Close-enough general routing problem for multiple unmanned aerial vehicles in monitoring missions

TL;DR

This paper introduces a novel two-phase iterative routing approach for multiple UAVs that accounts for neighborhood areas of nodes, improving route efficiency in monitoring missions.

Contribution

It proposes a new close-enough multi-UAV routing problem model and an innovative AILS-VND-SOCP algorithm combining heuristic and optimization techniques.

Findings

The proposed algorithm outperforms existing methods in efficiency.

Incorporating disk neighborhoods improves route quality.

Extensive experiments validate the approach's effectiveness.

Abstract

In this paper, we introduce a close-enough multi-UAV general routing problem (CEMUAVGRP) where a fleet of homogeneous UAVs conduct monitoring tasks containing nodes, each of which has its disk neighborhood, and edges, aiming to minimize the total distance. A two-phase iterative method is proposed, partitioning the CEMUAVGRP into a general routing phase where a satisfactory route including required nodes and edges for each UAV is obtained without considering the disk neighborhoods of required nodes, and a close-enough routing phase where representative points are optimized for each required node in the determined route. To be specific, a variable neighborhood descent (VND) heuristic is proposed for the general routing phase, while a second-order cone programming (SOCP) procedure is applied in the close-enough routing phase. These two phases are performed in an iterative fashion under the framework of an adaptive iterated local search (AILS) algorithm until the predefined termination criteria are satisfied. Extensive experiments and comparative studies are conducted, demonstrating the efficiency of the proposed AILS-VND-SOCP algorithm and the superiority of disk neighborhoods.