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  1. Home
  3. Path Planning Of Flexible Needle Based On Improved Particle Swarm Optimization Algorithm.
  1. Home
  3. Path Planning Of Flexible Needle Based On Improved Particle Swarm Optimization Algorithm.

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Related Experiment Video

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware
08:13

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

Path Planning of Flexible Needle Based on Improved Particle Swarm Optimization Algorithm.

Yan-Jiang Zhao1,2, Wen-Shuo Shen1, Hui Gao1

  • 1Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin, Heilongjiang, China.

International Journal for Numerical Methods in Biomedical Engineering
|June 26, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a hybrid path planning algorithm combining particle swarm optimization (PSO) and simulated annealing (SA) to improve flexible needle navigation. The novel method enhances global search capabilities, reducing errors in clinical procedures.

Keywords:
flexible needleparticle swarm optimizationsimulated annealing

Related Experiment Videos

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware
08:13

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

Area of Science:

  • Robotics and Medical Devices
  • Computational Intelligence
  • Surgical Navigation

Background:

  • Flexible needles offer steerable flexibility, crucial for intelligent navigation in medical procedures.
  • Current path planning methods for flexible needles often suffer from local optima, hindering globally optimal trajectory identification.

Purpose of the Study:

  • To develop a novel hybrid path planning algorithm for flexible needles that enhances global search capabilities.
  • To overcome limitations of existing methods, such as premature convergence and local optima, in flexible needle navigation.

Main Methods:

  • A hybrid path planning algorithm combining Particle Swarm Optimization (PSO) with a Simulated Annealing (SA) mechanism was proposed.
  • The algorithm integrates SA's probabilistic judgment and annealing mechanism to modify PSO's particle diffusion and dynamically adjust inertia weight.
  • Simulations were performed in a prostate-anatomy-mimicking environment to evaluate planning outcomes.

Main Results:

  • The proposed hybrid algorithm demonstrated superior planning outcomes compared to traditional PSO in simulations.
  • Experimental validation with two targets showed high accuracy: mean errors of 1.25mm and 1.21mm, RMSE of 1.43mm and 1.40mm, and max errors of 2.22mm and 2.19mm.
  • Achieved accuracy meets requirements for general clinical procedures, confirming algorithm feasibility and precision.

Conclusions:

  • The hybrid PSO-SA algorithm effectively enhances global search capabilities for flexible needle path planning.
  • The method significantly reduces the risk of premature convergence, leading to more accurate and globally optimal trajectories.
  • This advancement supports the clinical application of flexible needles and the development of intelligent navigation systems.