Autonomous Path Planning With Muscle Force Optimization for Robot-Assisted Pelvic Fracture Closed Reduction

Abstract

The development of pelvic fracture closed reduction surgical robots is in its infancy, and the path planning of such robots lacks consideration of muscle force. This studys objective is to develop an intelligent path-planning algorithm to reduce muscle resistance and improve surgical safety. First, an orientation planning strategy (OPS) is introduced to adjust the orientation of fracture fragments. Then, the OPS is coupled to the 3D A* algorithm to obtain the 3D-OPS A* algorithm, which initially lowers the muscle resistance. In addition, a musculoskeletal model of a fractured pelvis is constructed based on the OpenSim platform, and the models validity is verified using data comparison and in vitro experiments. Finally, the OpenSim API is combined with the 3D-OPS A* algorithm to obtain 3D-OPSF A*, which is used to search for the reduction path with the least muscle resistance. Results show that the OPS-based path planning algorithm lowers muscle resistance by 49.4% on average compared to traditional and 3D A* planning. Compared to 3D-OPS A*, 3D-OPSF A* achieves 12.8% lower muscle resistance. The path planned by the 3D-OPSF A* algorithm significantly lowers muscle resistance, optimizes the robot payload intensity, and enhances the safety of the surgery. This study will be of great reference value for future research on intelligent path planning for pelvic fracture closed reduction surgical robots.