SRMP: Search-Based Robot Motion Planning Library
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908566896508928 |
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| author | Mishani, Itamar Shaoul, Yorai Natarajan, Ramkumar Li, Jiaoyang Likhachev, Maxim |
| author_facet | Mishani, Itamar Shaoul, Yorai Natarajan, Ramkumar Li, Jiaoyang Likhachev, Maxim |
| contents | Motion planning is a critical component in any robotic system. Over the years, powerful tools like the Open Motion Planning Library (OMPL) have been developed, offering numerous motion planning algorithms. However, existing frameworks often struggle to deliver the level of predictability and repeatability demanded by high-stakes applications -- ranging from ensuring safety in industrial environments to the creation of high-quality motion datasets for robot learning. Complementing existing tools, we introduce SRMP (Search-based Robot Motion Planning), a new software framework tailored for robotic manipulation. SRMP distinguishes itself by generating consistent and reliable trajectories, and is the first software tool to offer motion planning algorithms for multi-robot manipulation tasks. SRMP easily integrates with major simulators, including MuJoCo, Sapien, Genesis, and PyBullet via a Python and C++ API. SRMP includes a dedicated MoveIt! plugin that enables immediate deployment on robot hardware and seamless integration with existing pipelines. Through extensive evaluations, we demonstrate in this paper that SRMP not only meets the rigorous demands of industrial and safety-critical applications but also sets a new standard for consistency in motion planning across diverse robotic systems. Visit srmp.readthedocs.io for SRMP documentation and tutorials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25352 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SRMP: Search-Based Robot Motion Planning Library Mishani, Itamar Shaoul, Yorai Natarajan, Ramkumar Li, Jiaoyang Likhachev, Maxim Robotics Motion planning is a critical component in any robotic system. Over the years, powerful tools like the Open Motion Planning Library (OMPL) have been developed, offering numerous motion planning algorithms. However, existing frameworks often struggle to deliver the level of predictability and repeatability demanded by high-stakes applications -- ranging from ensuring safety in industrial environments to the creation of high-quality motion datasets for robot learning. Complementing existing tools, we introduce SRMP (Search-based Robot Motion Planning), a new software framework tailored for robotic manipulation. SRMP distinguishes itself by generating consistent and reliable trajectories, and is the first software tool to offer motion planning algorithms for multi-robot manipulation tasks. SRMP easily integrates with major simulators, including MuJoCo, Sapien, Genesis, and PyBullet via a Python and C++ API. SRMP includes a dedicated MoveIt! plugin that enables immediate deployment on robot hardware and seamless integration with existing pipelines. Through extensive evaluations, we demonstrate in this paper that SRMP not only meets the rigorous demands of industrial and safety-critical applications but also sets a new standard for consistency in motion planning across diverse robotic systems. Visit srmp.readthedocs.io for SRMP documentation and tutorials. |
| title | SRMP: Search-Based Robot Motion Planning Library |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.25352 |