发表机构
University of Zagreb; Faculty of Electrical Engineering and Computing（萨格勒布大学; 电气工程与计算机学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对双臂操作的相对变换硬约束问题，提出主从参数化的快速规划流水线，仿真中规划速度提升19.4倍，实际实验验证轨迹可直接迁移至硬件。
AI 中文摘要
双臂操作可完成复杂任务，但因涉及大量自由度而增加了复杂度。处理刚性物体时，两个末端执行器之间的相对变换必须在整个运动过程中保持固定，这表现为非线性等式约束，将可行构型空间限制为测度零流形，对传统运动规划器构成挑战。我们提出一种快速双臂运动规划流水线，通过主从参数化在整条路径上持续强制执行该变换硬约束：将主臂构型视为自由变量，从臂构型则通过逆运动学计算以满足约束。我们在仿真中针对不同环境、约束和双臂平台对该方法进行了广泛评估，实现了比现有工作快19.4倍的规划速度，同时保证了约束的持续满足。在双臂Kinova Gen3装置上开展的实际实验，涉及托盘运输和细长物体操作，验证了规划轨迹可直接迁移至物理硬件。
英文摘要
Bimanual manipulation enables complex tasks but introduces added complexity from the high number of degrees of freedom involved. When handling rigid objects, the relative transformation between the two end effectors must remain fixed throughout the motion, manifesting as a nonlinear equality constraint that confines the feasible configuration space to a measure-zero manifold and challenges conventional motion planners. We propose a fast bimanual motion planning pipeline that enforces this hard transformation constraint continuously along the entire path, using a leader-follower parameterization: the leader's configuration is treated as a free variable, while the follower's is determined via inverse kinematics to satisfy the constraint. We extensively evaluate the method in simulation across diverse environments, constraints and bimanual platforms, achieving 19.4x faster planning than prior work while guaranteeing continuous constraint satisfaction. Real-world experiments on a bimanual Kinova Gen3 setup, involving tray transport and elongated-object manipulation, validate direct transfer of planned trajectories to physical hardware.
