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Dynamic Path Planning of AGV Based on Kinematical Constraint A* Algorithm and Following DWA Fusion Algorithms.

Xiong Yin1, Ping Cai1, Kangwen Zhao1

  • 1School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330000, China.

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Summary
This summary is machine-generated.

This study introduces a novel fusion algorithm for autonomous guided vehicle (AGV) path planning, enhancing efficiency and safety. The new method significantly reduces path length, time, and turns compared to traditional approaches.

Keywords:
A* algorithmDWA algorithmdynamic obstacle avoidanceglobal path planning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Traditional path planning algorithms for autonomous guided vehicles (AGVs) face limitations in efficiency and adaptability.
  • Existing methods often struggle with dynamic environments and complex kinematic constraints.

Purpose of the Study:

  • To develop an improved path planning algorithm for AGVs by fusing the kinematical constraint A* algorithm with the dynamic window approach (DWA).
  • To enhance the efficiency, reduce path length and turns, and improve obstacle avoidance capabilities of AGVs.

Main Methods:

  • A novel fusion algorithm combining a kinematical constraint A* algorithm for global path planning and a dynamic window approach (DWA) for local path planning and obstacle avoidance.
  • The kinematical constraint A* algorithm incorporates node optimization, an improved heuristic function, secondary redundancy reduction, and B-spline curve fitting for dynamic characteristic adherence.
  • The DWA algorithm is optimized with a heuristic function closer to the global optimum for dynamic obstacle avoidance.

Main Results:

  • The proposed fusion algorithm demonstrated a 3.6% reduction in path length compared to traditional A* and DWA fusion.
  • Path planning time was reduced by 6.7% with the new algorithm.
  • The final path exhibited a 25% reduction in the number of turns, indicating smoother trajectories.

Conclusions:

  • The fusion algorithm effectively addresses the limitations of traditional path planning methods for AGVs.
  • The optimized approach offers significant improvements in path efficiency and adaptability to dynamic environments.
  • This enhanced path planning strategy contributes to more robust and efficient AGV operations.