Path planning of surgical needle: A new adaptive intelligent particle swarm optimization method

Abstract

Percutaneous puncture interventional therapy is an important method for pathological examination, local anesthesia, and local drug delivery in modern clinics. Due to the existence of complex obstacles such as nerves, arteries, bones and so on in the puncture path, it is a challenging work to design the optimal path for surgical needle. In this paper, we propose a new path planning method based on the adaptive intelligent particle swarm optimization (PSO) algorithm with parameter adjustment mechanism. First, force and motion analysis are carried out on the bevel-tip flexible needle after piercing into human tissues, the motion model of the needle and the spatial transformation model of puncture route in three-dimensional space are obtained, respectively. Then, a multi-objective function is established, which includes puncture path length function, puncture error function and collision detection function. Finally, the optimal puncture path is obtained based on the adaptive intelligent PSO algorithm. The simulation results show that the newly proposed path planning method has higher efficiency, better adaptability to complex environments and higher accuracy than other path planning methods in literature.

A survey on puncture models and path planning algorithms of bevel-tipped flexible needles

“…However, PSO generally exhibited premature convergence and poor local optimization ability, and the overall robustness was also inadequate. Based on the kinematic model of flexible needle puncture, Tan et al [ 97 ] proposed an adaptive intelligent PSO algorithm with a parameter adjustment mechanism. Compared with other path planning algorithms, this method has better efficiency and accuracy and is well adapted to complex environments.…”

Mobile Robot Path Planning Based on Improved Particle Swarm Optimization and Improved Dynamic Window Approach

“…To determine that IPSO has strong exploration ability and search accuracy, this paper conducts simulation experiments and data comparisons with traditional PSO, [18] improved particle swarm optimization (IPSO1), and [19] improved particle swarm optimization (IPSO2) in 10 * 10 and 15 * 15 environments, respectively.…”

“…Additionally, a random positive feedback factor is included to further enhance the algorithm's performance. Tan et al [19] proposed a method to determine the excellence of particles and populations by calculating their evolutionary capabilities. Tey then adjusted the inertia weight and learning factors based on the evolutionary ratio to improve the global exploration capability and convergence speed of the algorithm.…”

Four Forklift Induced Docking Strategy Based on NMPC

“…Kumar introduced a hybrid approach that combines regression and adaptive particle swarm optimization, designed to enhance the efficiency and adaptability of humanoid robot navigation in challenging environments [8]. Tan introduced a novel adaptive intelligent method based on particle swarm optimization for planning surgical needle paths, with the goal of enhancing both the efficiency and precision of surgical procedures [9]. Fang introduced an advanced neural network-based model for vehicle dynamics, aimed at achieving superior performance in trajectory tracking control.…”