Artificial Potential Field Based Trajectory Tracking for Quadcopter UAV Moving Targets.

Area of Science:

• Robotics
• Control Systems
• Artificial Intelligence

Background:

• Moving-target tracking is crucial for Unmanned Aerial Vehicle (UAV) applications.
• Cooperative robotics scenarios involve ground and aerial robots, with ground robots potentially acting as mobile landing pads.

Purpose of the Study:

• To propose a novel approach for position-tracking using artificial potential fields (APF) for quadcopter UAVs.
• To address the dynamic nature of online tracking problems while minimizing tracking error.

Main Methods:

• Utilized artificial potential fields (APF) for online position-tracking of quadcopter UAVs.
• Developed a new flight control system based on roll, pitch, and yaw angle control, driven by the velocity vector.
• Simulated the approach using the AR Drone 2.0 model.

Main Results:

• Demonstrated the applicability of APF for holonomic UAVs like quadcopters in dynamic tracking.
• Showcased that APF-controlled quadcopters exhibit non-holonomic behavior during 90° turns.
• Enabled UAVs and their cameras to orient towards the tracked target effectively.

Conclusions:

• The proposed APF-based method enables precise online tracking of moving targets by quadcopter UAVs.
• The novel flight control aligns the UAV's course and velocity with the APF-derived velocity vector.
• Future research can validate this method in real-world flights.