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

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.