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A Cooperative Hunting Method for Multi-USV Based on the A* Algorithm in an Environment with Obstacles.

Zhihao Chen1, Zhiyao Zhao1,2,3, Jiping Xu1,2,3

  • 1School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China.

Sensors (Basel, Switzerland)
|August 26, 2023
PubMed
Summary

This study introduces an efficient cooperative hunting method for multiple unmanned surface vehicles (USVs) using an enhanced A* algorithm. The approach improves path planning and target search capabilities in obstacle-filled environments.

Keywords:
A* algorithmmulti-USV swarmobstacle avoidancepath planningtarget hunting

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Area of Science:

  • Robotics and Autonomous Systems
  • Marine Engineering
  • Artificial Intelligence

Background:

  • Single unmanned surface combatants (USVs) exhibit limited mission execution capabilities.
  • Cooperative strategies involving multiple USVs are essential for complex tasks, particularly cooperative hunting.
  • Existing path planning algorithms may lack efficiency and adaptability in dynamic, multi-agent scenarios.

Purpose of the Study:

  • To develop an efficient cooperative hunting method for multiple USVs in environments with obstacles.
  • To enhance the A* algorithm for improved path planning and target search efficiency.
  • To introduce a biomimetic swarm strategy for coordinated USV hunting behavior.

Main Methods:

  • An enhanced A* algorithm incorporating path smoothing based on USV minimum turning radius.
  • Utilization of a post-order traversal recursive algorithm for optimal path determination, improving A* efficiency.
  • A biomimetic multi-USV swarm hunting strategy simulating lion hunting tactics for pre-formation and target encirclement.

Main Results:

  • The proposed path smoothing method enhances the efficiency of the A* algorithm.
  • The biomimetic swarm strategy enables autonomous formation and effective target containment.
  • Simulation experiments validated the algorithm's effectiveness in path planning and target search.

Conclusions:

  • The developed cooperative hunting method significantly improves the performance of multi-USV systems.
  • The enhanced A* algorithm and biomimetic swarm strategy offer a robust solution for autonomous marine operations.
  • This research contributes to the advancement of coordinated autonomous vehicle systems for surveillance and interdiction.