Unmanned aerial vehicle multi-task planning method based on improved mayfly naiad algorithm

Abstract

The embodiment of the invention discloses an unmanned aerial vehicle multi-task planning method based on an improved mayfly naiad algorithm, relates to the technical field of unmanned aerial vehicle task planning, and aims at unmanned aerial vehicle multi-task planning based on the improved mayfly naiad algorithm for multi-target area planning to realize multi-unmanned aerial vehicle multi-target task planning. The method comprises the following steps: acquiring a multi-task target area and an unmanned aerial vehicle sub-node target according to task information; segmenting the multi-task target area to obtain sub-areas, and distributing the segmented sub-areas to each unmanned aerial vehicle; establishing a task planning model by using the task execution information of the regional task of each sub-region; through a mayfly naiad optimization algorithm, obtaining a solution for multi-task planning of the unmanned aerial vehicle; and evaluating multi-task data by using the solution of the unmanned aerial vehicle multi-task planning, and then distributing the multi-task data to the unmanned aerial vehicles in the unmanned aerial vehicle group.

Description

technical field [0001] The invention relates to the technical field of multi-UAV mission planning, in particular to a UAV multi-task planning method based on an improved mayfly algorithm. Background technique [0002] In recent years, multi-UAV collaborative mission planning has been widely used in the aerospace field due to its excellent efficiency and fault tolerance. However, as the complexity of the mission increases, the time for mission planning will increase greatly. As the number of mission drones increases, the dimension of the system also increases exponentially, which will bring a great burden to the system and even cause the system to crash. Therefore, for the UAV multi-task planning problem, it is necessary to obtain the information of the mission target under different conditions and calculate how to reasonably allocate these multi-tasks by the UAV subsystem. In war environments such as multi-UAV mission reconnaissance and coordinated strikes, UAV multi-missio...

AI Technical Summary

Problems solved by technology

However, in the case of an increase in task objects, still using this as a basis may sacrifice the overall performance of the system, and the conflict between multi-tasks will become more obvious

When multiple tasks are carried out at the same time, if multiple optimal targets appear at the same time, the system will randomly assign drones to perform these tasks, which will eventually lead to a decline in the overall system performance, and even cause internal conflicts, causing irreparable losses

Images