Dissertation (Ph.D. in Computer Science) Naval Postgraduate School, March 1993. / Dissertation supervisors, Michael L. Nelson and Robert B. McGhee. Includes bibliographical references (p. 288-302).

Thesis (Ph.D.)--Drexel University, 2010. / Includes abstract and vita. Includes bibliographical references (leaves 104-108).

Thesis (Ph. D.)--Florida State University, 2006. / Advisor: Emmanuel G. Collins, Jr., Florida State University, College of Engineering, Dept. of Mechanical Engineering. Title and description from dissertation home page (viewed Sept. 21, 2006). Document formatted into pages; contains xiii, 87 pages. Includes bibliographical references.

Thesis (Ph. D.)--Drexel University, 2007. / Includes abstract and vita. Includes bibliographical references (leaves 100-104).

Thesis (Ph. D.)--Computing, Georgia Institute of Technology, 2008. / Isbell, Charles, Committee Member ; Lipkin, Harvey, Committee Member ; Balch, Tucker, Committee Member ; Bobick, Aaron, Committee Member ; Arkin, Ronald, Committee Chair.

Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2008. / Fujimoto, Richard, Committee Member ; Ma, Xiaoli, Committee Member ; Fekri, Faramarz, Committee Member ; Copeland, John, Committee Member ; Akyildiz, Ian, Committee Chair.

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

Colloidal robots refer to the colloid scale (from nm to μm) machines capable of carrying out programmed actions for complex tasks automatically. Because of its promising application in engineering and medical service, colloidal robotics have been of much recent research interest in both theoretical and technological relevance. However, there remain many open challenges on increasing actuation efficiency, achieving high level tasks (e.g., autonomous navigation), etc. This dissertation, in general, focuses on developing new actuation mechanisms and designing autonomous navigation strategies for colloidal robots with both experimental and computational efforts. Firstly, the motivation, background and recent research advances on colloidal robots are reviewed. In Chapter 2, a high-efficiency actuation method called contact charge electrophoresis(CCEP) is introduced to propel the dielectric metallic Janus colloid particles. The autonomous propulsion of Janus particles shows colloidal particle asymmetries can be used to direct the motions of colloidal robots. Beyond single colloidal particle's propulsion, Chapter 3 shows multi-colloidal particles' motions can be coupled and synchronized to generate traveling waves via electrostatic interactions. Our results in Chapter 3 suggest that simple energy inputs can coordinate complex motions for colloidal robots. Then inspired by active particles motions' guided by their symmetry in Chapter 2, we show in Chapter 4 how multiple autonomous navigation can be achieved by designing the active particle's geometry and its stimulus response. Chapter 4 describes a strategy that colloid particles can sense the stimulus in environment via shape-shifting. The feedback loop of sensing and motion enables colloid particles to achieve positive or negative chemotaxis-like navigation. To experimentally realize similar navigation behaviors introduced in Chapter 4, we described a magnetic driven colloidal robot system in Chapter 5, which could show navigation behaviors (uphill and downhill) on a slope by rationally programming the external magnetic field. Chapter 6 highlights future research directions and potential applications of colloidal robots.

Autonomous robots deployed in complex, natural human environments such as homes and offices need to manipulate numerous objects throughout their deployment. For an autonomous robot to operate effectively in such a setting and not require excessive training from a human operator, it should be capable of discovering how to reliably manipulate novel objects it encounters. We characterize the possible methods by which a robot can act on an object using the concept of affordances. We define affordance-based behaviors as object manipulation strategies available to a robot, which correspond to specific semantic actions over which a task-level planner or end user of the robot can operate. This thesis concerns itself with developing the representation of these affordance- based behaviors along with associated learning algorithms. We identify three specific learning problems. The first asks which affordance-based behaviors a robot can successfully apply to a given object, including ones seen for the first time. Second, we examine how a robot can learn to best apply a specific behavior as a function of an object’s shape. Third, we investigate how learned affordance knowledge can be transferred between different objects and different behaviors. We claim that decomposing affordance-based behaviors into three separate factors— a control policy, a perceptual proxy, and a behavior primitive—aids an autonomous robot in learning to manipulate. Having a varied set of affordance-based behaviors available allows a robot to learn which behaviors perform most effectively as a function of an object’s identity or pose in the workspace. For a specific behavior a robot can use interactions with previously encountered objects to learn to robustly manipulate a novel object when first encountered. Finally, our factored representation allows a robot to transfer knowledge learned with one behavior to effectively manipulate an object in a qualitatively different manner by using a distinct controller or behavior primitive. We evaluate all work on a bimanual, mobile-manipulator robot. In all experiments the robot interacts with real-world objects sensed by an RGB-D camera.

This thesis concerns the design, construction, control, and testing of a novel self-contained soft robotic vehicle; the JenniFish is a free-swimming jellyfish-like soft robot that could be adapted for a variety of uses, including: low frequency, low power sensing applications; swarm robotics; a STEM classroom learning resource; etc. The final vehicle design contains eight PneuNet-type actuators radially situated around a 3D printed electronics canister. These propel the vehicle when inflated with water from its surroundings by impeller pumps; since the actuators are connected in two neighboring groups of four, the JenniFish has bi-directional movement capabilities. Imbedded resistive flex sensors provide actuator position to the vehicle’s PD controller. Other onboard sensors include an IMU and an external temperature sensor. Quantitative constrained load cell tests, both in-line and bending, as well as qualitative free-swimming video tests were conducted to find baseline vehicle performance capabilities. Collected metrics compare well with existing robotic jellyfish. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection