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1	Human-Multi-Drone Interaction in Search and Rescue Systems under High Cognitive Workload Ahlskog, Johanna January 2024 (has links) Unmanned Aerial Vehicles (UAV), often referred to as drones, have seen increased use in search and rescue (SAR) missions. Traditionally, these missions involve manual control of each drone for aerial surveillance. As UAV autonomy progresses, the next phase in drone technology consists of a shift to autonomous collaborative multi-drone operations, where drones function collectively in swarms. A significant challenge lies in designing user interfaces that can effectively support UAV pilots in their mission without an overload of information from each drone and of their surroundings. This thesis evaluates important human factors, such as situational awareness (SA) and cognitive workload, within complex search and rescue scenarios, with the goal of increasing trust in multi-drone systems through the design and testing of various components. Conducting these user studies aims to generate insights for the future design of multi-drone systems. Two prototypes were developed with a multi-drone user interface, and simulated a stressful search and rescue mission with high cognitive workload. In the second prototype, a heatmap guided UAV pilots based on the lost person model. The prototypes were tested in a conducted user study with experienced UAV pilots in different SAR organizations across Sweden. The results showed variability in SA while monitoring drone swarms, depending on user interface components and SA levels. The prototypes caused significant cognitive workload, slightly reduced in the heatmap-equipped prototype. Furthermore, there was a marginal increase in trust observed in the prototype with the heatmap. Notably, a lack of manual control raised challenges for the majority of participants and many desired features were suggested by participants. These early expert insights can serve as a starting point for future development of multi-drone systems. / The HERD project, supported by the Innovation Fund Denmark for the DIREC project (9142-00001B) Unmanned aerial vehicle human-drone interaction cognitive workload trust search and rescue drone swarm situational awareness multi-drone systems Computer Sciences Datavetenskap (datalogi)
2	Exploring the Interface to Aid the Operator’s Situation Awareness in Supervisory Control of Multiple Drones / Utforska gränssnittet för att hjälpa operatörens situationsmedvetenhet vid övervakningskontroll av flera drönare Sun, Sihan January 2022 (has links) Unmanned Aerial Vehicles (UAVs), commonly called drones, have been applied in manifold fields recently. With the development of UAV autonomy, the next generation of drone applications is moving towards team-based, multi-drone operations. This also promotes the transition of the operator role to the supervisory control of multiple UAVs. Situation awareness (SA) is a significant concept in this aspect to evaluate human performance in complex systems. This thesis work proposes a human-system interface for monitoring multiple autonomous UAVs simultaneously by a single operator, and investigates how to decrease the impact of task switching among different UAVs on the operator’s SA. Tasks in the context of fleet mission control are defined to be of different levels of urgency. Several design strategies have been concluded to address the research question. In conclusion, the usage of similar interface layouts between different tasks is effective to generally decrease the impact of task switching. The alert system with appropriate design is a specific factor in mitigating the impact of task switching towards higher urgency tasks/interfaces. Moreover, the reasonable division of areas of the interface and proper presentation of information by their importance are significant, especially for task switching towards lower urgency tasks/interfaces. / Unmanned Aerial Vehicles (UAV), vanligtvis kallade drönare, har använts i många områden nyligen. Med utvecklingen av UAV-autonomi, går nästa generation av drönarapplikationer mot teambaserad, multi-drönarverksamhet. Detta främjar också övergången av operatörsrollen till övervakande kontroll av flera UAV. Situationsmedvetenhet (SA) är ett betydelsefullt koncept i denna aspekt för att utvärdera mänsklig prestation i komplexa system. Detta examensarbete föreslår ett gränssnitt mellan människa och system för att övervaka flera autonoma UAV:er samtidigt av en enda operatör, och undersöker hur man kan minska effekten av uppgiftsbyte mellan olika UAV:er på operatörens SA. Uppgifter i samband med kontroll av flottans uppdrag definieras till att vara av olika brådskande nivå. Flera designstrategier har tagits fram för att ta itu med forskningsfrågan. Sammanfattningsvis är användningen av liknande gränssnittslayouter mellan olika uppgifter effektivt för att generellt minska effekten av uppgiftsbyte. Varningssystemet med lämplig design är en specifik faktor för att mildra effekterna av uppgiftsbyte mot mer brådskande uppgifter/gränssnitt. Dessutom är den rimliga uppdelningen av områden i gränssnittet och korrekt presentation av information efter deras betydelse betydande, särskilt för uppgiftsbyte mot mindre brådskande uppgifter/gränssnitt. Human-system Interface Ground Control System Multi-drone Supervisory Control Situation Awareness Task Switching Människan-systemgränssnitt markkontrollsystem multidrönare övervakningskontroll situationsmedvetenhet uppgiftsbyte Computer and Information Sciences Data- och informationsvetenskap
3	MULTI-DRONE COLLABORATION FOR SEARCH AND RESCUE MISSIONS Forsslund, Patrik, Monié, Simon January 2021 (has links) Unmanned Aerial Vehicle (UAV), also called drones, are used for Search And Rescue (SAR) missions, mainly in the form of a pilot manoeuvring a single drone. However, the increase in labour to cover larger areas quickly would result in a very high cost and time spent per rescue operation. Therefore, there is a need for an easy to use, low-cost, and highly autonomous swarm of drones for SAR missions where the detection and rescue times are kept to a minimum. In this thesis, a Subsumption-based architecture is proposed, which combines multiple behaviours to create more complex behaviours. An investigation of (1) what are the critical aspects of controlling a swarm of drones, (2) how can a combination of different behavioural algorithms increase the performance of a swarm of drones, and (3) what benchmarks are necessary when evaluating the fitness of the behavioural algorithms. The proposed architecture was simulated in AirSim using the SimpleFlight flight controller through experiments that evaluated the individual layers and missions that simulated real-life scenarios. The results validate the modularity and reliability of the architecture, where the architecture has the potential for improvements in future iterations. For the search area of 400×400meters, the swarm consistently produced an average area coverage of at least 99.917% and found all the missing people in all missions, with the slowest average being 563 seconds. Compared to related work, the result produced similar or better times when scaled to the same proportions and higher area coverage. As comparisons of results in SAR missions can be difficult, the introduction of Active time can serve as a benchmark for others in future swarm performance measurements. robotics drone multi-drone robot drones collaborative collaboration ros robot operating system agent subsumption airsim simulation layer behaviour layers behaviours missing sar search and rescue search rescue task allocation task tasks automation quadrotor quadcopter uav unmanned aerial vehicle unmanned vehicle mission missions experiment experiments navigation suppressor inhibitor Robotics Robotteknik och automation Computer Sciences Datavetenskap (datalogi)

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