No description available.

Bio-inspired swarming behaviors provide an effective decentralized way of coordinating robot teams. However, as robot swarms increase in size, bandwidth and time constraints limit the number of agents a human can communicate with and control. To facilitate scalable human interaction with large robot swarms it is desirable to monitor and influence the collective behavior of the entire swarm through limited interactions with a small subset of agents. However, it is also desirable to avoid situations where a small number of agent failures can adversely affect the collective behavior of the swarm. We present a bio-inspired model of swarming that exhibits distinct collective behaviors and affords limited human interaction to estimate and influence these collective behaviors. Using a simple naive Bayes classifier, we show that the global behavior of a swarm can be detected with high accuracy by sampling local information from a small number of agents. We also show that adding a bio-inspired form of quorum sensing to a swarm increases the scalability of human-swarm interactions and also provides an adjustable threshold on the swarm's vulnerability to agent failures.

In this thesis we propose methods and metrics for human interaction with bio-inspired robot teams. We refine the concept of a stakeholder and demonstrate how a human can use stakeholders to lead a swarm as well as switch the swarm between different collective behaviors. We extend the human interaction metrics of interaction time and interaction effort presented in [1] to swarm systems and introduce the concept of interaction effort. These metrics allow us to understand how well the system performs under human influence. We employ systems theory to estimate these metrics, which is useful because this can be done without performing user studies.

When working with a complex AI or robotics system in a specific application, users often need to incorporate their special domain knowledge into the autonomous system. Such needs call for the ability to manage autonomy. However, managing autonomy can be a difficult task because the internal mechanisms and algorithms of the autonomous components may be beyond the users' understanding. We propose an approach where users manage autonomy indirectly by managing information provided to the intelligent system hierarchically at three different temporal scales: strategic, between-episodes, and within-episode. Information management tools at multiple temporal scales allow users to influence the autonomous behaviors of the system without the need for tedious direct/manual control. Information fed to the system can be in the forms of areas of focus, representations of task difficulty, and the amount of autonomy desired. We apply this approach to using an Unmanned Aerial Vehicle (UAV) to support Wilderness Search and Rescue (WiSAR). This dissertation presents autonomous algorithms/components and autonomy management tools/interfaces we designed at different temporal scales, and provides evidence that the approach improves the performance of the human-robot team and the experience of the human partner.

Joint attention behaviours play a central role in natural and believable human-robot interactions. This research presents the design decisions of a semi-autonomous joint attention robotic system, together with the evaluation of its effectiveness and perceived social presence across different cognitive ability groups. For this purpose, two different studies were carried out: first with adults, and then with children between 10 and 12 years-old. The overall results for both studies reflect a system that is perceived as socially present and engaging which can successfully establish joint attention with the participants. When comparing the performance results between the two groups, children achieved higher joint attention scores and reported a higher level of enjoyment and helpfulness in the interaction. Furthermore, a detailed literature review on robot-assisted therapies for children with autism spectrum disorders is presented, focusing on the development of joint attention skills. The children’s positive interaction results from the studies, together with state-of-the-art research therapies and the input from an autism therapist, guided the author to elaborate some design guidelines for a robotic system to assist in joint attention focused autism therapies. / Gemensam uppmärksamhet (joint attention) spelar en central roll i naturliga och trovärdiga interaktioner mellan människor och robotar. Denna rapport presenterar designbesluten av ett semi-autonomt joint attentionsystem för sociala robotar, samt en utvärdering av dess effektivitet och hur grupper med olika kognitiv förmåga upplever dess sociala närvaro. För detta ändamål genomfördes två olika studier: förstmed vuxna, och sedan med barn mellan 10 och 12 år gamla. De övergripande resultaten för båda studierna visar att vi byggt ett system som uppfattas som socialt närvarande och engagerande, och som framgångsrikt kan skapa gemensam uppmärksamhet med deltagarna. När man jämför resultaten mellan de två grupperna, finner man att barn gav högre gemensam uppmärksamhetsresultat de rapporterade att de fick mer hjälp av och tyckte bättre om roboten som använda det utvecklade systemet för joint attention. Vidare presenteras en detaljerad litteraturstudie om robotassisterade terapier för barn med autismspektrumsjukdomar, med fokus på utveckling av gemensamma uppmärksamhetsförmågor. Barnens positiva interaktioner med en robot som hade det utvecklade joint attentionsystemet, tillsammans med litteraturstudier om forskningsterapier och en intervju med en autismterapeut, vägledde författaren attutarbeta riktlinjer för hur man ska designa sociala robotar som har till syfte att användas vid terapi som syftar till att förbättra autistiska barns förmåga att kommunicera icke-verbalt och förstå vad den de talar med fokuserar på.

This thesis worked towards answering the following question: Where, if at all, do the beliefs and behaviors associated with interacting with a nonhuman agent deviate from how we treat a human? This was done by exploring the inter-related fields of Human-Computer and Human-Robot Interaction in the literature review, viewing them through the theoretical lens of anthropomorphism. A study was performed which looked at how 104 participants would attribute blame in a robotic surgery scenario, as detailed in a vignette. A majority of results were statistically non-significant, however, some results emerged which may imply a diffusion of responsibility in human-robot collaboration scenarios.

Unmanned ground vehicles (UGVs) can be used in the military to mitigate risks taken by soldiers as well as to provide solutions to physically demanding, dull or dangerous tasks. While there are benefits of using UGVs, there are also needs and limitations that come with them. This thesis explores what the end user, a light infantry battalion in the Swedish Armed Forces, wants in terms of functions for a UGV designed for military oper- ations in urban terrain. This was done through a explorative case study with focus groups, where soldiers and officers from the 31st Ranger Battalion got to use two different UGV prototypes to complete tasks. This was followed by semi structured group discussions, where needs, limitations and requirements were explored. The collected data was then analysed by a thematic analysis approach. The results from the thematic analysis found several reoccurring opinions regarding requirements from the focus groups. The requirements were grouped into four categories; (1) Speed, (2) Use cases, (3) Image generating sensors, and (4) Autonomous functions. In conclusion, this thesis identified a total of 13 requirements within the four categories. To summarise, these requirements imply that a UGV meant for military operations in urban terrain must be able to keep up with sprinting soldiers, provide visual cover, be able to interact with nearby objects, have several high quality sensors and robust autonomous functions, allowing the soldiers to focus on other things than controlling the UGV.

No description available.

The United States Army has moved into the 21st century with the intent of redesigning not only the force structure but also the methods by which we will fight and win our nation's wars. Fundamental in this restructuring is the development of the Future Combat Systems (FCS). In an effort to minimize exposure of front line soldiers the future Army will utilize unmanned assets for both information gathering and when necessary engagements. Yet this must be done judiciously, as the bandwidth for net-centric warfare is limited. The implication is that the FCS must be designed to leverage bandwidth in a manner that does not overtax computational resources. In this study alternatives for improving human performance during operation of teleoperated and semi-autonomous robots were examined. It was predicted that when operating both types of robots, frame delay of the semi-autonomous robot would improve performance because it would allow operators to concentrate on the constant workload imposed by the teleoperated while only allocating resources to the semi-autonomous during critical tasks. An additional prediction was that operators with high spatial ability would perform better than those with low spatial ability, especially when operating an aerial vehicle. The results can not confirm that frame delay has a positive effect on operator performance, though power may have been an issue, but clearly show that spatial ability is a strong predictor of performance on robotic asset control, particularly with aerial vehicles. In operating the UAV, the high spatial group was, on average, 30% faster, lazed 12% more targets, and made 43% more location reports than the low spatial group. The implications of this study indicate that system design should judiciously manage workload and capitalize on individual ability to improve performance and are relevant to system designers, especially in the military community.

The purpose of this research was to examine operator workload and performance in a high risk, multi-task environment. Specifically, the research examined if a gunner of a Future Combat System, such as a Mounted Combat System, could effectively detect targets in the immediate environment while concurrently operating robotic assets in a remote environment. It also analyzed possible effects of individual difference factors, such as spatial ability and attentional control, on operator performance and workload. The experimental conditions included a gunner baseline and concurrent task conditions where participants simultaneously performed gunnery tasks and one of the following tasks: monitor an unmanned ground vehicle (UGV) via a video feed (Monitor), manage a semi-autonomous UGV, and teleoperate a UGV (Teleop). The analysis showed that the asset condition significantly impacted gunnery performance with the gunner baseline having the highest number of targets detected (M = 13.600 , SD = 2.353), and concurrent Teleop condition the lowest (M = 9.325 , SD = 2.424). The research also found that high spatial ability participants tended to detect more targets than low spatial ability participants. Robotic task performance was also affect by the asset condition. The results showed that the robotic target detection rate was lower for the concurrent task conditions. A significant difference was seen between the UGV-baseline (80.1%) when participants performed UGV tasks only and UGV-concurrent conditions (67.5%) when the participants performed UGV tasks concurrently with gunnery tasks. Overall, this study revealed that there were performance decrements for the gunnery tasks as well as the robotic tasks when the tasks were performed concurrently.