Multi-unmanned aerial vehicle formation flight obstacle avoidance control strategy making method

Abstract

The invention discloses a multi-unmanned aerial vehicle formation flight obstacle avoidance control strategy making method. The method comprises the following steps: introducing a repulsive force field function, determining the movement speeds and the directions of unmanned aerial vehicles after the encountering of the unmanned aerial vehicles through the value of the repulsive force field function, and determining the number of encountered unmanned aerial vehicles, the encountering direction of unmanned aerial vehicles, the order of unmanned aerial vehicles and the relative positions of unmanned aerial vehicles; for various conditions, including the encountering conditions of two unmanned aerial vehicles, three unmanned aerial vehicles and multiple unmanned aerial vehicles, respectively formulating unmanned aerial vehicle obstacle avoidance control strategies. According to the invention, the improved repulsive force field function is adopted, and unmanned aerial vehicles with the triggering condition and the relative distance between target points are introduced. As a result, an unmanned aerial vehicle can accurately fly towards a target pint after the obstacle avoidance conditionis triggered. Meanwhile, the air routes of other unmanned aerial vehicles are not influenced at all, so that the condition is not triggered again. The method is simple and feasible during the engineering practice of obstacle avoidance strategies for two unmanned aerial vehicles, three unmanned aerial vehicles and multiple unmanned aerial vehicles.

Description

Technical field [0001] The invention belongs to the technical field of UAV route planning, and specifically relates to a multi-UAV formation flying obstacle avoidance control strategy, which solves the problems of UAV formation avoidance and path replanning after obstacle avoidance in a dynamic environment. Background technique [0002] UAV formation obstacle avoidance and path re-planning after obstacle avoidance in a dynamic environment is a hot and difficult point in the field of cluster technology. Collision between UAVs in motion will cause great losses, so real-time obstacle avoidance algorithm It is especially important for drones flying in formation. Unlike offline obstacle avoidance, which knows the coordinates of obstacles in advance and performs path planning in advance, real-time obstacle avoidance is particularly a test of obstacle avoidance strategies. Currently popular algorithms include artificial potential field method, tangent graph method, grid method, neural ...

AI Technical Summary

Problems solved by technology

These advanced algorithms are theoretically feasible, but they are too cumbersome to be used in engineering practice

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