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A Method on Dynamic Path Planning for Robotic Manipulator Autonomous Obstacle Avoidance Based on an Improved RRT

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Summary
This summary is machine-generated.

This study introduces Smoothly RRT (S-RRT), an improved algorithm for autonomous obstacle avoidance in robotic manipulators. S-RRT enhances path planning efficiency and safety in dynamic, unstructured factory environments.

Keywords:
RRT algorithmautonomous obstacle avoidancedynamic path planningdynamic unstructured environmentrobotic manipulator

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Intelligent factories require robotic manipulators to operate safely and efficiently in dynamic, unstructured environments.
  • Autonomous path planning is crucial for enhancing robotic manipulator intelligence and enabling human-robot collaboration.
  • Existing Rapidly Exploring Random Tree (RRT) algorithms face limitations in dynamic, unstructured settings.

Purpose of the Study:

  • To propose an improved RRT algorithm (S-RRT) for autonomous obstacle avoidance in robotic manipulators.
  • To enhance path planning speed and efficiency in complex, dynamic environments.
  • To generate smooth, executable paths with maximum curvature constraints.

Main Methods:

  • Developed Smoothly RRT (S-RRT) by introducing directional node targeting to improve RRT sampling.
  • Implemented a path optimization strategy incorporating maximum curvature constraints.
  • Validated the method through static simulations (MATLAB), dynamic simulations (ROS), and real-world experiments.

Main Results:

  • The S-RRT algorithm demonstrated increased sampling speed and efficiency compared to standard RRT.
  • The proposed method successfully generated smooth, continuous, and executable paths.
  • Experimental validation confirmed the correctness, effectiveness, and practicability of S-RRT for autonomous obstacle avoidance.

Conclusions:

  • The S-RRT method offers significant practical engineering value for robotic manipulator obstacle avoidance in intelligent factories.
  • The research provides a valuable theoretical reference for path planning in other robotic systems.
  • The study highlights the potential of improved RRT algorithms for complex robotic applications.