Mechanism Design and Trajectory Layering Optimization of a Hybrid Robot for Minimally Invasive Surgery

doi:10.3901/JME.260359

Abstract

Abstract: Traditional minimally invasive surgical robots employ a serial structure at the end, which generally suffer from weak stiffness, safety hazards due to potential failure of algorithm-implemented remote center of motion (RCM) constraints, and difficulty in coordinating mechanism performance with trajectory characteristics. A novel hybrid minimally invasive surgical robot system is proposed, consisting of a Franka serial manipulator and an autonomously designed distal center parallel mechanism. Regarding the RCM parallel mechanism, the degrees of freedom of the parallel RCM mechanism are analyzed using the screw theory, and the analytical kinematic inverse solution is derived through the closed-loop vector method and geometric constraint relationship. Furthermore, a motion/force transmission performance atlas within the workspace is constructed to quantify the transmission performance of the mechanism. To optimize the performance of surgical robots, a hierarchical optimization framework based on serial/parallel structure is proposed. Firstly, trajectory features are extracted from manually demonstrated trajectories through dynamic motion primitive teaching learning, and initial optimization paths are generated. Subsequently, guided by the transmission performance atlas, the reference path is replanned to the high-performance region of the parallel mechanism while ensuring its characteristics. Depending on the different surgical requirements, different optimization functions are employed for fine-tuning. Finally, the posture of the serial robotic arm is optimized and selected through inverse kinematics analysis and comprehensive evaluation of static performance. During the experimental verification phase, a minimally invasive abdominal surgery simulation platform is built, and small incision trajectory operation tests are successfully conducted on a biomimetic prosthesis. This study validates the feasibility of the proposed hybrid surgical robot for minimally invasive surgery, and achieves generalized learning and performance optimization of surgical trajectories through hierarchical optimization of serial/parallel mechanisms.

Key words: surgical robot, RCM parallel mechanism, performance evaluation, teaching demonstration learning, trajectory planning

CLC Number:

TP242

ZHANG Xin, SHI Donghao, CHEN Zhihan, XU Lingmin, LI Qinchuan. Mechanism Design and Trajectory Layering Optimization of a Hybrid Robot for Minimally Invasive Surgery[J]. Journal of Mechanical Engineering, 2026, 62(11): 201-214.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260359

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