Topological Motion Planning Diffusion: Generative Tangle-Free Path Planning for Tethered Robots in Obstacle-Rich Environments
Yifu Tian, Xinhang Xu, Thien-Minh Nguyen, Muqing Cao
IEEE International Conference on Robotics and Automation (ICRA), 2026(Under Review)
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In extreme environments, tethered robots require continuous navigation while avoiding cable entanglement. Traditional planners struggle in these lifelong planning scenarios due
to topological unawareness, while topology-augmented graphsearch methods face computational bottlenecks in obstaclerich 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. 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 AGX-Dynamics for unity further validates the practicality of the proposed
approach.