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CP-QRRT*: A Path Planning Algorithm for Hyper-Redundant Manipulators Considering Joint Angle Constraints.

Tianya Wang1, Guoliang Ma1, Lisong Xu1

  • 1School of Energy and Power Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.

Sensors (Basel, Switzerland)
|March 17, 2025
PubMed
Summary
This summary is machine-generated.

A new algorithm, CP-QRRT*, improves path planning for hyper-redundant manipulators (HRMs) in tight spaces. It ensures joint angle constraints are met, creating shorter, faster paths.

Keywords:
PSORRThyper-redundant manipulatorsjoint angle constraintspath planning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Mechanical Engineering

Background:

  • Path planning for hyper-redundant manipulators (HRMs) in confined spaces faces challenges.
  • Existing algorithms struggle with joint angle constraints, path redundancy, and long computation times.

Purpose of the Study:

  • To propose a novel algorithm, CP-QRRT*, for efficient and constraint-aware path planning of HRMs.
  • To address limitations of previous methods in terms of path quality and planning efficiency.

Main Methods:

  • Utilized Particle Swarm Optimization (PSO) to enhance the directional sampling of RRT algorithms.
  • Integrated ancestor node backtracking from Quick-RRT* to prevent local optima.
  • Implemented a constraint module to enforce maximum joint angle limits for HRMs.

Main Results:

  • The CP-QRRT* algorithm successfully satisfies HRM joint angle constraints.
  • Planned paths were found to be shorter and required less planning time compared to existing methods.
  • Demonstrated improved performance in simulation experiments for complex path planning tasks.

Conclusions:

  • CP-QRRT* offers a robust solution for path planning of HRMs in confined environments.
  • The algorithm enhances planning efficiency and ensures adherence to critical joint angle limitations.
  • This advancement contributes to more effective robotic manipulation in constrained settings.