Unmanned aerial vehicle group dynamic recovery method

Abstract

The invention discloses an unmanned aerial vehicle group dynamic recovery method, and belongs to the technical field of unmanned aerial vehicles. The method specifically comprises the steps: obtaininginformation of an environment where an unmanned aerial vehicle group is located; combining different factors such as the number of tasks to be completed by each unmanned aerial vehicle, the distancefrom each unmanned aerial vehicle to a recovery point and the current performance, constructing a total cost function, and carrying out optimizing through an improved particle swarm optimization algorithm; on the premise of the lowest recovery cost, obtaining the priority sequence of each unmanned aerial vehicle; formulating a route for the sorted unmanned aerial vehicles to fly to a recovery point; considering that the unmanned aerial vehicle group encounters obstacles in the flight process, and carrying out the conflict resolution, and reordering part of the unmanned aerial vehicles waitingin a circling mode at the moment if the unmanned aerial vehicles possibly fail in line collision recovery, thereby guaranteeing the flight safety of the unmanned aerial vehicle group and the robustness of the algorithm. The method is low in algorithm complexity and high in calculation precision, and can effectively solve the problem of dynamic recovery of the unmanned aerial vehicle group in a complex environment.

Description

technical field [0001] The invention belongs to the technical field of unmanned aerial vehicles, and relates to a method for dynamically recovering an unmanned aerial vehicle group. Background technique [0002] UAVs have many advantages such as low cost, low loss, and high survival rate. They have been used in many fields such as military reconnaissance, disaster monitoring, pesticide spraying, and express transportation. As the flight environment becomes increasingly complex and changeable, the execution capability of a single UAV is increasingly unable to meet the needs of actual missions. Multi-UAV cluster formations to complete tasks together have become the main mode of future UAV clusters. [0003] UAV swarms can control a larger range, realize functional complementarity and task sharing, etc. Therefore, UAV swarms can perform complex tasks, shorten the time to perform tasks, and improve attendance efficiency, which is of great significance to the cooperation of UAV ...

AI Technical Summary

Problems solved by technology

As the flight environment becomes increasingly complex and changeable, the execution capability of a single UAV is increasingly unable to meet the actual task requirements, and multi-UAV cluster formations to jointly complete tasks have become the main mode of future UAV clusters

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