For dynamic collision-free trajectory planning in dual-robot and human collaborative tasks, this paper develops an online dual-robot Mutual Collision Avoidance (MCA) scheme based on convex optimization. A novel convex optimization formulation model, named Disciplined Convex programming by Shifting reference paths (DCS), is proposed for solving the single-robot trajectory optimization problem. Furthermore, a new dual-robot trajectory convex optimization algorithm is presented for online adjustment of the dual-robot trajectories according to the collaborative task priority. The overall pipeline, named DCS-MCA, generates collision-free and time-optimal dual-robot trajectories, while prioritizing the task accessibility of the high-priority robot. Simulation experiments demonstrate that DCS exhibits comparable performance to the current state-of-the-art single-robot motion planner, while the DCS-MCA outperforms common algorithms by up to 30% in time optimality for dual-robot collaborative tasks. The feasibility and dynamic performance of the proposed approach are further validated in a real collaborative cell, illustrating its suitability for collaborative dual-robot tasks in moderately dynamic environments.

中文翻译：

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通过凸优化在线生成双机器人-人类协作轨迹


针对双机器人与人类协作任务中的动态无碰撞轨迹规划，本文开发了一种基于凸优化的在线双机器人相互碰撞避免（MCA）方案。提出了一种新颖的凸优化公式模型，称为通过移动参考路径（DCS）的纪律凸规划来解决单机器人轨迹优化问题。此外，提出了一种新的双机器人轨迹凸优化算法，用于根据协作任务优先级在线调整双机器人轨迹。整个管道名为 DCS-MCA，生成无碰撞且时间最优的双机器人轨迹，同时优先考虑高优先级机器人的任务可访问性。仿真实验表明，DCS 的性能与当前最先进的单机器人运动规划器相当，而 DCS-MCA 在双机器人协作任务的时间最优性方面比常见算法高出 30%。该方法的可行性和动态性能在真实的协作单元中得到了进一步验证，说明其适用于中等动态环境中的协作双机器人任务。