GCS-Bridging: 凸集合の非連結性を回復するグラフ・オブ・凸集合運動計画
GCS-Bridging: Restoring Connectivity of Disconnected Convex Sets for Graph-of-Convex-Sets Motion Planning
凸集合のグラフ（GCS）に基づく運動計画で、開始・目標領域が異なる連結成分にある場合に、衝突回避経路と凸領域の膨張で連結性を回復する手法を提案。シミュレーションで99.8%の成功率、実機でも有効性を確認。
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著者: Xiaokai Zhou, Baoshi Cao, Yang Liu, Kui Sun, Boyu Ma, Zhengpu Wang, Zongwu Xie
分類: cs.RO
原文アブストラクト
Graph-of-Convex-Sets (GCS)-based trajectory optimization represents collision-free regions in configuration space as a finite collection of convex sets and directly performs collision-free trajectory planning over these sets, substantially simplifying the planning process. However, existing GCS-based trajectory planning methods generally assume sufficient connectivity among the convex regions and do not explicitly address cases in which the start and goal regions belong to different connected components of the initial GCS map. To address this limitation, we propose GCS-Bridging, which reconnects disconnected convex regions through collision-free point paths followed by convex region inflation, thereby recovering the feasibility of otherwise disconnected GCS planning problems. Extensive simulations across multiple IRIS-related algorithms and scenarios demonstrate that GCS-Bridging restores missing start-to-goal connectivity in the initial GCS map with a 99.8% success rate. In addition, a hardware experiment on a single-arm Franka platform in a real-world scenario with initially disconnected start and goal regions validates the effectiveness of the proposed method in practical motion planning. Project website: https://zhouxk1997.github.io/GCS_Bridging/