This dissertation presents a suite of design tools for multi-agent systems that address three main areas: network design, decentralized controller generation, and the synthesis of decentralized control strategies by combining individual decentralized controllers. First, a new metric for quantifying heterogeneity in multi-agent systems is presented based on combining different notions of entropy, and is shown to overcome the drawbacks associated with existing diversity metrics in various scientific fields. Moreover, a new method of controlling multi-agent networks through the single-leader network paradigm is presented where by directly exploiting the homogeneity of agent capabilities, a network which is not completely controllable can be driven closer to a desired target configuration than by using traditional control techniques. An algorithm is presented for generating decentralized control laws that allow for agents to best satisfy a desired global objective, while taking into account network topological constraints and limitations on how agents can compute their control signals. Then, a scripting tool is developed to aid in specifying sequences of decentralized controllers to be executed consecutively, while helping ensure that the required network topological requirements needed for each controller to execute properly are maintained throughout mode switches. Finally, the underlying concepts behind the developed tools are showcased in three example applications: distributed merging and spacing for heterogeneous aircraft during terminal approaches, collaborative multi-UAV convoy protection in dynamic environments, and an educational tool used to teach a graduate-level networked controls course at the Georgia Institute of Technology.

This research investigated the Mission Specialist role in micro unmanned aerial systems (mUAS) and was informed by human-robot interaction (HRI) and technology findings, resulting in the design of an interface that increased the individual performance of 26 untrained CBRN (chemical, biological, radiological, nuclear) responders during two field studies, and yielded formative observations for HRI in mUAS. Findings from the HRI literature suggested a Mission Specialist requires a role-specific interface that shares visual common ground with the Pilot role and allows active control of the unmanned aerial vehicle (UAV) payload camera. Current interaction technology prohibits this as responders view the same interface as the Pilot and give verbal directions for navigation and payload control. A review of interaction principles resulted in a synthesis of five design guidelines and a system architecture that were used to implement a Mission Specialist interface on an Apple iPad. The Shared Roles Model was used to model the mUAS human-robot team using three formal role descriptions synthesized from the literature (Flight Director, Pilot, and Mission Specialist). The Mission Specialist interface was evaluated through two separate field studies involving 26 CBRN experts who did not have mUAS experience. The studies consisted of 52 mission trials to surveil, evaluate, and capture imagery of a chemical train derailment incident staged at Disaster City. Results from the experimental study showed that when a Mission Specialist was able to actively control the UAV payload camera and verbally coordinate with the Pilot, greater role empowerment (confidence, comfort, and perceived best individual and team performance) was reported by a majority of participants for similar tasks; thus, a role-specific interface is preferred and should be used by untrained responders instead of viewing the same interface as the Pilot in mUAS. Formative observations made during this research suggested: i) establishing common ground in mUAS is both verbal and visual, ii) type of coordination (active or passive) preferred by the Mission Specialist is affected by command-level experience and perceived responsibility for the robot, and iii) a separate Pilot role is necessary regardless of preferred coordination type in mUAS. This research is of importance to HRI and CBRN researchers and practitioners, as well as those in the fields of robotics, human-computer interaction, and artificial intelligence, because it found that a human Pilot role is necessary for assistance and understanding, and that there are hidden dependencies in the human-robot team that affect Mission Specialist performance.

published_or_final_version / Architecture / Master / Master of Architecture

Le système Cospas-Sarsat est un système de recherche et de sauvetage à l’échelle mondiale qui fonctionne à l’aide de satellites en orbite basse et de satellites en orbite géostationnaire. La constellation de satellites actuelle est en cours de remplacement par des satellites en orbite moyenne qui couvrent de plus grandes zones de la surface de la Terre permettant des alertes quasi instantanées. L’objectif de cette thèse est d’étudier les performances de localisation de ce nouveau système, qui a été nommé système MEOSAR (Medium Earth Orbit Search and Rescue). Nous étudions d’abord la qualité de la liaison entre la balise de détresse, le satellite, et la station de réception au sol à l’aide d’un bilan de liaison. Ensuite, nous proposons un modèle de signal basé sur des fonctions sigmoïdes afin de modéliser les transitions douces du signal de détresse. Pour ce modèle, les performances de localisation (en terme de bornes de Cramér-Rao et de la variance d’estimateurs) sont étudiées pour l’estimation de position de la balise, et pour l’estimation de différents paramètres, y compris le temps d’arrivée, la fréquence d’arrivée et la durée du symbole. Ensuite, nous étudions l’impact de l’ajout d’information a priori sur la période symbole et sur le temps de montée du signal, qui proviennent des tolérances autorisées sur les spécifications des balises de détresse. Nous étudions également l’erreur introduite par l’ajout de bruit de phase caractéristique des oscillateurs des balises, et nous considérons l’amélioration de l’estimation de position en prenant en compte les multiples émissions de la balise de détresse. Finalement, les performances de localisation du système MEOSAR sont données pour les balises de détresse de deuxième génération, qui sont en cours de développement, et qui utilisent une modulation avec étalement de spectre. / Cospas-Sarsat is an international search and rescue system that operates using low-orbit satellites and geostationary satellites. The current satellite constellation is being replaced by medium Earth orbit satellites which will cover larger areas of the surface of the Earth, permitting almost instantaneous alerts. The objective of this thesis is to study the localization performance of this new system, named MEOSAR (Medium Earth Orbit Search and Rescue). We first study the quality of the link between the beacon, the satellite and the ground receiving station through a link budget. Then, we propose a signal model based on sigmoidal functions to model the smooth transitions of the distress signal. For this model, the localization performance (in terms of Cramér-Rao bounds and estimator variances) is studied for the estimation of the beacon position and for different parameters including the time of arrival, the frequency of arrival and the symbol width. Then, we study the impact of adding prior information on the symbol width and the signal rise time, which are constructed from the allowed tolerances on the beacon specifications. We also investigate the error introduced by the addition of oscillator phase noise, and we show how the position estimation can be improved by taking into account multiple emissions of the beacon. Finally, the localization performance of the MEOSAR system is studied for second generation beacons, which are being developed using spread spectrum modulation.

This thesis studies the performance of search dogs trained to locate the live scent (live only dogs) and compares their performance to that of search dogs trained to locate either live or cadaver scent depending on the verbal cue given by the handler (cross-trained dogs).

This thesis focuses on vision-based detection and observation of small, slow-moving targets using a gimballed fixed-wing unmanned aircraft system (UAS). Generally, visual tracking algorithms are tuned to detect motion of relatively large objects in the scene with noticeably significant motion; therefore, applications such as high-altitude visual searches for human motion often ignore target motion as noise. Furthermore, after a target is identified, arbitrary maneuvers for transitioning to overhead orbits for better observation may result in temporary or permanent loss of target visibility. We present guidelines for tuning parameters of the Visual Multiple Target Tracking (Visual MTT) algorithm to enhance its detection capabilities for very small, slow-moving targets in high-resolution images. We show that the tuning approach is able to detect walking motion of a human described by 10-15 pixels from high altitudes. An algorithm is then presented for defining rotational bounds on the controllable degrees of freedom of an aircraft and gimballed camera system for maintaining visibility of a known ground target. Critical rotations associated with the fastest loss or acquisition of target visibility are also defined. The accuracy of these bounds are demonstrated in simulation and simple applications of the algorithm are described for UAS. We also present a path planning and control framework for defining and following both dynamically and visually feasibly transition trajectories from an arbitrary point to an orbit over a known target for further observation. We demonstrate the effectiveness of this framework in maintaining constant target visibility while transitioning to the intended orbit as well as in transitioning to a lower altitude orbit for more detailed visual analysis of the intended target.

This essay aims to study which competencies, motives, and conditions that volunteer rescuers in the Aland Islands Sea Rescue Society consider important. An underlying aim is to investigate the importance of volunteer maritime rescue for the members themselves and for the local community from a maritime rescue and safety perspective. This is accomplished by examining qualitative ethnographic interviews through Discourse Theory. The essay explores what work tasks and competencies (such as knowledge and skills) volunteer maritime rescuers consider important and what strategies they use to acquire competencies. It even explores what motives they indicate as important for becoming involved as a volunteer rescuer and what conditions enable or limit their participation in the Sea Rescue Society The essay even explores why voluntary lifeguards need to exist in society and how com it exists.    The results show that each volunteer rescuer has an individual motive that keeps them engaged. The volunteer rescuers need a general competence but may also have special competence in maritime rescue practices. The living, health, work, and family situation of the volunteer rescuers are conditions that enable or limit their participation in the Sea Rescue Society. The finances of the maritime rescue service may also be a condition enabling or limiting.

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)

Development of technical and methodological support has been identified as a key area for future research and development within the search and rescue (SAR) of missing persons in Sweden. Previous studies have demonstrated the feasibility of using Wi-Fi signals to locate a missing person, and also developed hardware and software for SAR systems based around Unmanned Aerial Vehicles (UAV). This thesis empirically studies the actual transmission behaviour of three non-connected smartphones over Wi-Fi and Bluetooth Low Energy (BLE), under the assumption that a missing person could be carrying a smartphone with them. Furthermore, the thesis studies the range of relatively low-cost off-the-shelf Wi-Fi and BLE devices in a “best-case” scenario with free line-of-sight and a slightly elevated transmitter. Finally, the thesis studies how antennas and other factors impact range. The results show that all of the tested smartphones transmit so called Wi-Fi probe packets when Wi-Fi is enabled. Though, these probes are, in most cases, transmitted during short events separated by intervals of 5 to 9 minutes, depending on the smartphone. Furthermore, in most cases only a few packets are transmitted in each event. The interval lengths and scarce packets imply significant limitations to the usability of Wi-Fi in SAR. BLE broadcast transmissions, on the other hand, require that some kind of advertising service is enabled, but are transmitted at least every 10.5 seconds, due to the strict regulation in the Bluetooth standard. This was also demonstrated with the studied iPhone that continuously transmitted BLE packets every two seconds. Roughly 60 % of the smartphones in use in Sweden today are Apple devices, thus a significant portion of all the smartphones in use are likely to be detectable via BLE within brief time intervals. In the tested “best-case” scenario with free line-of-sight and a slightly elevated transmitter, at least 80 % of the transmitted Wi-Fi probes are detected up to 500-600 meters with a maximum range, with sparsely detected packets, of up to about 700 meters. Correspondingly, at least 80 % of the BLE advertisements are detected up to 60-110 meters with occasional packets detected up to a maximum range of approximately 210 meters. However, the ranges for both Wi-Fi and BLE are highly dependent on the receiving device. In addition to the receiver device and standard, it is found that the transmission power and antenna directivity of the transmitter have a potentially substantial impact on the range with a possible combined difference in directed transmitted power of up to 49 dB. This corresponds to the difference between a strong signal and no detected signal at all. In summary, there are clear differences between the properties of BLE and Wi-Fi, both in terms of range and how different devices transmit broadcast packets (transmission behaviour), resulting in both having advantages and disadvantages in SAR applications. There are also a number of factors on the transmitter and receiver devices that could have considerable impact on the possibility of detecting a device. Concludingly, both BLE and Wi-Fi could fill a role in SAR of missing persons, but both also have major drawbacks that are severely limiting their possible use. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

Unmanned Aerial Vehicles (UAVs) could play an important role in Search &amp; Rescue (SAR) operations thanks to their ability to cover large, remote, or inaccessible search areas quickly without putting any personnel at risk. As UAVs are becoming autonomous, the problem of identifying humans in a variety of conditions can be solved with computer vision implemented with a thermal camera. In some cases, it would be necessary to operate with one or several small, agile UAVs to search for people in dense and narrow environments, where flying at a high altitude is not a viable option. This could for example be in a forest, cave, or a collapsed building. A small UAV has a limitation in carrying capacity, which is why this thesis aimed to propose a lightweight thermal solution for human detection that could be applied on a small SAR-UAV for operation in dense environments. The solution included a Raspberry Pi 4 and a FLIR Lepton 3.5 thermal camera in terms of hardware, which were mainly chosen thanks to their small footprint regarding size and weight, while also fitting within budget restrictions. In terms of object detection software, EfficentDet-Lite0 in TensorFlow Lite format was incorporated thanks to good balance between speed, accuracy, and resource usage. An own dataset of thermal images was collected and trained upon. The objective was to characterize disturbances and challenges this solution might face during a UAV SAR-operation in dense environments, as well as to measure how the performance of the proposed platform varied with increasing amount of environmental coverage of a human. This was solved by conducting a literature study, an experiment in a replicated dense environment and through observations of the system behavior combined with analysis of the measurements. Disturbances that affect a thermal camera in use for human detection were found to be a mixture of objective and subjective parameters, which formed a base of what type of phenomena to include in a diverse thermal dataset. The results from the experiment showed that stable and reliable detection performance can be expected up to 75% vegetational coverage of a human. When fully covered, the solution was not reliable when trained on the dataset used in this thesis. / Obemannade drönare (UAVs) kan spela en viktig roll i sök- och räddningsuppdrag (SAR) tack vare deras förmåga att snabbt täcka stora, avlägsna eller otillgängliga sökområden utan att utsätta personal för risker. För autonoma UAVs kan problemet med att identifiera människor i en mängd olika förhållanden lösas med datorseende implementerat tillsammans med en värmekamera. I vissa fall kan det vara nödvändigt att operera med en eller flera små, smidiga UAVs för att söka efter människor i täta och trånga miljöer, där flygning på hög höjd inte är ett genomförbart alternativ. Det kan till exempel vara i en skog, grotta eller i en kollapsad byggnad. En liten UAV har begränsad bärförmåga, vilket är varför denna avhandling syftade till att föreslå en lättviktslösning för mänsklig detektering med värmekamera som skulle kunna appliceras på en liten SAR-UAV för drift i täta miljöer. Lösningen inkluderade Raspberry Pi 4 och en FLIR Lepton 3.5 värmekamera gällande hårdvara, tack vare liten formfaktor och liten vikt, samtidigt som de passade inom budgetramen. Gällande detekterings-mjukvara användes EfficentDet-Lite0 i TensorFlow Lite-format tack vare en bra balans mellan hastighet, noggrannhet och resursanvändning. En egen uppsättning av värmebilder samlades in och tränades på. Målet var att identifiera vilka störningar och utmaningar som denna lösning kan påträffa under en sökoperation med UAVs i täta miljöer, samt att mäta hur prestandan för den föreslagna plattformen varierade när täckningsgraden av en människa ökar p.g.a. omgivningen. Detta löstes genom att genomföra en litteraturstudie, ett experiment i en replikerad tät miljö och genom observationer av systemets beteende kombinerat med analys av mätningarna. Störningar som påverkar en värmekamera som används för mänsklig detektion visade sig vara en blandning av objektiva och subjektiva parametrar, vilka utgjorde en bas för vilka typer av fenomen som skulle inkluderas i en mångsidig kollektion med värmebilder. Resultaten från experimentet visade att stabil och pålitlig detekteringsprestanda kan förväntas upp till 75% täckningsgrad av en människa p.g.a. vegetation. När människan var helt täckt var lösningen inte tillförlitlig när den var tränad på kollektionen som användes i denna avhandling.