Heterogeneous multi-unmanned aerial vehicle system cooperative reconnaissance task self-organization method

Abstract

The invention discloses a self-organizing method for cooperative reconnaissance and strike tasks of heterogeneous multi-unmanned aerial vehicle systems. The invention decomposes the heterogeneous multi-unmanned aerial vehicle system into two isomorphic subsystems, and designs a corresponding cooperation mode between the two subsystems, and the two subsystems respectively perform task planning and cooperate with each other. The present invention further designs a cooperative search task self-organization method and a cooperative reconnaissance and strike autonomous task planning method for the decomposed isomorphic reconnaissance UAV system and the isomorphic reconnaissance UAV system. The collaborative search task self-organization method uses the method based on distributed model predictive control to decompose the problem, and then uses the particle swarm algorithm to solve it. The autonomous mission planning method of collaborative reconnaissance and attack is based on the distributed ant colony algorithm. In the process of constructing the local problem of each UAV, the normal flight mode when no threat is detected and the threat avoidance mode when a threat is detected are designed. Can be better adapted to online requirements.

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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