Document Type : Original Research Paper

Authors

• Zahra Hassani
• Vahab Nekoukar

Control Engineering Department, Electrical Engineering Faculty, Shahid Rajaee Teacher Training University, Tehran, Iran.

Abstract

Background and Objectives: Currently, the control engineering community is increasingly focusing on research related to Unmanned Aerial Vehicles (UAVs) due to their versatile capabilities. Among the various applications, target detection and tracking stand out as crucial. Recent advancements in Artificial Intelligence (AI) and Deep Learning (DL) have the potential to enhance the synergy between vision and control in UAV operations. By integrating AI algorithms with control methods, the accuracy of target information can be significantly improved in UAVs. This research introduces an autopilot system for quadcopters to search for and track a predetermined target.
Methods: The autopilot system utilizes the YOLO network, a robust convolutional neural network-based system, for real-time target detection. To enhance object tracking robustness, the Kalman filter is integrated into the system. Furthermore, Proportional-Derivative (PD) controllers are utilized to calculate suitable control commands, enabling the quadcopter to effectively track both stationary and moving targets. Additionally, an object retrieval strategy is proposed to locate and recover lost objects during the tracking phase.
Results: The effectiveness of the proposed system was evaluated through real-time experimental trials involving diverse scenarios encompassing both stationary and moving targets. The integration of the YOLOv5 network with the Kalman filter substantially improved detection accuracy and stability. Furthermore, the object retrieval mechanism demonstrated high reliability in recovering lost targets, thereby increasing overall system resilience. The PD-based control scheme enabled responsive and precise trajectory adjustments, contributing to consistent target tracking performance across all test cases.
Conclusion: Integration of a YOLOv5-based detection module, a Kalman filter for robust tracking, and PD controllers for flight control provides an autonomous quadcopter system capable of detecting and tracking both stationary and moving targets with unknown dynamics. The proposed approach shows promise for real-time autonomous tracking applications and offers a foundation for future development in more complex, outdoor scenarios.

Keywords

• Deep Learning
• Machine Vision
• Quadcopter
• YOLO

Main Subjects

• Robotic

Open Access

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Publisher

Shahid Rajaee Teacher Training University