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Related Experiment Video

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Trajectory Data Analyses for Pedestrian Space-time Activity Study

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Published on: February 25, 2013

Global Dynamic Path Planning of AGV Based on Fusion of Improved A* Algorithm and Dynamic Window Method.

Te Wang¹, Aijuan Li¹, Dongjin Guo²

¹School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China.

Sensors (Basel, Switzerland)

|March 28, 2024

Summary

This study introduces a novel fusion algorithm for automated guided vehicle (AGV) path planning and obstacle avoidance. The improved A* and dynamic window method combination enhances efficiency and safety in dynamic environments.

Keywords:

AGV dynamic obstacle avoidance dynamic window method improved A* algorithm

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Area of Science:

Robotics
Artificial Intelligence
Computer Science

Background:

Automated Guided Vehicles (AGVs) require efficient global path planning and real-time dynamic obstacle avoidance.
Traditional algorithms face challenges in complex, dynamic environments with unpredictable obstacles.

Purpose of the Study:

To develop a fusion algorithm combining an improved A* algorithm and the dynamic window method for AGV global optimal path planning and dynamic obstacle avoidance.
To enhance path planning efficiency, reduce search scope, and improve obstacle avoidance capabilities.

Main Methods:

Dynamically weighting the heuristic function of the A* algorithm to reduce search scope.
Implementing a path-optimization method to eliminate redundant nodes and turning points.
Integrating the improved A* algorithm with the dynamic window method for local dynamic obstacle avoidance within the global path.

Main Results:

The improved A* algorithm demonstrated a 26.3% reduction in planning time and a 57.9% smaller search scope compared to the traditional A* algorithm.
Path length was reduced by 7.2%, with significant reductions in path nodes (85.7%) and turning points (71.4%).
The fusion algorithm successfully evaded moving and unknown static obstacles in real-time within diverse map environments.

Conclusions:

The proposed fusion algorithm effectively addresses AGV global path planning and dynamic obstacle avoidance demands.
The enhanced A* algorithm significantly improves planning efficiency and path quality.
The integrated approach ensures real-time navigation and obstacle evasion in complex, dynamic environments.