Multi-UAV Search and Rescue with Enhanced A<a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:msup> <a:mrow/> <a:mrow> <a:mo>∗</a:mo> </a:mrow> </a:msup> </a:math> Algorithm Path Planning in 3D Environment
Yuwen Du
Abstract
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 <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M2"> <a:msup> <a:mrow/> <a:mrow> <a:mo>∗</a:mo> </a:mrow> </a:msup> </a:math> 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 <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M3"> <c:msup> <c:mrow/> <c:mrow> <c:mo>∗</c:mo> </c:mrow> </c:msup> </c:math> 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 <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" id="M4"> <e:msup> <e:mrow/> <e:mrow> <e:mo>∗</e:mo> </e:mrow> </e:msup> </e:math> algorithm, the results demonstrate that the enhanced algorithm is effective in the scenario.