发表机构
Delft University of Technology; Delft Center for Systems and Control（代尔夫特理工大学; 代尔夫特系统与控制中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对带松弛系绳的移动机器人，提出一种含三步流程的运动规划算法，可生成动态可行的无纠缠轨迹，经仿真验证能实现更安全可靠的导航。
AI 中文摘要
在系绳移动机器人的运动规划算法中，系绳的纠缠状态是规划阶段需考虑的关键方面，这在系绳处于松弛状态时尤为重要。此时，系绳的形状不仅由环境几何与障碍物位置决定，还受系绳动力学、机器人轨迹及外力影响。该场景下，防止纠缠需规划考虑纠缠定义、机器人与系绳动力学的机器人轨迹。本研究提出一种带松弛系绳的系绳移动机器人运动规划算法，可计算动态可行的无纠缠轨迹，以在含静态障碍物的环境中导航。通过在规划流程的所有阶段考虑纠缠状态，该算法能计算更安全的轨迹，避免机器人运动过程中发生纠缠。其实现依赖三步流程：（i）构建系绳机器人无纠缠构型空间的拓扑模型；（ii）利用该模型生成一组候选路径；（iii）通过求解同伦约束的轨迹生成问题，计算动态可行的无纠缠轨迹。生成的轨迹可引导机器人到达目标位置，同时保持系绳处于无纠缠构型。我们在仿真中验证了该算法的优势，结果表明其可避免违反纠缠约束，生成更安全、更可靠的轨迹。
英文摘要
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.
Comments19 pages, 13 figures
