Topological Motion Planning Diffusion: Generative Tangle-Free Path Planning for Tethered Robots in Obstacle-Rich Environments
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918413464502272 |
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| author | Tian, Yifu Xu, Xinhang Nguyen, Thien-Minh Cao, Muqing |
| author_facet | Tian, Yifu Xu, Xinhang Nguyen, Thien-Minh Cao, Muqing |
| contents | In extreme environments such as underwater exploration and post-disaster rescue, tethered robots require continuous navigation while avoiding cable entanglement. Traditional planners struggle in these lifelong planning scenarios due to topological unawareness, while topology-augmented graph-search methods face computational bottlenecks in obstacle-rich environments where the number of candidate topological classes increases. To address these challenges, we propose Topological Motion Planning Diffusion (TMPD), a novel generative planning framework that integrates lifelong topological memory. Instead of relying on sequential graph search, TMPD leverages a diffusion model to propose a multimodal front-end of kinematically feasible trajectory candidates across various homotopy classes. A tether-aware topological back-end then filters and optimizes these candidates by computing generalized winding numbers to evaluate their topological energy against the accumulated tether configuration. Benchmarking in obstacle-rich simulated environments demonstrates that TMPD achieves a collision-free reach of 100% and a tangle-free rate of 97.0%, outperforming traditional topological search and purely kinematic diffusion baselines in both geometric smoothness and computational efficiency. Simulation with realistic cable dynamics further validates the practicality of the proposed approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_26696 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Topological Motion Planning Diffusion: Generative Tangle-Free Path Planning for Tethered Robots in Obstacle-Rich Environments Tian, Yifu Xu, Xinhang Nguyen, Thien-Minh Cao, Muqing Robotics In extreme environments such as underwater exploration and post-disaster rescue, tethered robots require continuous navigation while avoiding cable entanglement. Traditional planners struggle in these lifelong planning scenarios due to topological unawareness, while topology-augmented graph-search methods face computational bottlenecks in obstacle-rich environments where the number of candidate topological classes increases. To address these challenges, we propose Topological Motion Planning Diffusion (TMPD), a novel generative planning framework that integrates lifelong topological memory. Instead of relying on sequential graph search, TMPD leverages a diffusion model to propose a multimodal front-end of kinematically feasible trajectory candidates across various homotopy classes. A tether-aware topological back-end then filters and optimizes these candidates by computing generalized winding numbers to evaluate their topological energy against the accumulated tether configuration. Benchmarking in obstacle-rich simulated environments demonstrates that TMPD achieves a collision-free reach of 100% and a tangle-free rate of 97.0%, outperforming traditional topological search and purely kinematic diffusion baselines in both geometric smoothness and computational efficiency. Simulation with realistic cable dynamics further validates the practicality of the proposed approach. |
| title | Topological Motion Planning Diffusion: Generative Tangle-Free Path Planning for Tethered Robots in Obstacle-Rich Environments |
| topic | Robotics |
| url | https://arxiv.org/abs/2603.26696 |