Multi-Strategy Enhanced Particle Swarm Optimization for Variable Curvature Path Planning in Flexible Needle Insertion

Abstract

Flexible needles provide enhanced adaptability for navigating puncture pathways and avoiding obstacles when compared to conventional rigid needles. However, developing a three dimensional (3D) curved path for flexible needle is challenging, particularly in achieving both effective obstacle avoidance and precise targeting. To this end, we proposed an improved particle swarm optimization-based path planning approach by incorporating good point set initialization and heuristic multi-mutation strategy. Such incorporation greatly enhanced the algorithm’s global search capability while ensuring fast convergence speed. In addition, 3D biarc curve fitting was employed to develop a kinematically reachable path for bevel tip needles. Obstacle-avoidance simulations conducted demonstrate the superior performance of proposed method against state-of-the-art algorithms in the aspect of path length and distance to obstacles, repeatability and local minima trap avoidance. Needle puncturing experiments performed using duty cycling control achieved a small curvature radius of 49.6 mm and targeting errors of less than 4 mm. This algorithm facilitates efficient variable curvature path planning for flexible needles, ensuring precise targeting while effectively avoiding obstacles.