发表机构
University of Hong Kong; Tsinghua University（香港大学; 清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于KC-SVSDF的移动操作臂实时全身运动规划框架，通过链分解碰撞检查等方法，可可靠在复杂环境中运输大型非凸任意形状负载。
AI 中文摘要
移动操作臂日益被要求在杂乱环境中运输大型非凸负载，但现有规划器要么过度简化负载几何，要么无法处理操作臂连杆间的运动学耦合，导致可行空间丢失或优化停滞。本文提出一种用于携带任意形状负载的移动操作臂的实时全身运动规划框架。前端采用链分解的基于核的碰撞检查，保留机器人和负载的真实几何，存储紧凑且位级查询快速。中端预处理阶段将前端路径转换为连续轨迹，确保平滑性和可行性，当无碰撞时直接执行以绕过代价高昂的后端。当需要优化时，后端执行基于运动学耦合SVSDF（KC-SVSDF）的轨迹优化，该方法沿运动学链传播避障梯度以生成一致的全身逃逸方向。消融研究、与最先进基线的对比基准以及在差速驱动移动操作臂上的真实世界实验表明，所提框架可可靠地在狭窄通道和杂乱环境中运输大型非凸负载。
英文摘要
Mobile manipulators are increasingly tasked with transporting large, non-convex payloads through cluttered environments, yet existing planners either oversimplify the payload geometry or fail to handle the kinematic coupling between manipulator links, leading to lost feasible space or stalled optimization. This letter presents a real-time whole-body motion planning framework for mobile manipulators carrying arbitrarily shaped payloads. The front-end employs a chain-decomposed kernel-based collision check that preserves the true geometry of the robot and payload, with compact storage and fast bit-level queries. A mid-end preprocessing stage converts the front-end path into a continuous trajectory enforcing smoothness and feasibility, and executes it directly when collision-free to bypass the costly back-end. When refinement is required, the back-end performs trajectory optimization built on a Kinematically-Coupled SVSDF (KC-SVSDF), which propagates collision-avoidance gradients along the kinematic chain to produce coherent whole-body escape directions. Ablation studies, comparative benchmarks against state-of-the-art baselines, and real-world experiments on a differential-drive mobile manipulator demonstrate that the proposed framework reliably transports large, non-convex payloads through tight passages and cluttered environments.
