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Real-Time Path Planning for Robot Using OP-PRM in Complex Dynamic Environment.

Lingjian Ye1,2, Jinbao Chen1, Yimin Zhou1

  • 1Shenzhen Institute of Advanced Technology, Chinese Academy of Science, Shenzhen, China.

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Summary
This summary is machine-generated.

This study introduces a new path planning method for autonomous robots navigating complex environments with obstacles. The Obstacle Potential field-Probabilistic Roadmap Method (OP-PRM) improves real-time pathfinding in narrow corridors.

Keywords:
OP-PRM algorithmincremental heuristic search algorithmnarrow corridorobstacle potential fieldpath planning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Path Planning Algorithms

Background:

  • Autonomous robots face challenges in dynamic environments with narrow corridors and mobile obstacles.
  • Traditional offline path planning struggles with real-time path acquisition in such complex scenarios.

Purpose of the Study:

  • To propose a novel online path planning algorithm for autonomous robots.
  • To enhance the efficiency of path planning in complex, dynamic environments with narrow passages.

Main Methods:

  • Introduced the Obstacle Potential field-Probabilistic Roadmap Method (OP-PRM).
  • Utilized an obstacle potential field to define obstacle areas and construct a potential-linked roadmap.
  • Focused sampling points on narrow corridors around obstacle boundaries.
  • Applied the D* Lite algorithm for shortest path searching on the constructed roadmap.

Main Results:

  • OP-PRM effectively increases sampling point effectiveness in narrow corridors.
  • The algorithm enables fast optimal path searching for robots.
  • Successfully demonstrated efficient navigation through narrow corridors in complex dynamic environments.

Conclusions:

  • OP-PRM is a viable solution for real-time online path planning in challenging robotic navigation scenarios.
  • The method significantly improves the ability of autonomous robots to traverse narrow spaces amidst dynamic obstacles.