Coordinated Target Tracking via a Hybrid Optimization Approach.

Area of Science:

• Robotics and Control Systems
• Computer Vision and AI
• Aerospace Engineering

Background:

• Unmanned aerial vehicles (UAVs) have advanced significantly, enabling applications like ground object tracking.
• Single UAVs struggle with tracking moving objects due to environmental uncertainties and limited sensor range.
• Cooperative tracking with multiple UAVs is essential for robust and continuous monitoring.

Purpose of the Study:

• To develop an optimal cooperative path planning strategy for multiple UAVs tracking ground moving objects.
• To address flight dynamic and collision-free constraints in multi-UAV cooperative tracking.
• To enhance sensor coverage duration and tracking reliability.

Main Methods:

• Investigated cooperative tracking path planning for multiple UAVs with gimbaled sensors.
• Employed an evolutionary optimization technique based on Chemical Reaction Optimization (CRO).
• Simulated and compared the proposed method against existing approaches.

Main Results:

• The developed cooperative tracking paths significantly increase sensor coverage time.
• The method effectively manages flight dynamic and collision-free constraints.
• Demonstrated superior performance in maintaining continuous tracking of ground moving objects.

Conclusions:

• The CRO-based optimal cooperative tracking path planning method enhances UAV tracking capabilities.
• This approach improves robustness and efficiency in multi-UAV surveillance missions.
• The strategy ensures safe and effective operation under dynamic conditions.