发表机构
State Key Laboratory of Robotics and Systems, Harbin Institute of Technology; School of Mechanical and Aerospace Engineering, Nanyang Technological University（哈尔滨工业大学机器人技术与系统国家重点实验室; 南洋理工大学机械与航天工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对凸集图（GCS）规划中起始与目标区域断开的问题，提出GCS-Bridging方法，通过无碰撞点路径与凸集膨胀恢复连通性，模拟与硬件实验验证其高成功率与实用性。
AI 中文摘要
基于凸集图（Graph-of-Convex-Sets，GCS）的轨迹优化将构型空间中的无碰撞区域表示为有限个凸集的集合，并直接在这些集合上执行无碰撞轨迹规划，大幅简化了规划过程。然而，现有的基于GCS的轨迹规划方法通常假设凸区域之间具有足够的连通性，并未明确处理初始GCS地图中起始区域和目标区域属于不同连通分量的情况。为解决这一局限，我们提出了GCS-Bridging方法，该方法通过无碰撞点路径随后进行凸集膨胀，重新连接断开的凸区域，从而恢复原本断开的GCS规划问题的可行性。在多个与IRIS相关的算法和场景中进行的大量模拟表明，GCS-Bridging可在初始GCS地图中恢复缺失的起始-目标连通性，成功率达99.8%。此外，在初始起始和目标区域断开的真实场景中，针对单臂Franka平台开展的硬件实验验证了该方法在实际运动规划中的有效性。项目网站：this https URL
英文摘要
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/
Comments8 pages, 3 figures
