This thesis addresses the issues of visual landmark recognition in autonomous robot navigation along known routes, by intuitively exploiting the functions of the human visual system and its navigational ability. A feedforward-feedbackward architecture has been developed for recognising visual landmarks in real-time. It integrates the theoretical concepts from the pre-attentive and attentive stages in the human visual system, the selective attention adaptive resonance theory neural network and its derivatives, and computational approaches toward object recognition in computer vision. / The main contributions of this thesis lie within the emulations of the pre-attentive and attentive stages in the context of object recognition, embedding various concepts from neural networks into a computational template-matching approach in the computer vision. The real-time landmark recognition capability is achieved by mimicking the pre-attentive stage, where it models a selective attention mechanism for computational resource allocation, focusing only on the regions of interest. This results in a parsimonious searching method, addressing the computational restrictive nature of current computer processing power. Subsequently, the recognition of visual landmarks in both clean and cluttered backgrounds (invariant to different viewpoints) is implemented in the attentive stage. This is achieved by developing a memory feedback modulation (MFM) mechanism that enables knowledge from the memory to interact and enhance the efficiency of earlier stages in the system, and the use of viewer-centre object representation which is mimicked from the human visual system. Furthermore, the architecture has been extended to incorporate both top-down and bottom-up facilitatory and inhibition pathways between the memory and the earlier stages to enable the architecture to recognise a 2D landmark, which is partially occluded by adjacent features in the neighbourhood. / The feasibility of the architecture in recognising objects in cluttered backgrounds is demonstrated via computer simulations using real-images, consisting of a larger number of real cluttered indoor and outdoor scenes. The system's applicability in mobile robot navigation is revealed through real-time navigation trials of known routes, using a real robotic vehicle which is designed and constructed from the component level. The system has been evaluated by providing the robot with a topological map of the routes prior to navigation, such that object recognition serves as landmark detection with reference to the given map, where autonomous guidance is based on the recognition of familiar objects to compute the robot's absolute position along the pathways. / Thesis (PhD)--University of South Australia, 2006.

The development of wall climbing robots is relatively recent, beginning with some large scale robots in the early 1990s. Wall climbing robots can be used to gain access to or inspect space that is not easily accessible or dangerous for human operators. The range of applicable fields encompasses industrial processes and inspection, exploration, rescue and monitoring. The smaller robots can be used for surveillance purposes due to their stealthy nature. Larger crawling robots may be used to carry out specific tasks such as sand blasting of ship hulls and blasting and spray painting of large containers such as cylindrical storage tanks used by the chemical, petroleum and nuclear industries. Their flexibility and mobility mean that they can accomplish tasks that would be impossible for more conventional robots. The flexibility of mobility that such robots gain from their ability to move on all surfaces rather than only horizontal ones creates some unique challenges. Broadly, they can be split into three categories: robot design, robot control and motion planning, and environmental mapping and localisation. This thesis examines the first two of these problems. A prototype bipedal robot has been built and a second designed in order to capitalise on the experience gained with the first. An in-depth examination of the motion planning problem has been made and new techniques to tackle this problem have been developed. Such techniques are not limited to applications with wall climbing robots as there is commonality with more traditional fixed manipulators. Finally, the planning techniques were combined with the robot design in a test scenario that validated both the design and the motion planning techniques developed throughout the dissertation.

The protection of assets is an important part of daily life. Currently this is done using a combination of passive security cameras and security officers actively patrolling the premises. However, security officers, being human, are subject to a number of limitations both physical and mental. A security robot would not suffer from these limitations, however currently there are a number of challenges to implementing such a robot. These challenges include navigation in a complex real-world environment, fast and accurate threat detection and threat tracking. Overcoming these challenges is the focus of my research. To that end a small security robot, the CuWITH or Curious WITH,has been developed and is presented in this thesis. The CuWITH utilises a programmable navigation system, curiosity-based threat detection and curiosity-driven threat tracking curiosity to protect a real office environment. In this thesis we will first discuss the CuWITH's system design in detail, with a particular focus on the components and the architectural strategies employed. We then move to a more detailed examination of the mathematical underpinnings of the CuWITHs curiosity based threat detection and curiosity driven threat tracking. The details of the CuWITH's navigation will also be explained. We will then present a number of experiments which demonstrate the effectiveness of the CuWITH. We show that the programmable navigation of the CuWITH, although simple, allows for easy modification of the patrol path without risk to the stability of the system. We will then present the results of both offline and online testing of the CuWITH's curiosity based threat detection. The reaction time and accuracy of the CuWITHs curiosity driven threat tracking will also be illustrated. As a final test the CuWITH is instructed to execute a patrol in a real office environment, with threatening and non-threatening persons present. The results of this test demonstrate all major systems of the CuWITH working together very well and successfully executing the patrol even when moved to a different environment.

The protection of assets is an important part of daily life. Currently this is done using a combination of passive security cameras and security officers actively patrolling the premises. However, security officers, being human, are subject to a number of limitations both physical and mental. A security robot would not suffer from these limitations, however currently there are a number of challenges to implementing such a robot. These challenges include navigation in a complex real-world environment, fast and accurate threat detection and threat tracking. Overcoming these challenges is the focus of my research. To that end a small security robot, the CuWITH or Curious WITH,has been developed and is presented in this thesis. The CuWITH utilises a programmable navigation system, curiosity-based threat detection and curiosity-driven threat tracking curiosity to protect a real office environment. In this thesis we will first discuss the CuWITH's system design in detail, with a particular focus on the components and the architectural strategies employed. We then move to a more detailed examination of the mathematical underpinnings of the CuWITHs curiosity based threat detection and curiosity driven threat tracking. The details of the CuWITH's navigation will also be explained. We will then present a number of experiments which demonstrate the effectiveness of the CuWITH. We show that the programmable navigation of the CuWITH, although simple, allows for easy modification of the patrol path without risk to the stability of the system. We will then present the results of both offline and online testing of the CuWITH's curiosity based threat detection. The reaction time and accuracy of the CuWITHs curiosity driven threat tracking will also be illustrated. As a final test the CuWITH is instructed to execute a patrol in a real office environment, with threatening and non-threatening persons present. The results of this test demonstrate all major systems of the CuWITH working together very well and successfully executing the patrol even when moved to a different environment.

PhD / This thesis is concerned with combining the perceptual abilities of mobile robots and human operators to execute tasks cooperatively. It is generally agreed that a synergy of human and robotic skills offers an opportunity to enhance the capabilities of today’s robotic systems, while also increasing their robustness and reliability. Systems which incorporate both human and robotic information sources have the potential to build complex world models, essential for both automated and human decision making. In this work, humans and robots are regarded as equal team members who interact and communicate on a peer-to-peer basis. Human-robot communication is addressed using probabilistic representations common in robotics. While communication can in general be bidirectional, this work focuses primarily on human-to-robot information flow. More specifically, the approach advocated in this thesis is to let robots fuse their sensor observations with observations obtained from human operators. While robotic perception is well-suited for lower level world descriptions such as geometric properties, humans are able to contribute perceptual information on higher abstraction levels. Human input is translated into the machine representation via Human Sensor Models. A common mathematical framework for humans and robots reinforces the notion of true peer-to-peer interaction. Human-robot information fusion is demonstrated in two application domains: (1) scalable information gathering, and (2) cooperative decision making. Scalable information gathering is experimentally demonstrated on a system comprised of a ground vehicle, an unmanned air vehicle, and two human operators in a natural environment. Information from humans and robots was fused in a fully decentralised manner to build a shared environment representation on multiple abstraction levels. Results are presented in the form of information exchange patterns, qualitatively demonstrating the benefits of human-robot information fusion. The second application domain adds decision making to the human-robot task. Rational decisions are made based on the robots’ current beliefs which are generated by fusing human and robotic observations. Since humans are considered a valuable resource in this context, operators are only queried for input when the expected benefit of an observation exceeds the cost of obtaining it. The system can be seen as adjusting its autonomy at run-time based on the uncertainty in the robots’ beliefs. A navigation task is used to demonstrate the adjustable autonomy system experimentally. Results from two experiments are reported: a quantitative evaluation of human-robot team effectiveness, and a user study to compare the system to classical teleoperation. Results show the superiority of the system with respect to performance, operator workload, and usability.

Thesis (M.S.)--Ohio University, June, 1985. / Title from PDF t.p.

Thesis (Ph. D.)--University of Hong Kong, 1990.

Typescript. Includes bibliographical references (leaves 91-95).

Thesis (Ph.D.)--City University of Hong Kong, 2009. / "Submitted to Department of Manufacturing Engineering and Engineering Management in partial fulfillment of the requirements for the degree of Doctor of Philosophy." Includes bibliographical references (leaves 87-100)

Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2007. / Magnus Egerstedt, Committee Chair ; Patricio Vela, Committee Member ; Ayanna Howard, Committee Member ; Tucker Balch, Committee Member ; Wayne Book, Committee Member.