morales, L. Y., Akai, N., Tomizawa, T., Kato, S., Nagoya University Furo-cho, C.-K., et al. (2017) · Journal of Robotics and Mechatronics · doi:10.20965/jrm.2017.p0668 · Cited by 81 · see citing works
Chaffre, T., Moras, J., Chan-Hon-Tong, A., & Marzat, J. (2020) · Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics · doi:10.5220/0009821603140323 · Cited by 38 · see citing works
SkillMaN — A skill-based robotic manipulation framework based on perception and reasoning
Diab, M., Pomarlan, M., Beßler, D., Akbari, A., Rosell, J., Bateman, J., & Beetz, M. (2020) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2020.103653 · Cited by 23 · see citing works
Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusions
Wu, R., Cheng, K., Shen, Y., Ning, C., Zhan, G., & Dong, H. (2023) · arXiv · doi:10.48550/arxiv.2309.07510 · Cited by 11 · see citing works
“Moreover, sampling-based motion planning [19, 35], a kind of commonly used approach for planning and manipulation, also faces performance degradation with increasing occluders [38] as the probability of valid sampling rapidly diminishes.”
Goal-Conditioned Model Simplification for 1-D and 2-D Deformable Object Manipulation
Wang, S., Leonetti, M., & Dogar, M. (2025) · IEEE Transactions on Robotics · doi:10.1109/tro.2025.3577052 · Cited by 4 · see citing works
“We implement RRT from Open Motion Planning Library [59] (OMPL), and set the goal bias and error threshold to 0.5 and 1 e − 6 respectively.”
Influential
- Authors
- Sucan, I. A., Moll, M., & Kavraki, L. E.
- Affiliations
- Rice University (US)
- Venue
- IEEE Robotics & Automation Magazine
- Published
- 2012-12
- Volume
- 19
- Issue
- 4
- Pages
- 72-82
- ISSN
- 1070-9932 (ISSN-L)
- 1558-223X
- OpenAlex
- W2110762409
- Cited by
- 1,616
- References
- 2
- Citations by year
- 2026: 92 · 2025: 144 · 2024: 115 · 2023: 148 · 2022: 148 · 2021: 150 · 2020: 161 · 2019: 144 · 2018: 135 · 2017: 93 · 2016: 77 · 2015: 85 · 2014: 63 · 2013: 46 · 2012: 10
- FWCI
- 67.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Robotics & Automation Magazine (publishedVersion)
- Funders
- National Science Foundation
- Rice University
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotic Path Planning Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- sampling-based motion planning, motion planning algorithms, robot path planning, Robot Operating System, graphical user interface, educational robotics
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