Multi-UAV Search and Rescue with Enhanced A∗ Algorithm Path Planning in 3D Environment

• Published in: International Journal of Aerospace Engineering Vol. 2023; pp. 1 - 18
• Main Author: Du, Yuwen
• Format: Journal Article
• Language: English
• Published: New York Hindawi 06.02.2023; John Wiley & Sons, Inc; Wiley
• Subjects: Aerospace engineering; Algorithms; Casualties; Collaboration; Collision avoidance; Communication; Cooperation; Drone aircraft; Drones; Efficiency; Evacuations & rescues; Methods; Obstacle avoidance; Path planning; Planning; Search and rescue missions; Search and rescue operations; Surveys; Unmanned aerial vehicles; Vehicles; China
• ISSN: 1687-5966; 1687-5974
• DOI: 10.1155/2023/8614117

The unmanned aerial vehicles (UAV) are now widely used in search and rescue (SAR) missions to locate casualties and survey terrain. To solve the problem of long calculation time and large memory usage of the UAV obstacle-avoidance path-planning algorithm in cooperative tasks, this paper proposes a method that combines the A∗ algorithm and the task allocation algorithm to achieve a faster and more effective path-planning method. First, the environment is displayed in the form of a grid. Then, the enhanced algorithm divides the task area for UAVs. Finally, each UAV performs SAR path planning in the mission area. The tasks of mapping the environment and searching for target points by UAV swarms are discussed in this study. Our research enhances A∗ algorithms for generating the shortest collision-free paths for drone swarms. Further, we evaluate the algorithm via simulating the task assignment algorithm and path-planning algorithm of a 3D map and 2D map. Compared with the traditional A∗ algorithm, the results demonstrate that the enhanced algorithm is effective in the scenario.