论文标题
避免在线轨迹优化连续操作器的奇异性
Singularity Avoidance with Application to Online Trajectory Optimization for Serial Manipulators
论文作者
论文摘要
这项工作提出了一种基于已知奇异配置的实时轨迹优化的新型奇异性避免方法。这项工作的重点在于分析具有不同运动学结构的三个机器人的运动学奇异配置，即Comau Racer 7-1.4，Kuka LBR IIWA R820和Franka Emika Panda，以及以量身定制的功能来避免单数。对所提出的方法和常用的可操作性最大化方法进行了蒙特卡洛模拟以进行比较。数值结果表明，可以减少平均计算时间，并且通过建议的方法获得时间和路径长度的较短轨迹
This work proposes a novel singularity avoidance approach for real-time trajectory optimization based on known singular configurations. The focus of this work lies on analyzing kinematically singular configurations for three robots with different kinematic structures, i.e., the Comau Racer 7-1.4, the KUKA LBR iiwa R820, and the Franka Emika Panda, and exploiting these configurations in form of tailored potential functions for singularity avoidance. Monte Carlo simulations of the proposed method and the commonly used manipulability maximization approach are performed for comparison. The numerical results show that the average computing time can be reduced and shorter trajectories in both time and path length are obtained with the proposed approach
