Thesis (M.S.)--University of Wyoming, 2008. / Title from PDF title page (viewed on Apr. 1, 2010). Includes bibliographical references (p. 69-71).

Thesis (Ph. D.)--Drexel University, 2005. / Includes abstract and vita. Includes bibliographical references (leaves 65-67).

Thesis (Ph. D.)--Duke University, 2008.

Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Includes bibliographical references (p. 246-254).

Electric transmission lines suffer from many hazards, including wind-induced vibrations (WIV), which can lead to fatigue failure of the transmission conductors. Current vibration mitigation methods do not adequately address WIV because they overwhelmingly rely on narrow-band fixed absorbers. A mobile damping robot (MDR) can overcome the limitations of these fixed absorbers by actively transporting them to locations of highest amplitude on the cable; i.e., antinodes. These antinodes are where the absorbers can most efficiently remove energy from the system. While analyses have been performed for vibration absorbers on transmission line conductors, they have not been in the context of a mobile damping robot (MDR). There is a need to investigate the potential impact of the MDR on a transmission line and the resulting implications for the MDR's development. In this thesis, we explore the dynamics of a power line conductor through finite element analysis (FEA) and modal testing. We perform numerical analysis in MATLAB using equations of motion obtained via Hamilton's Principle. We discuss the design and validation of an appropriate test bench and MDR prototype. We also experimentally investigate the ability of the MDR prototype to transport a mass along a conductor to antinode locations. Experimental results indicate that the damping robot is indeed able to navigate to cable locations of highest amplitude corresponding to antinodes. We then conclude and discuss future work. The insights gained from this research lay a foundation to guide further development of the MDR. Through this work, we are better able to define the operating conditions of the MDR, which will facilitate the creation of a more robust, adaptable control framework for expanded capability. / Master of Science / Power transmission lines are important civil structures used to deliver electricity across the nation. However, these lines are subject to an array of hazards that can damage them. One such hazard is vibration due to wind, which can cause fatigue damage, leading to power line failure and outages. A popular form of vibration control is the use of a fixed vibration absorber, which has significant limitations. A mobile damping robot (MDR) can greatly improve upon the efficiency of these absorbers by transporting them to optimal locations along the power line. This thesis explores the utility and feasibility of an MDR to do so. We investigate with the help of engineering software and establish the conditions for experimentation. Our research suggests that the MDR prototype we constructed can autonomously navigate itself along the power line to optimal locations. This research will guide improvements to the MDR so that it can be more effective under real-world conditions.

The development of completely autonomous mobile vehicles has been the topic of a great deal of research over the past few decades. Spurred by interests as diverse as space exploration and land mine removal, research has focused on the mechanical requirements, sensing and computational requirements, and intelligence required for autonomous decision making. This thesis focuses on developing the software required for autonomous control, while building upon previous research into appropriate mechanical designs and sensing technologies. The thesis begins by giving an overview of the problem, and then moves on to reviewing the literature relevant to the task of fusing diverse, and often conflicting, sensor data into a usable representation. Literature relevant to the task of using that data to make intelligent decisions in an autonomous manner is reviewed. The focus then shifts to developing a working platform, called Navigator, which tests the theory in the setting of the Intelligent Ground Vehicle Competition. The theory required to control Navigator, along with the dynamic analysis used for controls testing, is developed. Image processing techniques useful for extracting features from the course are discussed, and the required mathematical relationships are derived. The thesis then discusses modifications to the Vector Field Histogram technique, which enable Navigator to fuse data from both the image processing and laser rangefinder. Development of the navigation decision-making algorithm is discussed. The information in this thesis is presented in such a way that it can serve as a reference to those who follow in the task of building autonomous vehicles. / Master of Science

There are millions of robots in operation around the world today, and almost all of them operate on factory floors in isolation from people. However, it is now becoming clear that robots can provide much more value assisting people in daily tasks in human environments. Perhaps the most fundamental capability for a mobile robot is navigating from one location to another. Advances in mapping and motion planning research in the past decades made indoor navigation a commodity for mobile robots. Yet, questions remain on how the robots should move around humans. This thesis advocates the use of semantic maps and spatial rules of engagement to enable non-expert users to effortlessly interact with and control a mobile robot. A core concept explored in this thesis is the Tour Scenario, where the task is to familiarize a mobile robot to a new environment after it is first shipped and unpacked in a home or office setting. During the tour, the robot follows the user and creates a semantic representation of the environment. The user labels objects, landmarks and locations by performing pointing gestures and using the robot's user interface. The spatial semantic information is meaningful to humans, as it allows providing commands to the robot such as ``bring me a cup from the kitchen table". While the robot is navigating towards the goal, it should not treat nearby humans as obstacles and should move in a socially acceptable manner. Three main navigation behaviors are studied in this work. The first behavior is the point-to-point navigation. The navigation planner presented in this thesis borrows ideas from human-human spatial interactions, and takes into account personal spaces as well as reactions of people who are in close proximity to the trajectory of the robot. The second navigation behavior is person following. After the description of a basic following behavior, a user study on person following for telepresence robots is presented. Additionally, situation awareness for person following is demonstrated, where the robot facilitates tasks by predicting the intent of the user and utilizing the semantic map. The third behavior is person guidance. A tour-guide robot is presented with a particular application for visually impaired users.

No description available.

This thesis addresses the problem of intelligent control of autonomous mobile robots, particularly under circumstances unforeseen by the designer. As the range of applications for autonomous robots widens and increasingly includes operation in unknown environments (exploration) and tasks which are not clearly specifiable a priori (maintenance work), this question is becoming more and more important. It is argued that in order to achieve such flexibility in unforeseen situations it is necessary to equip a mobile robot with the ability to autonomously acquire the necessary task achieving competences, through interaction with the world. Using mobile robots equipped with self-organising, behaviour-based controllers,experiments in the autonomous acquisition of motor competences and navigational skills were conducted to investigate the viability of this approach. A controller architecture is presented that allows extremely fast acquisition of motor competence such as obstacle avoidance, wall and corridor following and deadend escape: these skills are obtained in less than five learning steps,performed in under one minute of real time. This is considerably faster than previous approaches. Because the effective wiring between sensors and actuators is determined autonomously by the robot, sensors and actuators may initially be wired up arbitrarily,which reduces the risk of human error during the setting up phase of the robot. For the first time it was demonstrated that robots also become able to autonomously recover from unforeseen situations such as changes in the robot's morphology, the environment or the task. Rule-based approaches to error recovery obviously cannot offer recovery from unforeseen errors,as error situations covered by such approaches have to be identified beforehand. A robust and fast map building architecture is presented that enables mobile robots to autonomously construct internal representations of their environment, using self-organising feature maps. After a short training time the robots are able to use these self-organising feature maps successfully for location recognition. For the first time the staged acquisition of multiple competences in mobile robots is presented. First obtaining fundamental motor competences such as wall following and deadend escape (primary skills), the robots use these in a second stage to learn higher levels of competence such as the navigational task of location recognition (secondary skills). Besides laying the foundation of autonomous, staged acquisition of high level competences, this approach has the interesting property of securely grounding secondary skills in the robot's own experience, as these secondary skills are defined in terms of the primary ones.

In this thesis, a variety of different approaches are proposed to study the energy restoration problem in wireless sensor networks by one or more robots. First, we introduce an on-demand decentralized strategy performed by a robot that visits the sensors in a predefined circular order. We study it both analytically and experimentally analyzing the impact of various network parameters on network coverage, disconnection time, and time sensors have to wait to be served. We then introduce an optimal centralized approach as a benchmark to assess how close to optimal our on-demand strategy is, and we discover that, for sufficiently large networks, the on-demand strategy is indeed optimal. We then propose an even simpler mechanism where the robot simply moves blindly along the circular order, which is experimentally shown to be as efficient as the other two. The results above apply to arbitrary sensor network; we then consider a common special topology: a linear arrangement of sensors, were we propose three restoring mechanisms. We compare them experimentally discovering, once again, that the simplest approach is also the best, in most cases. We finally consider the case of multiple robots. We propose two strategies where the network is portioned among the robots and each robot takes care of a portion, and we compare those with a collaborative strategy where all robots work on the global network. The main general result of this study is that simple solutions are often as good as more sophisticated ones. In fact, a totally blind strategy where a robot simply moves around restoring energy on its way turns out to be as efficient as the best possible centralized solution for most networks.