The Close Proximity Mothership Risky Team Orienteering Problem
Translated title
The Close Proximity Mothership Risky Team Orienteering Problem: Leveraging Close Proximity Motherships for Improved Fault Tolerance
Author
Nørbjerg, Martin Sig
Term
4. term
Education
Publication year
2026
Submitted on
2026-08-03
Abstract
This thesis examines how to plan remote sensing missions using a group of heterogeneous unmanned aerial vehicles (UAVs) together with a single nearby support aircraft, called a mothership. The mothership acts as a communication relay between the UAVs and the human operator and can forward collected data when it flies close enough to a UAV. The UAVs are assumed to operate in an area with known, fixed operational risks and limited communication coverage. As a result, the planning must consider that information may be lost if one or more UAVs fail or become non-operational during the mission. The planning problem is modeled as an extension of the so‑called Team Orienteering Problem, a mathematical framework used to choose the most valuable tasks under limited resources. Algorithms are proposed to solve this planning problem, and their performance is evaluated using extensive computational experiments and statistical analysis.
Denne afhandling undersøger, hvordan man bedst planlægger dataindsamling (fjernmåling) med en gruppe ubemandede luftfartøjer (UAV’er), der er forskellige fra hinanden, og én bemandet eller større enhed, kaldet et ‘mothership’, som flyver tæt på dem. Mothershippen fungerer som bindeled mellem UAV’erne og operatøren og kan videresende data, når den kommer tilstrækkelig tæt på en UAV. Det antages, at UAV’erne arbejder i et område med kendte, faste risici og begrænsede kommunikationsmuligheder. Derfor skal planlægningen tage højde for, at information kan gå tabt, hvis én eller flere UAV’er går i stykker eller stopper med at fungere undervejs. Planlægningsproblemet modelleres som en udvidelse af det såkaldte Team Orienteering Problem, en matematisk model der bruges til at vælge de mest værdifulde opgaver under begrænsede ressourcer. Der foreslås algoritmer til at løse dette planlægningsproblem, og deres ydeevne vurderes gennem omfattende beregninger og statistisk analyse.
[This abstract has been rewritten with the help of AI based on the project's original abstract]
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