Research on an Autonomous UAV Search and Rescue System Based on the Improved

TL;DR

This paper presents an improved autonomous UAV search and rescue system utilizing an enhanced EGO-Planner algorithm, inverse motor backstepping, and object detection, demonstrating higher efficiency and reliability in complex environments.

Contribution

The paper introduces a novel UAV search and rescue system with an improved EGO-Planner and robust obstacle avoidance, enhancing flight efficiency and operational reliability.

Findings

Higher efficiency in search and rescue tasks compared to traditional algorithms.

Enhanced obstacle avoidance capabilities.

Successful validation through simulation and field tests.

Abstract

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.