Thesis (Ph. D.)--Ohio State University, 2004. / Title from first page of PDF file. Document formatted into pages; contains xiii, 155 p.; also includes graphics (some col.) Includes bibliographical references (p. 150-155). Available online via OhioLINK's ETD Center

Thesis (Ph.D.)--University of Delaware, 2006. / Principal faculty advisor: Chien-Chung Shen, Dept. of Computer & Information Sciences. Includes bibliographical references.

Thesis (M. Sc.)Computer Science)--University of Pretoria, 2005. / Includes summary. Includes bibliographical references. Available on the Internet via the World Wide Web.

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In this thesis, we investigate the problem of path formation and prey retrieval in a swarm of robots. We present two swarm intelligence control mechanisms used for distributed robot path formation. In the first, the robots form linear chains. We study three variants of robot chains, which vary in the degree of motion allowed to the chain structure. The second mechanism is called vectorfield. In this case, the robots form a pattern that globally indicates the direction towards a goal or home location. Both algorithms were designed following the swarm robotics control principles: simplicity of control, locality of sensing and communication, homogeneity and distributedness. We test each controller on a task that consists in forming a path between two objects—the prey and the nest—and to retrieve the prey to the nest. The difficulty of the task is given by four constraints. First, the prey requires concurrent, physical handling by multiple robots to be moved. Second, each robot’s perceptual range is small when compared to the distance between the nest and the prey; moreover, perception is unreliable. Third, no robot has any explicit knowledge about the environment beyond its perceptual range. Fourth, communication among robots is unreliable and limited to a small set of simple signals that are locally broadcast. In simulation experiments we test our controllers under a wide range of conditions, changing the distance between nest and prey, varying the number of robots used, and introducing different obstacle configurations in the environment. Furthermore, we tested the controllers for robustness by adding noise to the different sensors, and for fault tolerance by completely removing a sensor or actuator. We validate the chain controller in experiments with up to twelve physical robots. We believe that these experiments are among the most sophisticated examples of self-organisation in robotics to date.

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

Computer game-playing agents are almost as old as computers themselves, and people have been developing agents since the 1950's. Unfortunately the techniques for game-playing agents have remained basically the same for almost half a century -- an eternity in computer time. Recently developed approaches have shown that it is possible to develop game playing agents with the help of learning algorithms. This study is based on the concept of algorithms that learn how to play board games from zero initial knowledge about playing strategies. A coevolutionary approach, where a neural network is used to assess desirability of leaf nodes in a game tree, and evolutionary algorithms are used to train neural networks in competition, is overviewed. This thesis then presents an alternative approach in which particle swarm optimization (PSO) is used to train the neural networks. Different variations of the PSO are implemented and compared. The results of the PSO approaches are also compared with that of an evolutionary programming approach. The performance of the PSO algorithms is investigated for different values of the PSO control parameters. This study shows that the PSO approach can be applied successfully to train game-playing agents. / Dissertation (MSc)--University of Pretoria, 2007. / Computer Science / Unrestricted

In the post-9/11 world, new and improved surveillance and information-gathering technologies have become a high-priority problem to be solved. Surveillance systems are often needed in areas too hostile or dangerous for a direct human presence. The field of robotics is being looked to for an autonomous mobile surveillance system. One major problem is the control and coordination of multiple cooperating robots. Researchers have looked to the distributed control strategies found in nature in the form of social insects as an inspiration for new control schemes. Swarm intelligence research centers around the interactions of such systems and how they can be applied to contemporary problems. In this thesis, a surveillance system of mobile autonomous robots based on the principles of swarm intelligence is presented. / Master of Science

Thesis (M.S.)--Baylor University, 2005.

Thesis (Ph.D.)--University of Wyoming, 2008. / Title from PDF title page (viewed on August 5, 2009). Includes bibliographical references (p. 261-271).