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Multiple UAVs in forest fire fighting mission using particle swarm optimization

机译:使用粒子群优化森林灭火使命的多个无人机

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This paper investigates forest fires fighting application using team(s) of unmanned aerial vehicles (UAVs), in view of UAVs having great advantages in performing such tasks. However, important challenges in fire fighting missions in general are to perform the task with high performance in minimum time. In this paper, it is assumed that the fire spots are already detected and their coordinates will be sent to the fire fighting UAVs teams. Once the fire fighting team(s) receive relevant information, the team begins to solve the task assignment problem using the auction-based algorithm. The objective of the algorithm is to assign each UAV to each fire spot according to their relative distances, to minimize the distance traveled between each UAV's initial position and its assigned fire spot. Then, each UAV will optimally plan its path to its assigned fire spot by using particle swarm optimization (PSO) algorithm. The proposed algorithm calculates the optimal control inputs while taking into consideration the control inputs constraints while avoiding potential UAVs collisions during motion.
机译:本文研究了森林火灾用团队(S)无人驾驶飞行器(UAV)的,鉴于其在执行这些任务的巨大优势无人机战斗应用。然而,在一般的消防任务的重要挑战是在最短的时间高性能执行任务。在本文中,假定火点已经被检测它们的坐标将被发送到消防无人机队。一旦扑火队伍(S)收到相关信息,球队开始使用基于拍卖的算法来解决任务分配问题。该算法的目标是根据它们的相对距离每个UAV分配给每个火点,以最小化每个UAV的初始位置和其分配的火点之间行进的距离。然后,每个UAV将最佳地通过使用粒子群优化(PSO)算法计划分配给它的火点其路径。同时考虑控制输入的约束,而运动期间避免了潜在的碰撞无人机该算法计算最佳控制输入。

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