Maritime obstacle-aware formation control for USVs with an optimized artificial potential field

Area of Science:

• Robotics and Autonomous Systems
• Maritime Engineering
• Control Theory

Background:

• Multi-USV formation control in cluttered maritime settings faces challenges like local minima and dynamic obstacle avoidance.
• Conventional path planning methods struggle with goal non-reachability and delayed responses to moving obstacles.

Purpose of the Study:

• To present an enhanced Artificial Potential Field (APF) algorithm for robust multi-USV formation control and obstacle avoidance.
• To address limitations of existing methods, including local minima, goal non-reachability, and dynamic obstacle handling.

Main Methods:

• Introduced instantaneous escape forces, rotational factors, and relative velocity terms to the APF framework.
• Developed collision-free formation trajectory generation for follower USVs.
• Enhanced obstacle avoidance efficiency in dynamic maritime environments.

Main Results:

• Numerical simulations showed significant performance improvements over traditional and existing APF methods.
• Reduced target arrival times by 34.52% and 25.13% compared to benchmark methods.
• Experimental validation confirmed the method's ability to maintain formation integrity and avoid collisions.

Conclusions:

• The enhanced APF algorithm offers a promising solution for autonomous maritime operations.
• The method ensures smooth formation maintenance and effective obstacle avoidance, even with dynamic obstacles.
• Validated practical applicability for real-world USV deployment.