Multi-UAV route planning and dynamic obstacle avoiding method based on combination of Voronoi ant colony algorithm with artificial potential field method

Abstract

The invention discloses a multi-UAV route planning and dynamic obstacle avoiding method based on combination of a Voronoi ant colony algorithm with an artificial potential field method. According to the method, modules like a laser radar and a camera are used for acquiring a position of a static obstacle in an environment; the static environment is divided based on a Voronoi diagram and offline planning of optimal paths of all unmanned aerial vehicles is carried out based on an improved ant colony algorithm; and when all unmanned aerial vehicles fly along the offline planned paths, dynamic obstacles are monitored by ultrasonic sensors in real time, a gravitational force and repulsive force model of the unmanned aerial vehicles is established by using the dynamic obstacles as threat sources, combined forces on the unmanned aerial vehicles are calculated based on the gravitational forces and repulsive forces, the unmanned aerial vehicles fly to static routes and fly continuously along the offline planned paths, circulation is carried out based on the method until all unmanned aerial vehicles fly to target destinations. With the disclosed method, optimal routes for avoiding all staticobstacles are planned for the multi-UAV formation and the iteration time is short; the dynamic obstacles can be monitored in real time; and local routes can be planned timely to avoid collisions.

Description

technical field [0001] The invention belongs to the technical field of multi-UAV route planning and dynamic obstacle avoidance, in particular to a multi-UAV route planning and dynamic obstacle avoidance method combined with a Voronoi ant colony algorithm and an artificial potential field method. technical background [0002] The problem of UAV path planning is an important and difficult point of formation control, and it is also a research focus of mission planning. The UAV path planning problem needs to consider many constraints, including static environmental factors, radar and static obstacle factors, flight route length (route fuel cost) and total flight time. After considering various external environmental factors and various flight indicators and performance of the UAV itself, an optimal or better path from the starting point to the target point is planned. In the case of known static environment and static threat sources, according to the research content of static ...

AI Technical Summary

Problems solved by technology

This method is more intuitive and has a small amount of calculation. It is a dynamic path planning method and is widely used in target tracking. However, this algorithm also has problems such as easy to fall into local optimum and path oscillation.

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