Self-organizing method for cooperative scouting and hitting task of heterogeneous multi-unmanned-aerial-vehicle system

Abstract

The invention discloses a self-organizing method for a cooperative scouting and hitting task of a heterogeneous multi-unmanned-aerial-vehicle system. A heterogeneous multi-unmanned-aerial-vehicle system is decomposed into two isomorphic sub systems and a corresponding cooperative way between the two sub systems is also designed; and the two sub systems carry out task planning respectively and also carry out mutual cooperation. For an isomorphic reconnaissance type unmanned aerial vehicle system and an isomorphic scouting and hitting unmanned aerial vehicle system, a cooperative searching task self-organizing method and a cooperative scouting and hitting autonomous task planning method are designed respectively. According to the cooperative searching task self-organizing method, problem decomposition is carried out by using a method based on distributed model prediction control; and then solution is carried out by using a particle swarm algorithm. And according to the cooperative scouting and hitting autonomous task planning method, a normal flight mode without any detection of a threat and a threat avoiding mode with threat detection are designed during a local problem construction process of each unmanned aerial vehicle based on a distributed ant colony algorithm. Therefore, an on-line requirement can be met well.

Description

technical field [0001] The invention relates to a multi-unmanned aerial vehicle system task planning method, in particular to a heterogeneous multi-unmanned aerial vehicle system cooperative reconnaissance mission self-organization method. Background technique [0002] Unmanned Aerial Vehicle (UAV) is an unmanned aerial vehicle that does not carry a driver and can fly remotely or autonomously. Compared with manned aircraft, in addition to advantages in speed, size and maneuverability, UAV has the characteristics of no casualties, low life cycle cost, continuous combat and strong combat resistance. With the development of UAVs, in the next five years or so, UAVs will be active in air suppression and strike operations. [0003] According to the OODA (Observe-Orient-Decide-Act) model, the U.S. Air Force Research Laboratory divides the autonomous control capabilities of UAVs into 10 levels: remote guidance, real-time fault diagnosis, fault self-repair and flight environment ada...

AI Technical Summary

Problems solved by technology

However, at present, the task autonomous planning method for the heterogeneous multi-UAV system composed of reconnaissance and reconnaissance UAVs is still rare.

A Chinese patent (publication number 105302153A, publication date is 2016-2-3) discloses a "planning method for heterogeneous multi-UAV cooperative reconnaissance mission", which uses heterogeneous multi-colony ant colony algorithm to solve The task planning model can solve the problem of multi-UAV multi-task assignment under complex constraints, but this method is for the overall task planning of the offline situation, and the adaptability to the dynamic environment is not strong

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