Khóa luận tốt nghiệp: Nghiên cứu xây dựng và thử nghiệm cụm thiết bị bay không người lái phục vụ mục tiêu tìm kiếm, cứu hộ

Recent years have seen a rise in natural disasters and accidents, leading to significant human and property losses. In these critical situations, effective search and rescue (SAR) operations are paramount, yet often hindered by difficult-to-access or vast search areas. To address these challenges, the development of advanced robotic systems has become crucial. This study focuses on the design, construction, and experimental testing of an unmanned aerial vehicle (UAV) cluster, or swarm drone system, specifically tailored for tìm kiếm, cứu hộ (search and rescue) missions. Such a system holds immense potential to enhance the efficiency, speed, and safety of SAR efforts, minimizing risks for human responders and improving outcomes in urgent scenarios.

Researchers and engineers in robotics, unmanned aerial vehicles (UAVs), mechatronics, and emergency management, as well as students specializing in related engineering disciplines.

This thesis details the comprehensive process of researching, developing, and experimentally testing a drone cluster, or unmanned aerial vehicle (UAV) system, specifically designed for tìm kiếm, cứu hộ (search and rescue) operations. The work encompasses the entire lifecycle, from initial conceptualization and hardware selection to software integration and field trials. Key tasks for the drone cluster in SAR identified and addressed include locating and pinpointing missing persons, detecting and marking critical items, conducting reconnaissance in hazardous zones, and transmitting real-time imagery and data to a Ground Control Station (GCS). The practical implementation involved the design, fabrication, and integration of essential drone hardware components such as GPS modules and wireless communication systems, alongside the development of the GCS. A significant focus was placed on enhancing the wireless video transmission module, aiming to improve both operational range and signal quality to enable autonomous and efficient drone performance in diverse SAR environments. Following development, the drone cluster underwent rigorous testing in several simulated and real-world tìm kiếm, cứu hộ scenarios. The results demonstrated the system's effectiveness and feasibility, showcasing stable flight in both manual and autonomous modes, accurate waypoint navigation, and the capability for coordinated multi-drone operations. Furthermore, the system successfully tackled challenges related to extending video transmission range and quality. While achieving positive outcomes, the research also identified areas for future improvement, such as extending flight duration and integrating advanced obstacle avoidance features, paving the way for more robust drone rescue capabilities.