A PSO-Optimized Fuzzy Reinforcement Learning Method for Making the Minimally Invasive Surgical Arm Cleverer

Abstract

For robotic-assisted surgery, the preoperative preparation procedure within which the surgical arms need to be adjusted manually to their expected configuration can pose interlocking problems in terms of accuracy, system robustness, and human-robot interaction experience. Previous work about variable impedance/admittance control methods did improve the smoothness of this procedure while individual characteristics of different operating personnel haven't yet been adequately considered. To further improve this process on the basis of existing methods, a novel strategy based on fuzzy Sarsa(λ)-learning algorithm is both proposed and incorporated into the virtual parameter adjustment strategy to achieve physical human-robot interaction (pHRI) so that the robot can acclimatize various handling characteristics of different operators through enough online learning. To also shorten the online training cycle, reduce the undesirable subjective factors and improve the overall training performance, a particle-swarm-optimization-based (PSO-based) algorithm is as well employed for optimizing the partition of state variable space and the distribution of discrete actions. Several groups of experiments have demonstrated the validity of this scheme and the effectiveness of the PSO optimization element.

Keywords

• Fuzzy reinforcement learning
• PSO-based optimization
• physical human-robot interaction
• variable admittance control
• surgical robot preoperative preparation