たるみのあるテザーを持つ移動ロボットのための絡まりのない軌道計画
Entanglement-Free Trajectory Planning for Tethered Mobile Robots with a Slack Tether
たるみのあるテザーを持つ移動ロボットが、静的な障害物環境で絡まりを回避しながら動的に実行可能な軌道を計画するアルゴリズムを提案した。トポロジカルモデルとホモトピー制約を用いた3段階パイプラインで安全な軌道を生成する。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Gianpietro Battocletti, Dimitris Boskos, Bart De Schutter
分類: cs.RO
原文アブストラクト
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.
