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Improved RRT*-Connect Manipulator Path Planning in a Multi-Obstacle Narrow Environment.

Xueyi He1,2, Yimin Zhou2, Haonan Liu1,2

  • 1School of Mechanical and Control Engineering, Guilin University of Technology, Guilin 541006, China.

Sensors (Basel, Switzerland)
|April 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an improved RRT*-Connect algorithm for efficient robotic arm path planning in complex environments. The new method significantly reduces planning time and path costs for manipulator operations.

Keywords:
RRT*-Connectadaptive step sizeheuristic sampling strategynode rejection strategypath planningrobotic arm

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Motion Planning

Background:

  • Robotic arm path planning in narrow, obstacle-rich environments presents significant computational challenges.
  • Existing algorithms often struggle with efficiency and optimality in complex scenarios.

Purpose of the Study:

  • To propose an improved Rapidly-exploring Random Tree star-Connect (IRRT*-Connect) algorithm for enhanced robotic arm path planning.
  • To address limitations in sampling efficiency, search acceleration, and path optimality in cluttered environments.

Main Methods:

  • Implemented a heuristic sampling strategy integrating ellipsoidal subset and goal-biased sampling to compress the search space.
  • Introduced an adaptive step-size and node rejection strategy for accelerated, collision-free path generation.
  • Applied cubic non-uniform B-spline interpolation for path smoothing and pruning optimization to remove redundant nodes.

Main Results:

  • IRRT*-Connect demonstrated reduced planning times compared to existing algorithms.
  • The proposed method achieved lower path costs for robotic manipulator operations.
  • Successful simulations in Python/ROS and physical experiments on a UR5 robotic arm validated the approach.

Conclusions:

  • The IRRT*-Connect algorithm offers a significant improvement for robotic arm path planning in challenging environments.
  • The enhanced sampling and search strategies contribute to faster and more optimal path generation.
  • This work provides a robust solution for real-world robotic applications requiring efficient motion planning.