OPTIMIZATION OF END TRAJECTORY OF MEDICAL ROBOT ARM BASED ON HUMAN BIOMIMETICS

Abstract

The end effector of a medical robotic arm based on human bionics is responsible for surgical operations, and reasonable end effector trajectory operation is significant to robots. A deep learning method based on Faster R-CNN network is proposed for optimizing the end trajectory of a seven-degrees-of-freedom biomimetic medical robotic arm. In this method, RPN replaces conventional search in deep learning, and RoI Pooling completes network partitioning, thereby improving the efficiency of deep learning. The experimental results show that, with four data types as inputs: velocity, angular velocity, acceleration, and angular acceleration, the Faster R-CNN network achieves effective optimization results. The end trajectory of the seven-degrees-of-freedom biomimetic medical robotic arm becomes smooth, and the impact force during the speed reversal process is significantly reduced.