Designing Reality: A Mapping-Based Participatory Toolkit for Drone Experts Addressing Integration into Critical Infrastructure
Author
Kristensen, Maja Hornbæk
Term
4. Term
Publication year
2025
Submitted on
2025-06-06
Pages
14
Abstract
As drones are increasingly used in critical infrastructure, it is essential that drone specialists and other stakeholders can work together effectively. However, many current methods do not provide a clear way to support meaningful dialogue and shared learning. This thesis develops a scenario-based participatory design (PD) workshop protocol, supported by a domain-specific toolkit, that helps drone experts involve stakeholders in co-design. The approach uses structured scenarios and tangible artifacts (simple, hands-on materials that make ideas concrete) to guide discussion, explore options, and test ideas against real-world conditions. Through iterative evaluation with drone experts and researchers, we show how the workshops improve co-creation, mutual learning, and the alignment of technical solutions with operational realities. The result is a practical and adaptable method for PD in complex, safety-critical contexts that can help produce more context-sensitive and realistic strategies for integrating drones.
I takt med at droner i stigende grad bruges i kritisk infrastruktur, bliver det afgørende, at dronefagfolk og andre interessenter kan samarbejde effektivt. Mange eksisterende metoder giver dog ikke en klar ramme for meningsfuld dialog og fælles læring. Denne afhandling udvikler en scenariobaseret deltagende design (PD) workshop-protokol, understøttet af et domænespecifikt værktøjssæt, som hjælper droneeksperter med at involvere interessenter i samskabelse. Tilgangen bruger strukturerede scenarier og håndgribelige artefakter (enkle, fysiske materialer, der gør idéer konkrete) til at styre diskussionen, udforske muligheder og afprøve idéer mod virkelighedens rammer. Gennem iterativ evaluering med droneeksperter og forskere viser vi, hvordan workshoppen styrker samskabelse, gensidig læring og en bedre kobling mellem teknisk ekspertise og driftsmæssige realiteter. Resultatet er en praktisk og tilpasningsdygtig metode til PD i komplekse, sikkerhedskritiske kontekster, som kan føre til mere kontekstfølsomme og realistiske strategier for integration af droner.
[This apstract has been rewritten with the help of AI based on the project's original abstract]