Multi-objective optimization method and apparatus for task allocation and flight path planning of unmanned aerial vehicle formation

Abstract

The invention discloses a multi-objective optimization method and apparatus for task allocation and flight path planning of an unmanned aerial vehicle formation, which is used for multiple fixed-wingunmanned aerial vehicles to depart from a same airport in the same time to execute a coverage detection task for multiple objective regions. The method comprises the steps of obtaining objective region data, optimal detection time window data, unmanned aerial vehicle data and data of sensors carried by unmanned aerial vehicles; based on the objective region data, the optimal detection time windowdata, the unmanned aerial vehicle data and the data of the sensors carried by the unmanned aerial vehicles, building an MO-MUAV-STW model; based on constraints, building an initial solution set of theMO-MUAV-STW model; based on a preset NSGA-II algorithm, calculating the initial solution set to obtain a Pareto optimal solution of the MO-MUAV-STW model; and translating the optimal solution into aspecific allocation scheme, which serves as an allocation result of multi-objective optimization for the detection task allocation and the flight path planning of the unmanned aerial vehicle formation. The calculation is performed through the modeling and optimization algorithms to obtain the optimized unmanned aerial vehicle allocation scheme.

Description

technical field [0001] The invention relates to the technical field of unmanned aerial vehicle detection, in particular to a multi-objective optimization method and device for unmanned aerial vehicle formation task assignment and track planning. Background technique [0002] With the continuous development of aviation technology, more and more high-tech equipment has been applied to the aviation field. Among many high-tech equipment, UAV has quickly become a more important high-tech equipment in the process of aviation operations due to its advantages of high operating efficiency, low labor intensity, and low overall cost. For example, operational tasks such as aerial photography or scanning imaging can be performed. The current UAVs can be roughly divided into two categories: auto-wing and fixed-wing. Among them, fixed-wing UAVs are widely used in aviation operations due to their long flight distance, large cruising area, fast flight speed, and high altitude. [0003] Ho...

AI Technical Summary

Problems solved by technology

[0003] However, in the process of implementing the present invention, the inventors found that since the current drone operation is mainly based on human remote control, the effect of the actual operation is greatly affected by the operator's operating level, and it is planned through the artificial and visual way. The flight path deviates seriously from the theoretical flight path, resulting in a high omission rate and repetition rate of drone operations

Images