Multi-Strategy Enhanced Particle Swarm Optimization for Variable Curvature Path Planning in Flexible Needle Insertion
Yanding Qin, Jianing Teng, Chao Wen, Ge Fang, Hongpeng Wang, Jianda Han
AI summary
Problem
Planning safe, kinematically feasible 3D paths for flexible needles is challenging due to the risk of local minima in standard optimization algorithms and the difficulty of satisfying complex needle bending constraints while avoiding obstacles.
Approach
The authors improve particle swarm optimization by initializing particles with a uniform good point set and applying a hybrid mutation strategy to escape local optima, then refine the resulting path using a 3D biarc model to match the needle's physical bending limits.
Key results
- Enhanced PSO algorithm with good point set initialization and hybrid mutation for improved global search
- 3D biarc mathematical model ensuring kinematic feasibility of flexible needle trajectories
- Simulation results demonstrating superior obstacle avoidance and path length compared to PSO, WOA, and HWPSO
- Physical experiments achieving a minimum curvature radius of 49.6 mm and targeting errors under 4 mm
Why it matters
This method enhances the safety and precision of minimally invasive surgeries by providing reliable, automated path planning for steerable flexible needles in complex anatomical environments.
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.