拓扑运动规划扩散：用于缆绳机器人在障碍密集环境中的生成无纠缠路径规划
Topological Motion Planning Diffusion: Generative Tangle-Free Path Planning for Tethered Robots in Obstacle-Rich Environments
AI总结：
本文提出TMPD框架，结合终身拓扑记忆，通过扩散模型生成可行轨迹候选，并利用拓扑后端过滤优化，实现无碰撞和无纠缠的高效路径规划。
摘要：
在极端环境如水下探索和灾后救援中，缆绳机器人需要持续导航并避免缆绳缠绕。传统规划器在这些终身规划场景中因拓扑 unawareness 而表现不佳，而拓扑增强的图搜索方法在障碍密集环境中面临计算瓶颈，因为候选拓扑类别的数量增加。为了解决这些挑战，我们提出了拓扑运动规划扩散（TMPD），一种新颖的生成规划框架，整合了终身拓扑记忆。不同于依赖序列图搜索，TMPD利用扩散模型提出跨各种同调类别的多模态前端的运动学可行轨迹候选。一个缆绳意识的拓扑后端然后通过计算广义环绕数来过滤和优化这些候选，以评估它们的拓扑能量相对于累积的缆绳配置。在障碍密集的模拟环境中基准测试显示，TMPD实现了100%的无碰撞可达性和97.0%的无纠缠率，在几何平滑度和计算效率方面均优于传统拓扑搜索和纯运动学扩散基线。与现实电缆动力学的模拟进一步验证了所提出方法的实用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
