Spacecraft formations offer more powerful and robust space system architectures than single spacecraft systems. Investigations into the dynamics and control of spacecraft formations are vital for the development and design of future successful space missions. The problem of controlling the attitude of a formation of spacecraft is investigated. The spacecraft formation is modelled as a distributed system, where the individual spacecraft's attitude control systems are the local control agents. A decentralized attitude controller utilizing behavior-based control is developed. The global stability of the controller is proven using Lyaponuv stability theory. Convergence of the attitude controller is proven through the use of an invariance argument. The attitude controller's stability and convergence characteristics are investigated further through numeric simulation of the attitude dynamics of the spacecraft formation. / Master of Science

This quasi-experimental field study examined the efficacy of a safety self-management intervention to increase safety-related work practices in mining operations. A total of 15 male miners participated in the study while engaging in their normal work practices at the Virginia Tech Quarry, located in Blacksburg, Virginia. The study had two groups, Feedforward (n=8)--participants self-recorded their intentions to engage in specific percentages of safety-related work behaviors before starting their shift for the day, and Feedback (n=7)--participants self-recorded their percentages of safety-related work behaviors after their shift for the day. After a seven-week Baseline, miners participated in a safety training presentation. Immediately following this training, participants from each group were instructed to complete one self-monitoring form each day on their self-intentions (Feedforward) or actual (Feedback) safety performance for four weeks. Participants were paid $1.00 for each completed self-monitoring form. All completed forms were entered into a raffle for a cash prize of $50.00 at the end of the Intervention phase. During Withdrawal (four weeks) miners did not complete any self-monitoring forms. Trained research assistants made a total of 10, 905 obtrusive behavioral observations on three target behaviors (ear plugs, dust mask, and safety glasses) and five non-target behaviors (gloves, hard hat, boots, knee position during lifts, body position during lifts) across phases. Results showed the safety self-management intervention significantly increased safety performance across both target and non-target behaviors during the Intervention phase. / Master of Science

Pedestrian crowd simulation explores crowd behaviors in virtual environments. It is extensively studied in many areas, such as safety and civil engineering, transportation, social science, entertainment industry and so on. As a common phenomenon in pedestrian crowds, grouping can play important roles in crowd behaviors. To achieve more realistic simulations, it is important to support group modeling in crowd behaviors. Nevertheless, group modeling is still an open and challenging problem. The influence of groups on the dynamics of crowd movement has not been incorporated into most existing crowd models because of the complexity nature of social groups. This research develops a framework for group modeling in agent-based pedestrian crowd simulations. The framework includes multiple layers that support a systematic approach for modeling social groups in pedestrian crowd simulations. These layers include a simulation engine layer that provides efficient simulation engines to simulate the crowd model; a behavior-based agent modeling layers that supports developing agent models using the developed BehaviorSim simulation software; a group modeling layer that provides a well-defined way to model inter-group relationships and intra-group connections among pedestrian agents in a crowd; and finally a context modeling layer that allows users to incorporate various social and psychological models into the study of social groups in pedestrian crowd. Each layer utilizes the layer below it to fulfill its functionality, and together these layers provide an integrated framework for supporting group modeling in pedestrian crowd simulations. To our knowledge this work is the first one to focus on a systematic group modeling approach for pedestrian crowd simulations. This systematic modeling approach allows users to create social group simulation models in a well-defined way for studying the effect of social and psychological factors on crowd’s grouping behavior. To demonstrate the capability of the group modeling framework, we developed an application of dynamic grouping for pedestrian crowd simulations.

Robotic science can be defined as a modern multi-disciplinary branch of science, which hosts many technological elements with a huge theoretic base. From electrical and electronics engineering point of view, construction of intelligent agents that produce and/or collects information by interacting the surrounding environment and that can achieve some goal via learning, is investigated in robotic science. In this scope, behavior-based robotic control has emerged in recent years, which can be defined as a hierarchically higher control mechanism over classical control theory and applications. In this thesis, software which is capable of producing behavior-based control over mobile robots is constructed. Research encapsulates an investigation on behavior-based robotic concept by comparison of different approaches. Although there are numerous commercial and freeware software products for robotics, the number of open source, detail documented software on behavior-based control concept together with easy usage is limited. Aimed to fulfill a necessity in this field, an open source software environment is implemented in which different algorithms and applications can be developed. In order to evaluate the effectiveness and the capabilities of the implemented software, a fully detailed simulation is conducted. This simulation covers multi-behavior coordination concept for a differential drive mobile robot navigating in a collision free path through a target point which is detected by sensors, in an unstructured environment, that robot has no priori information about, in which static and moving obstacles exists. Coordination is accomplished by artificial neural network with back-propagation training algorithm. Behaviors are constructed using fuzzy control concept. Mobile robot has no information about sizes, number of static and/or dynamic obstacles. All the information is gathered by its simulated sensors (proximity, range, vision sensors). Yielded results are given in detail.

Salesforce control systems have been widely studied with numerous constructs mediating the effects on performance. Behavior-based salesforce control (BBC) has often proved to increase performance and recently the Ability, Motivation, Opportunity (AMO) theory has been proposed to cluster the mediators. However, the extant literature is limited regarding implementation of BBC. The purpose of the thesis is twofold: (1) to explore the challenges that emerge during the implementation process of BBC at the Swedish sales organization of a global MedTech company, and (2) to investigate whether these challenges can be explained by prior research. An embedded case study at a MedTech company is used to explore the challenges that emerge while implementing BBC. Both structured and semi-structured interviews were conducted, where the structured part was an operationalization of the conceptual framework adopted from a previous study of a closely related field. The findings suggest that challenges within Process, Purpose and Communication have emerged in the case study, and that they affect AMO constructs. Further, the juxtaposition of our findings with the conceptual framework used and extant literature within BBC and implementation provides for an adaptation of the conceptual framework. This new framework could be used in future research to extend the research within implementation of BBC and possibly validate our findings in other case studies. / Olika metoder för säljstyrning har i bred utsträckning studerats tillsammans med faktorer som påverkar dess effekt på säljarnas prestation. Beteendebaserad säljstyrning (BBC) har ofta visats öka säljarnas prestation, och nyligen har teorin som grundar sig i Förmåga, Motivation och Möjlighet (AMO) föreslagits för att gruppera de påverkande faktorerna. Emellertid är den befintliga litteraturen med fokus på implementationsfasen av BBC begränsad. Syftet med detta examensarbete är tvådelat: (1) att utforska de utmaningar som uppstår vid implementationen av BBC inom den svenska säljorganisationen på ett globalt medicinteknikföretag (MedTech), och (2) att undersöka om utmaningarna kan förklaras med tidigare forskning. Empiriska data från en fallstudie vid ett medicinteknikföretag används för att utforska de utmaningar som uppstår under implementationen av BBC. Datainsamlingen har skett genom både strukturerade och semistrukturerade intervjuer, där resultat från de strukturerade intervjuerna syftade till att undersöka om ett konceptuellt ramverk som presenterats i en tidigare studie inom ett närliggande område kan vara applicerbart i en bredare kontext. De empiriska resultaten från fallstudien visar att utmaningar inom tre teman, Process, Syfte och Kommunikation, uppstod vid implementationen av BBC, samt att de tre temana i sin tur påverkar säljarnas Förmåga, Motivation och Möjlighet (AMO). Baserat på sammansättningen av resultaten från fallstudien och befintlig litteratur inom BBC och implementation föreslås en utvidgning av det konceptuella ramverk som undersökts. Det nya utvidgade ramverket skulle kunna användas i framtida studier för att utöka forskningen inom implementation av BBC, samt möjligen validera resultaten från detta examensarbete genom ytterligare fallstudier.

Simulation is an important tool for prediction and assessment of the behavior of complex systems and situations. The importance of simulation has increased tremendously during the last few decades, mainly because the rapid pace of development in the field of electronics has turned the computer from a costly and obscure piece of equipment to a cheap ubiquitous tool which is now an integral part of our daily lives. While such technological improvements make it easier to analyze well-understood deterministic systems, increase in speed and storage capacity alone are not enough when simulating situations where human beings and their behavior are an integral part of the system being studied. The problem with simulation of intelligent entities is that intelligence is still not well understood and it seems that the field of Artificial Intelligence (AI) has a long way to go before we get computers to think like humans. Behavior-based agent modeling has been proposed in mid-80's as one of the alternatives to the classical AI approach. While used mainly for the control of specialized robotic vehicles with very specific sensory capabilities and limited intelligence, we believe that a behavior-based approach to modeling generic autonomous agents in complex environments can provide promising results. To this end, we are investigating a behavior-based model for controlling groups of collaborating and competing agents in a geographic terrain. In this thesis, we are focusing on scenarios of military nature, where agents can move within the environment and adversaries can eliminate each other through use of weapons. Different aspects of agent behavior like navigation to a goal or staying in group formation, are implemented by distinct behavior modules and the final observed behavior for each agent is an emergent property of the combination of simple behaviors and their interaction with the environment. Our experiments show that while such an approach is quite efficient in terms of computational power, it has some major drawbacks. One of the problems is that reactive behavior-based navigation algorithms are not well suited for environments with complex mobility constraints where they tend to perform much worse than proper path planning. This problem represents an important research question, especially when it is considered that most of the modern military conflicts and operations occur in urban environments. One of the contributions of this thesis is a novel approach to reactive navigation where goals and terrain information are fused based on the idea of transforming a terrain with obstacles into a virtual obstacle-free terrain. Experimental results show that our approach can successfully combine the low run-time computational complexity of reactive methods with the high success rates of classical path planning. Another interesting research problem is how to deal with the unpredictable nature of emergent behavior. It is not uncommon to have situations where an outcome diverges significantly from the intended behavior of the agents due to highly complex nonlinear interactions with other agents or the environment itself. Chances of devising a formal way to predict and avoid such abnormalities are slim at best, mostly because such complex systems tend to be be chaotic in nature. Instead, we focus on detection of deviations through tracking group behavior which is a key component of the total situation awareness capability required by modern technology-oriented and network-centric warfare. We have designed a simple and efficient clustering algorithm for tracking of groups of agent suitable for both spatial and behavioral domain. We also show how to detect certain events of interest based on a temporal analysis of the evolution of discovered clusters.

This report presents a strategy to control multiple collaborative intelligent agents acting in a complex, versatile environment. The proposed method utilizes back-chained behavior trees and 1-to-1 task distribution. The agents claim a task, which prevents other agents in the system to start working on the same task. Backward chaining is an algorithm for generating reactive agents from a set of goals. The method was evaluated in Minecraft with Microsoft’s Project Malmo API. Two different scenarios were considered. In the first one, a group of agents collaborated to build a structure. In the second one, a group of agents collaborated while gathering material. We propose and evaluate three algorithms with different levels of agent-cooperation and complexity (Algorithm 1, Algorithm 2, and Algorithm 3). The evaluation shows that backward chained Behaviour Trees (BTs) works well for multiagent coordination in complex versatile environments and that adding 1-to-1 task distribution increases the efficiency of the agents when completing the experiment tasks. / Rapporten presenterar en metod för styrning av en grupp kollaborativa intelligenta agenter agerande i en komplex dynamisk miljö. Den förslagna metoden använder sig av bakåtkedjade beteendeträd och 1-mot-1 uppgiftsdistribution, där en agent reserverar en uppgift vilket hindrar andra agenter att börja arbeta på samma uppgift. Bakåtkedjning är en metod som möjliggör generering av flexibla agenter utifrån en lista av mål och krav. Metoden utvärderades i två olika scenarion i tv-spelet Minecraft. Agenterna samarbetar i det första scenariot med att bygga en struktur och i det andra scenariot med att samla material. Vi föreslår och utvärderar tre algoritmer med olika nivåer av agentsamarbete och komplexitet (Algoritm 1, Algoritm 2, och Algorithm 3). Utvärderingerarna indikerar att bakåtkedjade beteendeträd fungerar bra för multiagentkoordination i komplexa dynamiska miljöer och att 1-mot-1 uppgiftsdistribution ökar agenternas förmåga att genomföra experimentuppgifterna ytterligare.

This paper presents a bio-inspired approach for optical flow data interpretation based on fuzzy inference decision making for visual mobile robot navigation. The interpretation results of regionally averaged optical flow patterns with pyramid segmentation of the optical flow field deliver fuzzy topological and topographic information of the surrounding environment (topological structure from motion). It allows a topological localization in a global map as well as controlled locomotion (obstacle avoidance, goal seeking) in a changing and dynamic environment. The topological optical flow processing is embedded in a behavior based mobile robot navigation system which uses only a mono-camera as primary navigation sensor. The paper discusses the optical flow processing approach as well as the rule based fuzzy inference algorithms used. The implemented algorithms have been tested successfully with synthetic image data for a first verification and parameter tuning as well as in a real office environment with real image data.

This thesis focuses on developing of a distributed, efficient and fault tolerant multiresolutional architecture for sensor networks. For demonstrative purpose, a powerful simulation environment using 3D environment model has been developed. The robot network is composed of autonomous robots capable of working cooperatively equipped with single typed simple sensor. The developed layered control architecture is hybrid including both subsumption and motor schema control strategies. In this proposed control method, behaviors in different or in same layer are coordinated with an evaluator unit that overcomes the difficulties of subsumption based architectures in terms of behavioral coordination. The final coordination between these layers is achieved cooperatively. We performed many simulation experiments to test robot deployment, search and task execution. It is shown that some important parameters such as target reaching time, energy consumption, and communication range can be optimized if an approximate prior information about the environment is known. Robots executes task based on a task allocation algorithm. Market based auction method is used as a task allocation algorithm with completely different robot fitness evaluation method allowing a distributive problem solving. Six non-linear fitness functions are developed to increase the fairness, and fault tolerance of task allocation. These functions have been tested to represent the successes and failures of robots in a compact form. Performance analyses test results have shown that fairness increases two times more in task allocation when these fitness functions are used, compared to the results existing fitness evaluation methods used in the market based auction algorithms. Moreover, fault tolerance is increased by using fitness functions devoted to failure conditions.

In the U.S., construction accidents remain a significant economic and social problem. Despite recent improvement, the Construction industry, generally, has lagged behind other industries in implementing safety as a total management process for achieving zero accidents and developing a high-performance safety culture. One aspect of this total approach to safety that has frustrated the construction industry the most has been “measurement”, which involves identifying and quantifying the factors that critically influence safe work behaviors. The basic problem attributed is the difficulty in assessing what to measure and how to measure it – particularly the intangible aspects of safety. Without measurement, the notion of continuous improvement is hard to follow. This research was undertaken to develop a strategic framework for the measurement and continuous improvement of total safety in order to achieve and sustain the goal of zero accidents, while improving the quality, productivity and the competitiveness of the construction industry as it moves forward. The research based itself on an integral model of total safety that allowed decomposition of safety into interior and exterior characteristics using a multiattribute analysis technique. Statistical relationships between total safety dimensions and safety performance (measured by safe work behavior) were revealed through a series of latent variables (factors) that describe the total safety environment of a construction organization. A structural equation model (SEM) was estimated for the latent variables to quantify relationships among them and between these total safety determinants and safety performance of a construction organization. The developed SEM constituted a strategic framework for identifying, measuring, and continuously improving safety as a total concern for achieving and sustaining the goal of zero accidents.