Route Optimization in Mission Planning for Hybrid DRONE+VEHICLE Transport Systems

Abstract

Introduction. In the context of modern technologies and the widespread use of unmanned aerial vehicles (UAVs) in various fields of activity, the study of optimizing their mission planning becomes increasingly relevant. This is particularly true for hybrid systems where UAVs are integrated with ground transportation ("Drone+Vehicle"). The article deals with the aspects of optimizing the mission routes of a drone that can be transported by a specialized vehicle, performing reconnaissance or maintenance missions for the presented targets. A mathematical model has been developed that allows integrating various planning stages, including determining the direction of the vehicle based on the data obtained during the drone's mission. The purpose of the paper is development and application of mathematical and software-algorithmic tools, in particular, based on the ideas of swarm intelligence, in planning operations for the inspection or maintenance of a given set of objects using hybrid systems "Drone+Vehicle". Results. A mathematical model of the problem of routing hybrid systems of the "Drone+Vehicle" type has been formed. Greedy type algorithms, deterministic local search and ant colony optimization (ACO) to solve the problem are proposed, implemented and analyzed. A computational experiment has been conducted to demonstrate the advantages of the AMC algorithm in terms of speed and efficiency, even for problems of high dimensionality. Conclusions. The proposed approach allows to cover several stages of planning the mission of a hybrid "Drone+Vehicle" system with an aggregated mathematical model. The developed mathematical model also covers the problem of choosing the direction of further movement of a vehicle located in a certain place, depending on the analysis of the results of the inspection of specified targets that may contain objects for inspection or maintenance. To solve the formulated combinatorial optimization problem, greedy type, deterministic local search, and OMC algorithms have been developed. The results of the computational experiment demonstrate the superiority of the OMC algorithm over the combined "greedy + deterministic local search" algorithm. An important future direction of research is the development and application of routing models and algorithms that take into account the obstacles present on the ground. The developed mathematical apparatus allows to move on to consider problems in which the locations of the vehicle's base on the route are not specified but are determined depending on the configuration of the targets. Keywords: unmanned aerial vehicles, hybrid systems, mission planning, route optimization, mathematcal modeling, ant colony optimization, logistics.