Piecewise-potential-field-based path planning method for fixed-wing UAV formation.

Area of Science:

• Robotics
• Aerospace Engineering
• Artificial Intelligence

Background:

• Traditional artificial potential field (APF) methods for multi-UAV path planning face challenges with initial heading constraints and local minima.
• Existing APF methods often produce paths lacking sufficient smoothness for fixed-wing UAV formations.
• Dynamic obstacles and complex scenarios require robust path planning solutions for UAVs.

Purpose of the Study:

• To propose a novel piecewise potential field (PPF) based path planning method for fixed-wing Unmanned Aerial Vehicle (UAV) formations.
• To address limitations of APF methods, including initial heading constraints and local minima traps.
• To ensure smooth and conflict-free path planning for multiple UAVs in dynamic environments.

Main Methods:

• Utilizing potential field vectors to represent obstacle and target point functions, adhering to UAV kinematic constraints.
• Implementing a strategy to detect and escape local minimum regions by introducing additional potential field vectors.
• Designing change rules for potential field vectors during formation reconstruction to ensure smooth flight tracks via speed adjustments.

Main Results:

• The PPF-based method successfully planned paths for a five-UAV formation, considering initial heading limits.
• Compared to APF methods, the PPF approach generated significantly smoother flight paths.
• The proposed method demonstrated the ability to plan multiple, conflict-free UAV tracks in complex scenarios.

Conclusions:

• The piecewise potential field (PPF) method offers an effective solution for fixed-wing UAV formation path planning.
• PPF overcomes key limitations of APF, providing smoother trajectories and avoiding local minima.
• This approach enhances the safety and efficiency of multi-UAV operations in complex environments.