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

翻译：本文提出了一种基于已知奇异构型的实时轨迹优化奇异规避新方法。研究重点在于分析三种具有不同运动学结构的机械臂——Comau Racer 7-1.4、KUKA LBR iiwa R820与Franka Emika Panda——的运动学奇异构型，并利用这些构型构建定制化势函数以实现奇异规避。通过蒙特卡洛仿真，将所提方法与常用的可操作度最大化方法进行对比分析。数值结果表明，所提方法在降低平均计算时间的同时，可获得时间与路径长度均更短的优化轨迹。