The aim of coordinated planning is to avoid robot-to-robot collisions in a multi-robot system, and there are two standard solution approaches: centralized planning and decoupled planning. Our first contribution is a decoupled planning approach that ensures C2-continuous control commands with zero velocities at the start and goal. We benchmark our decoupled approach with a centralized approach. Contrary to literature, we show that for a standard motion planning pipeline, such as the one used by MoveIt!, centralized planning is superior to decoupled planning in dual-arm manipulation: It has a lower computation time and a higher robustness. Our second contribution is an optimization that minimizes the rotational motion of an end-effector while considering obstacle avoidance. We derive the analytic gradients of this optimization problem, making the algorithm suitable for online motion planning. Our optimization extends an existing path quality improvement method. Integrating it into our decoupled approach overcomes its shortcomings and provides a motion planning pipeline that is robust at up to 99.9% with a planning time of less than 1s and that computes high-quality paths.

翻译：协调规划的目标是在多机器人系统中避免机器人间的碰撞，目前存在两种标准解决方案：集中式规划和解耦式规划。我们的第一个贡献在于提出一种解耦规划方法，该方法能确保在起点和目标点速度为零的情况下实现C2连续的控制指令。我们将该解耦方法与集中式方法进行了基准对比。与文献观点相反，我们证明对于如MoveIt!所采用的标准运动规划流程，在双臂操作中集中式规划优于解耦式规划：其计算时间更短且鲁棒性更高。第二个贡献是提出一种优化方法，在考虑避障的同时最小化末端执行器的旋转运动。我们推导出该优化问题的解析梯度，使算法适用于在线运动规划。该优化扩展了现有路径质量改进方法，通过将其集成到解耦方法中，克服了后者的缺陷，构建出一种鲁棒性高达99.9%、规划时间低于1秒且能生成高质量路径的运动规划流程。