Abstract
Multi-UAV Cooperative Task Assignment Based on Orchard Picking Algorithm
Highlights
The unmanned aerial vehicle (UAV) has the advantages of strong continuous combat capability, high maneuverability, and environmental adaptability due to the fact that they are not controlled by pilots [1,2,3,4]
Author(s) [6,7,8,9], for example, proposed the fast task assignment (FTA) method algorithm based on Q-learning by neural network approximation and experience replay sequencing, which effectively transferred online computing to an offline learning process to assign tasks to heterogeneous UAVs under the condition of environmental uncertainty
The orchard picking algorithm proposed in this paper considers the attributes of the task, and considers the location distribution of the target group, which is an important index considered by the algorithm
Summary
The unmanned aerial vehicle (UAV) has the advantages of strong continuous combat capability, high maneuverability, and environmental adaptability due to the fact that they are not controlled by pilots [1,2,3,4]. In the process of actual combat, due to the complex and varied environment and wide distribution of targets, it usually requires multiple UAVs to cooperate with each other to carry out tasks [5], which makes the task execution time shorter and the completion rate higher [6,7]. The assignment of cooperative tasks to multi-UAVs refers to strategical execution of reconnaissance, attack, evaluation, and more for multiple targets within a certain area for multiple homogeneous or heterogeneous UAVs, maximizing the overall success of combat. If the position of the enemy target group on the battlefield is known, it is necessary to perform reconnaissance-attack-evaluation tasks for the enemy target group. If each UAV can carry only one reconnaissance device, one type of ammunition, or one image acquisition device, all the UAVs that perform tasks jointly form a heterogeneous UAV group. To function properly in this environment, the UAVs must complete specified tasks in the shortest possible time
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