The demand is to solve the issue of UAV (unmanned aerial vehicle) operating autonomously and implementing practical functions such as search and rescue in complex unknown environments. This paper proposes an autonomous search and rescue UAV system based on an EGO-Planner algorithm, which is improved by innovative UAV body application and takes the methods of inverse motor backstepping to enhance the overall flight efficiency of the UAV and miniaturization of the whole machine. At the same time, the system introduced the EGO-Planner planning tool, which is optimized by a bidirectional A* algorithm along with an object detection algorithm. It solves the issue of intelligent obstacle avoidance and search and rescue. Through the simulation and field verification work, and compared with traditional algorithms, this method shows more efficiency and reliability in the task. In addition, due to the existing algorithm's improved robustness, this application shows good prospection.

翻译：为解决无人机在复杂未知环境中自主运行并实现搜救等实际功能的需求，本文提出一种基于改进型EGO-Planner算法的自主搜救无人机系统。通过创新的无人机本体应用及采用逆电机反步法，提升了无人机整体飞行效率并实现了整机小型化。同时，系统引入经双向A*算法与目标检测算法优化的EGO-Planner规划工具，解决了智能避障与搜救难题。仿真与实地验证结果表明，相较于传统算法，本方法在任务中展现出更高的效率与可靠性。此外，由于现有算法鲁棒性的提升，该应用具有良好的应用前景。