Method for planning routes of multi-unmanned aerial vehicles based on particle swarm optimization algorithm

Abstract

The invention provides a method for planning the routes of multi-unmanned aerial vehicles based on a particle swarm optimization algorithm. The method comprises the following steps: establishing a three-dimensional map for planning space of the routes of the multi-unmanned aerial vehicles at first; then constructing multi-unmanned aerial vehicle route planning models under the three-dimensional map, wherein the models mainly comprise a barrier model, a route model, an unmanned aerial vehicle state model, a constraint model and a multi-unmanned aerial vehicle route planning mathematic model; and solving the problem of multi-unmanned aerial vehicle route planning under the three-dimensional map by using the particle swarm optimization algorithm. The method provided by the invention improves multi-unmanned aerial vehicle route planning capacity in a complex environment and provides technical support for air traffic management platforms for unmanned aerial vehicles, autonomous flight systems for multi-unmanned aerial vehicles, etc.

Description

technical field [0001] The invention relates to a multi-UAV route planning method based on a particle swarm optimization algorithm, and belongs to the field of UAV route planning. Background technique [0002] At present, UAVs have been successfully applied in many fields such as electric power, communication, meteorology, and monitoring, and have achieved good economic and social effects. Due to the limitations of the software and hardware conditions of a single UAV, it is difficult to meet the increasingly complex application environment and diverse mission requirements. The mode of multi-UAV cooperative task completion is an important trend in the development and application of UAVs in the future, and it is an effective way to improve the efficiency of UAV mission execution, expand new mission methods, and improve system reliability. The U.S. Air Force's Scientific Advisory Committee has pointed out that drones should work in groups rather than acting alone. [0003] Mu...

AI Technical Summary

Problems solved by technology

These methods have great limitations in practical application, such as the lack of effective decision-making for multi-UAV route planning, that is, insufficient consideration of the conflict and correlation redundancy of information observations between multi-UAVs; the weighting algorithm The distribution of weights is highly subjective, lacking effective and practical weight distribution methods, etc.

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