PLANNING OF GROUP ACTIONS OF UNMANNED AERIAL VEHICLES IN A DISTRIBUTED INTELLIGENT CONTROL SYSTEM

Abstract

The actual progress achieved in the field of unmanned flying vehicles makes it possible to use Unmanned Aerial Vehicles (UAVs) to solve various tasks in the civil and defense areas. As a result, UAV groups require scheduling of their actions at various stages of their mission. In their previous publications the authors suggested the architecture of a distributed intellectual control system for the implementation of UAVs collective actions. It was demonstrated, that one of the most important functions of such an intellectual control system is the so called pre-flight scheduling of UAVs actions within a group. The article discusses the approaches to the implementation of algorithms that ensure the pre-flight scheduling of UAVs actions in the frames of a distributed intellectual control system, summarizing the current state of research in this area as well as the authors’ own results. The most important task to be solved at the stage of a UAV group’s actions pre-flight scheduling is to determine the types of UAVs involved in the implementation of the target task. An approximate solution to this problem is proposed, basing on the use of analytical probabilistic models to evaluate the group actions efficiency. The use of such models makes it possible to determine the optimal number of UAVs in the group, to justify the requirements for their survivability, the characteristics of both on-board optoelectronic means and weapons. Algorithms for scheduling the UAV group actions are described, which are based on both mathematical models and formalized criteria for evaluation of the collective actions efficiency. In this case, the planning task, as a rule, can be reduced to the problem of integer linear or nonlinear programming. The possibility of using artificial intelligence technologies for the purposes of the UAV group actions scheduling is discussed. Special attention is paid to the problem of planning the UAV group actions within the framework of a decentralized strategy of flock management.