COVER:COverage-VErified Roadmaps for Fixed-time Motion Planning in Continuous Semi-Static Environments
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911602028052480 |
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| author | Ilampooranan, Niranjan Kumar Chamzas, Constantinos |
| author_facet | Ilampooranan, Niranjan Kumar Chamzas, Constantinos |
| contents | The ability to solve motion-planning queries within a fixed time budget is critical for deploying robotic systems in time-sensitive applications. Semi-static environments, where most of the workspace remains fixed while a subset of obstacles varies between tasks, exhibit structured variability that can be exploited to provide stronger guarantees than general-purpose planners. However, existing approaches either lack formal coverage guarantees or rely on discretizations of obstacle configurations that restrict applicability to realistic domains.
This paper introduces COVER, a framework that incrementally constructs coverage-verified roadmaps for semi-static environments. COVER decomposes the arrangement space by independently partitioning the configuration space of each movable obstacle and verifies roadmap feasibility within each partition, enabling fixed-time query resolution for verified regions.We evaluate COVER on a 7-DoF manipulator performing object-picking in tabletop and shelf environments, demonstrating broader problem-space coverage and higher query success rates than prior work, particularly with obstacles of different sizes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03875 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | COVER:COverage-VErified Roadmaps for Fixed-time Motion Planning in Continuous Semi-Static Environments Ilampooranan, Niranjan Kumar Chamzas, Constantinos Robotics The ability to solve motion-planning queries within a fixed time budget is critical for deploying robotic systems in time-sensitive applications. Semi-static environments, where most of the workspace remains fixed while a subset of obstacles varies between tasks, exhibit structured variability that can be exploited to provide stronger guarantees than general-purpose planners. However, existing approaches either lack formal coverage guarantees or rely on discretizations of obstacle configurations that restrict applicability to realistic domains. This paper introduces COVER, a framework that incrementally constructs coverage-verified roadmaps for semi-static environments. COVER decomposes the arrangement space by independently partitioning the configuration space of each movable obstacle and verifies roadmap feasibility within each partition, enabling fixed-time query resolution for verified regions.We evaluate COVER on a 7-DoF manipulator performing object-picking in tabletop and shelf environments, demonstrating broader problem-space coverage and higher query success rates than prior work, particularly with obstacles of different sizes. |
| title | COVER:COverage-VErified Roadmaps for Fixed-time Motion Planning in Continuous Semi-Static Environments |
| topic | Robotics |
| url | https://arxiv.org/abs/2510.03875 |