Thesis (M.S.)-- Florida State University, 2006. / Advisor: Patrick Hollis, Florida State University, College of Engineering, Dept. of Mechanical Engineering. Title and description from dissertation home page (viewed June 7, 2006). Document formatted into pages; contains xi, 82 pages. Includes bibliographical references.

Thesis (Ph.D.) - University of Queensland, 2006. / Includes bibliography.

Thesis (Ph.D.)--Ohio University, March, 2005. / Title from PDF t.p. Includes bibliographical references (162-166)

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references (leaves 61-68). Also available in electronic version.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 148-153).

L’autonomie d’un robot mobile autonome requiert la réalisation coordonnée de tâches de commande et de perception de l’environnement. Parmi celles-ci, la navigation joue un rôle de pivot dans l’interaction du robot avec son terrain d’évolution. Elle consiste en la détermination de trajectoires réalisables par le robot pour suivre un chemin préétabli, tout en assurant la non collision avec les obstacles, mobiles ou fixes. Pour effectuer cette tâche, notre approche s’appuie sur le modèle cinématique direct du véhicule pour générer des trajectoires admissibles par le robot. En premier lieu, une trajectoire de référence est construite à partir du chemin à suivre. Le problème de navigation est alors modélisé sous la forme d’un problème d’optimisation sous contraintes dont la fonction coût quantifie l’écart entre la trajectoire prédite du robot et la trajectoire de référence. Les obstacles sont intégrés sous forme de contraintes en pénalisant le critère, et sa minimisation détermine la commande optimale à appliquer. Cette navigation par commande prédictive nous permet d’anticiper les mouvements de contournement d’obstacles sur l’horizon de prédiction choisi, tout en gardant une certaine réactivité vis-à-vis de la dynamique des obstacles et du robot. En outre, l’utilisation de familles de trajectoires paramétrées permet de maitriser le comportement du véhicule. / Autonomous robots have to perform both control and perception tasks coordinately. Among these ones, the navigation task is a key in the interaction between the robot and its environment. It consists of determining the trajectories which the robot can follow in order to negotiate correctly around static and dynamic obstacles, assuming that it is programmed to map out its environment and situate itself within that environment. To perform this task, our approach rests on the direct kinematics model of the robot to generate admissible trajectories for the robot. Firstly, a reference trajectory is computed from the reference path provided by a path planer. Then the navigation task is modelized as an optimization under constraints problem, whose the cost function quantify the gap between the reference trajectory and the predicted trajectory of the robot. The obstacles are taken into account as constraints, and the minimization of the resulting cost function determinate the optimal control for the robot on a prediction horizon. This predictive navigation allows the robot to anticipate bi-pass movements on the chosen prediction horizon, Moreover, the behaviour of the robot is mastered by the use of parametered trajectories families.

Imagine the following situation. You give your favorite robot, named Pippi, the task to fetch a parcel that just arrived at your front door. While pushing the parcel back to you, she must travel through a door opening. Unfortunately, the parcel she is pushing is blocking her camera, giving her a hard time to see the door to cross. If she cannot see the door, she cannot safely push the parcel through the door opening. What would you as a human do in a similar situation? Most probably you would ask someone for help, someone to guide you through the door, as we ask for help then we need to park our car in a tight parking spot. Why not let the robots do the same? Why not let robots help each other. Luckily for Pippi, there is another robot, named Emil, vacuum cleaning the floor in the same room. Since Emil can view both Pippi and the door at the same time, he can guide pippi through the door, enabling her to deliver the parcel to you. This work is about societies of autonomous robots in which robots can help each other by offering information-producing functionalities. A functional configuration is a way to allocate and connect functionalities among robots. In general, different configurations can be used to solve the same task, depending on the current situation. For the work on configurations, we have three steps. The first step is to formally define the idea of functional configuration. Second, to show how configurations can be automatically generated and executed. The third step is to address the problem of when and how to change a configuration in response to changing conditions. In this licenciate thesis we report initial work that focus on the two first steps: the third step is subject of future work. We propose a formal definition of functional configurations, and we propose an approach based on artificial intelligence (AI) planning techniques to automatically generate a preferred configuration for a given task, environment, and set of resources. To illustrate these ideas, we describe an experimental system where these are implemented, and show two example of it in which two robots mutually help each other to address tasks. In the first example they help each other to cross a door, and in the second example they carry a bar together.

Personal Digital Assistants (PDAs) have recently become a popular component in mobile robots. This compact processing device with its touch screen, variety of built-in features, wireless technologies and affordability can perform various roles within a robotic system. Applications include low-cost prototype development, rapid prototyping, low-cost humanoid robots, robot control, robot vision systems, algorithm development, human-robot interaction, mobile user interfaces as well as wireless robot communication schemes. Limits on processing power, memory, battery life and screen size impact the usefulness of a PDA in some applications. In addition various implementation strategies exist, each with its own strengths and weaknesses. No comparison of the advantages and disadvantages of the different strategies and resulting architectures exist. This makes it difficult for designers to decide on the best use of a PDA within their mobile robot system. This dissertation examines and compares the available mobile robot architectures. A thorough literature study identifies robot projects using a PDA and examines how the designs incorporate a PDA and what purpose it fulfils within the system it forms part of. The dissertation categorises the architectures according to the role of the PDA within the robot system. The hypothesis is made that using a distributed control system architecture makes optimal use of the rich feature set gained from including a PDA in a robot system’s design and simultaneously overcomes the device’s inherent shortcomings. This architecture is developed into a novel distributed intelligence framework that is supported by a hybrid communications architecture, using two wireless connection schemes. A prototype implementation illustrates the framework and communications architecture in action. Various performance measurements are taken in a test scenario for an office robot. The results indicate that the proposed framework does deliver performance gains and is a viable alternative for future projects in this area.

Autonomous mobile robots (AMRs) use decentralized, AI-driven decision-making processes to providing material handling capabilities in industrial settings. Essay 1 examines how firms organize and engage to mitigate uncertainty during external technology integration (ETI), using an abductive approach with dyadic customer-supplier data to extend prior ETI models by exploring firm engagement, organizational adaptation, and distinct uncertainty types in AMR ETI projects. Essay 2 applies a grounded theory approach to examine AMR integration, using constant comparison and theoretical sampling to develop core categories explaining how suppliers, customers, and users exchange knowledge impacting AMR integration and project performance. Finally, Essay 3 is a conceptual paper examining the importance of end-user adoption by integrating ETI and technology acceptance model (TAM) frameworks, exploring important relationships between managerial interventions, cognitive constructs, user acceptance, and project success in AMR ETIs. As a whole, these essays contribute to the body of knowledge by extending the breadth and depth of current ETI models, emerging a substantive theory of AMR AIU, and extending TAM by grounding managerial interventions and individual cognitive constructs in an AMR context. Managers can use these frameworks to differentiate AMRs and other autonomous collaborative technology from traditional automation, and develop strategies enabling timely and effective AMR implementation.