In this paper, an improved recurrent neural network (RNN) scheme is proposed to perform the trajectory control of redundant robot manipulators using remote center of motion (RCM) constraints. Firstly, learning by demonstration is implemented to model the surgical operation skills in the Cartesian space. After that, considering the kinematic constraints associated with the optimization control of redundant manipulators, we propose a novel RNN-based approach to facilitate accurate task tracking based on the general quadratic performance index, which includes managing the constraints on RCM joint angle, and joint velocity, simultaneously. The results of the conducted theoretical analysis confirm that the RCM constraint has been established successfully, and accordingly. The corresponding end-effector tracking errors asymptotically converge to zero. Finally, demonstration experiments are conducted in a laboratory setup environment using KUKA LWR4+ to validate the effectiveness of the proposed control strategy.

本文提出了一种改进的递归神经网络（RNN）方案，以利用远程运动中心（RCM）约束来执行冗余机器人操纵器的轨迹控制。首先，通过示范学习来对笛卡尔空间中的手术操作技能进行建模。此后，考虑到与冗余机械手的优化控制相关的运动学约束，我们提出了一种基于RNN的新颖方法，以基于常规二次性能指标促进精确的任务跟踪，其中包括管理RCM关节角度和关节速度的约束， 同时。进行的理论分析结果证实，RCM约束已成功建立，因此。相应的末端执行器跟踪误差渐近收敛于零。