为带松动缆绳的移动机器人设计无缠绕路径规划算法
Entanglement-Free Trajectory Planning for Tethered Mobile Robots with a Slack Tether

- 构建缆绳无缠绕配置空间的拓扑模型
- 生成满足动态可行性的无缠绕路径,避免缆绳打结
- 适合需安全拖拽的机器人应用,如救援或深海探测
在带缆绳移动机器人的运动规划中,缆绳的缠绕状态是规划阶段必须考虑的关键因素。尤其当缆绳松弛时,其形状不仅取决于环境几何和障碍物位置,还受缆绳动力学、机器人轨迹及外部力影响。为此,本文提出一种针对松弛缆绳的运动规划算法,可计算出动态可行且无缠绕的轨迹,使机器人在静态障碍环境中安全通行。算法采用三步流程:(i)构建缆绳无缠绕配置空间的拓扑模型;(ii)基于该模型生成候选路径;(iii)通过求解同伦约束轨迹生成问题,计算出动态可行的无缠绕轨迹。仿真结果表明,该算法能有效避免缠绕约束违反,提升轨迹安全性与可靠性。
原文摘要 · Abstract (English)
In motion planning algorithms for tethered mobile robots, the entanglement state of the tether is a critical aspect to consider during the planning phase. This is particularly important in case of a slack tether, where the shape of the tether is not determined solely by the geometry of the environment and the location of the obstacles, but also by the dynamics of the tether, by the trajectory followed by the robot, and possibly by exogenous forces. In this scenario, preventing entanglement requires planning a robot trajectory that accounts for the entanglement definition and for the dynamics of the robot and of the tether. In this work, we propose a motion planning algorithm for tethered mobile robots with a slack tether that computes dynamically feasible entanglement-free trajectories to navigate through an environment with static obstacles. By considering the entanglement state during all the stages of the planning pipeline, we are able to compute safer trajectories that avoid entanglement during the motion of the robot. We achieve this through a three-step pipeline, which includes (i) the construction of a topological model of the entanglement-free configuration space of the tethered robot, (ii) the generation of a set of candidate paths using this model, and (iii) the computation of a dynamically feasible entanglement-free trajectory by solving a homotopy-constrained trajectory generation problem. The resulting trajectory can then be executed to lead the robot to its target location, while maintaining the tether in an entanglement-free configuration. We demonstrate the benefits of this algorithm in simulations, where we show how the planning algorithm avoids violations of the entanglement constraints, resulting in safer and more reliable trajectories.
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