This thesis describes the use of a Real-Time Evolutionary Algorithm (RTEA) to optimise an Artificial Neural Network (ANN) on-line (in this context “on-line” means while it is in use). Traditionally, Evolutionary Algorithms (Genetic Algorithms, Evolutionary Strategies and Evolutionary Programming) have been used to train networks before use - that is “off-line,” as have other learning systems like Back-Propagation and Simulated Annealing. However, this means that the network cannot react to new situations (which were not in its original training set). The system outlined here uses a Simulated Legged Robot as a test-bed and allows it to adapt to a changing Fitness function. An example of this in reality would be a robot walking from a solid surface onto an unknown surface (which might be, for example, rock or sand) while optimising its controlling network in real-time, to adjust its locomotive gait, accordingly. The project initially developed a Central Pattern Generator (CPG) for a Bipedal Robot and used this to explore the basic characteristics of RTEA. The system was then developed to operate on a Quadruped Robot and a test regime set up which provided thousands of real-environment like situations to test the RTEA’s ability to control the robot. The programming for the system was done using Borland C++ Builder and no commercial simulation software was used. Through this means, the Evolutionary Operators of the RTEA were examined and their real-time performance evaluated. The results demonstrate that a RTEA can be used successfully to optimise an ANN in real-time. They also show the importance of Neural Functionality and Network Topology in such systems and new models of both neurons and networks were developed as part of the project. Finally, recommendations for a working system are given and other applications reviewed.

The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed on January 22, 2010). Thesis advisor: Dr. Marjorie Skubic. Includes bibliographical references.

Symbolic grounding is a bridge between task level planning and actual robot sensing and actuation. Uncertainties raised by unstructured environments make a bottleneck for integrating traditional artificial intelligence with service robotics. In this research, a fuzzy optimisation based symbolic grounding approach is presented. This approach can handle uncertainties and helps service robots to determine the most comfortable base region for grasping objects in a fetch and carry task. Novel techniques are applied to establish fuzzy objective function, to model fuzzy constraints and to perform fuzzy optimisation. The approach does not have the short comings of others’ work and the computation time is dramatically reduced in compare with other methods. The advantages of the proposed fuzzy optimisation based approach are evidenced by experiments that were undertaken in Care-O-bot 3 (COB 3) and Robot Operating System (ROS) platforms.

Thesis (M.S.)--Brigham Young University. Dept. of Mechanical Engineering, 2006. / Includes bibliographical references (p. 91-93).

Thesis (M. E.) (Honours) -- University of Western Sydney, 2002. / Thesis submitted in fulfilment of the requirements for the degree of Master of Engineering (Honours), University of Western Sydney, School of Engineering & Industrial Design, March 2002. Bibliography : p. 99-104.

Thesis (Ph.D)--Computing, Georgia Institute of Technology, 2010. / Committee Chair: Arkin, Ronald C.; Committee Member: Christensen, Henrik I.; Committee Member: Fisk, Arthur D.; Committee Member: Ram, Ashwin; Committee Member: Thomaz, Andrea. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (Ph. D.)--University of Cambridge, 1991. / "November 1991." Includes bibliographical references.

Robotics is an emerging field, and it is also affecting several other fields. Design of robotic platforms gains more importance since the focus and aim of the robotics research broadens widely and the variety of the users is significant. This work aims to present the design of a modular mobile robotic platform, which should be simple, easy to build and easy to use. The concept of modularity, usage of off-the shelf components and utilizing a PC platform, are addressed in this work. As a result of this work, a conceptual design is presented, and a prototype is built to highlight some important aspects of the conceptual design.

Thesis (M.Sc. [i.e. M.C.M.S.])--University of Waikato, 2006. / Includes bibliographical references (p. 167-173) Also available via the World Wide Web.

This master‘s thesis is about the digital commissioning of a robotic production system for a machine tool tending. The initial research describes the current state of the art in the field of industrial robotics. Their types and possibilities are followed by the types and SW capabilities for digital commissioning. The system analysis of the robotic work cell. The practical part of this thesis describes the creation of the digital model and SW design. At the end of the thesis is performed virtual testing of the robotic system.