Automation of the industrial transformer core assembly process is highly desirable. A survey undertaken by the author however, revealed that due to the high cost of existing fully automated systems, Australian manufacturers producing low to medium transformer volumes continue to maintain a manual construction approach. The conceptual design of a cost-effective automation system for core assembly from pre-cut lamination stacks was consequently undertaken. The major hurdle for automating the existing manual process was identified as the difficulty in reliably handling and accurately positioning the constituent core laminations, which number in their thousands, during transformer core construction. Technical evaluation of the proposed pick-and-place core assembly system, incorporating two mobile robots bearing a common gripper, is presented herein to address these requirements. A unique robotic gripper, having the capability to selectively pick a given number of steel laminations (typically two or three) concurrently from a stack, has the potential to significantly increase productivity. The only available avenue for picking multiple laminations was deemed to be a gripper based on magnetism. Closed form analytical and finite element models for an electromagnet-stack system were contrived and their force distributions obtained. The theoretical findings were validated by experiment using a specially constructed prototype. Critical parameters for reliably lifting the required number of laminations were identified and a full scale electromagnet, that overcame inherent suction forces present in the stack during picking, was subsequently developed. A mechanical docking arrangement is envisaged that will ensure precise lamination placement. Owing to the grippers unwieldy length however, conventional robots cannot be used for assembling larger cores. Two wheeled mobile robots (WMRs) compliantly coupled to either end of the gripper could be considered although a review of the current literature revealed the absence of a suitable controller. Dynamic modelling for a single WMR was therefore undertaken and later expanded upon for the dual WMR system conceived. Nonlinear adaptive controllers for both WMR systems were developed and subsequently investigated via simulation. Neglecting the systems dynamics resulted in analogous, simplified kinematic control schemes, that were verified experimentally using prototypes. Additional cooperative control laws ensuring the synchronisation of the two robots were also implemented on the prototype system.

Navigation and guidance of mobile robots towards steady or maneuvering objects (targets) is one of the most important areas of robotics that has attracted a lot of attention in recent decades. However, in most of the existing methods, both the line-of-sight angle (bearing) and the relative distance (range) are assumed to be available for navigation and guidance algorithms. There is also a relatively large body of research on navigation and guidance with bearings-only measurements. In contrast, only a few results on navigation and guidance towards an unknown target using range-only measurements have been published. Various problems of navigation, guidance, location estimation and target tracking based on range-only measurements often arise in new wireless networks related applications. Recent advances in these applications allow us to use inexpensive transponders and receivers for range-only measurements which provide information in dynamic and noisy environments without the necessity of line-of-sight. To take advantage of these sensors, algorithms must be developed for range-only navigation. The main part of this thesis is concerned with the problem of real-time navigation and guidance of Wheeled Mobile Robots (WMRs) towards an unknown stationary or moving target using range-only measurements. The range can be estimated using the signal strength and the robust extended Kalman filtering. Several similar algorithms for navigation and guidance termed Equiangular Navigation and Guidance (ENG) laws are proposed and mathematically rigorous proofs of convergence and stability of the proposed guidance laws are given. The experimental investigation into the use of range data for a WMR navigation is documented and the results and discussions on the performance of the proposed guidance strategies are presented, where a wheeled robot successfully approach a stationary or follow a maneuvering target. In order to safely navigate and reliably operate in populated environments, ENG is then modified into Augmented-ENG (AENG), which enables the robot to approach a stationary target or follow an unpredictable maneuvering object in an unknown environment, while keeping a safe distance from the target, and simultaneously preserving a safety margin from the obstacles. Furthermore, we propose and experimentally investigate a new biologically inspired method for local obstacle avoidance and give the mathematically rigorous proof of the idea. In order for the robot to avoid collision and bypass the enroute obstacles in this method, the angle between the instantaneous moving direction of the robot and a reference point on the surface of the obstacle is kept constant. The proposed idea is combined with the ENG law, which leads to a reliable and fast long-range navigation. The performance of both navigation strategy and local obstacle avoidance techniques are confirmed with computer simulations and several experiments with ActivMedia Pioneer 3-DX wheeled robots. The second part of the thesis investigates some challenging problems in the area of wheeled robot navigation. We first address the problem of bearing-only guidance of an autonomous vehicle following a moving target with smaller minimum turning radius compared to that of the follower and propose a simple and constructive navigation law. In compliance with the increasing research on decentralized control laws for groups of mobile autonomous robots, we consider the problems of decentralized navigation of network of WMRs with limited communication and decentralized stabilization of formation of WMRs. New control laws are presented and simulation results are provided to illustrate the control laws and their applications.

When animals explore a new environment, they do not acquire a precise map of the places visited. In fact, research has shown that learning is a recurring process. Over time, new information helps the animal to update their perception of the locations it has visited. Yet, they are still able to use the fuzzy and often incomplete representation to find their way home. This process has been termed the cognitive mapping process. The work presented in this thesis uses a mobile robot equipped with sonar sensors to investigate the nature of such a process. Specifically, what is the information that is fundamental and prevalent in spatial navigation? Initially, the robot is instructed to compute a “cognitive map” of its environment. Since a robot is not a cognitive agent, it cannot, by definition, compute a cognitive map. Hence the robot is used as a test bed for understanding the cognitive mapping process. Yeap’s (1988) theory of cognitive mapping forms the foundation for computing the robot’s representation of the places it has visited. He argued that a network of local spaces is computed early in the cognitive mapping process. Yeap coined these local spaces as Absolute Space Representations (ASRs). However, ASR is not just a process of partitioning the environment into smaller local regions. The ASRs describe the bounded space that one is in, how one could leave that space (exits) and how the exits serves to link the ASRs to form a network that serves as the cognitive map (see Jefferies (1999)). Like the animal’s cognitive map, ASRs are not precise geometrical maps of the environment but rather, provide a rough shape or feel of the space the robot is currently in. Once the robot computes its “cognitive map”, it is then, like foraging and hoarding animals, instructed to find its way home. To do so, the robot uses two crucial pieces of information: distance between exits of ASRs and relative orientation of adjacent ASRs. A simple animal-like strategy was implemented for the robot to locate home. Results from the experiments demonstrated the robot’s ability to determine its location within the visited environment along its journey. This task was performed without the use of an accurate map. From these results and reviews of various findings related to cognitive mapping for various animals, we deduce that: Different animals have different sensing capabilities. They live in different environments and therefore face unique challenges. Consequently, they evolve to have different navigational strategies. However, we believe two crucial pieces of information are inherent in all animals and form the fundamentals of navigation: distance and orientation. Higher level animals may encode and may even prefer richer information to enhance the animal’s cognitive map. Nonetheless, distance and orientation will always be computed as a core process of cognitive mapping. We believe this insight will help future research to better understand the complex nature of cognitive mapping.

Este trabalho apresenta o desenvolvimento dos algoritmos de controle de um robô móvel autônomo para coleta de lixo. O objetivo do robô é coletar latas de refrigerante espalhadas pelo chão. O sistema de navegação do robô foi implementado utilizando a arquitetura denominada \"Motor-Schema\". Essa arquitetura fornece um método para projetar comportamentos primitivos que atuam em forma paralela para realizar ações robóticas inteligentes em resposta aos estímulos do ambiente. O sistema de controle apresentado foi constituído por vários comportamentos primitivos que, coordenados, permitiram ao robô explorar de forma segura um ambiente desconhecido, detectar e coletar o lixo e levá-lo num depósito determinado. Os algoritmos desenvolvidos foram testados utilizando uma ferramenta de simulação 2D denominada Player/Stage. Os resultados obtidos mostraram que a solução apresentada é adequada para resolver a aplicação robótica de coleta de lixo. / This work presents the control system for an autonomous mobile robot for soda can collection. The navigation system is implemented using a reactive architecture called \"Motor-Schema\". This architecture provides a methodology to design primitive behaviors that can act in a distributed and parallel manner to yield intelligent robotic actions in response to environmental stimuli. The control system is composed of several primitive behaviors, which enable the robot explore an unknown environment, detect and collect the soda cans and navigate toward a soda can reservoir. The algorithms are tested using Player/Stage, a software for 2D simulation. The results show that the solution is suitable for soda can collection.

The recent advent of consumer grade Brain-Computer Interfaces (BCI) provides a new revolutionary and accessible way to control computers. BCI translate cognitive electroencephalography (EEG) signals into computer or robotic commands using specially built headsets. Capable of enhancing traditional interfaces that require interaction with a keyboard, mouse or touchscreen, BCI systems present tremendous opportunities to benefit various fields. Movement restricted users can especially benefit from these interfaces. In this thesis, we present a new way to interface a consumer-grade BCI solution to a mobile robot. A Red-Green-Blue-Depth (RGBD) camera is used to enhance the navigation of the robot with cognitive thoughts as commands. We introduce an interface presenting 3 different methods of robot-control: 1) a fully manual mode, where a cognitive signal is interpreted as a command, 2) a control-flow manual mode, reducing the likelihood of false-positive commands and 3) an automatic mode assisted by a remote RGBD camera. We study the application of this work by navigating the mobile robot on a planar surface using the different control methods while measuring the accuracy and usability of the system. Finally, we assess the newly designed interface’s role in the design of future generation of BCI solutions.

The master's thesis describes the complex design of autonomous mobile robot and its realization. It consists of the design of the chassis, actuators, safety components and electronics. The design part is given by specifications defined by external firm; the specifications are overviewed in the introduction section. The design part describes the function of particular elements the construction nodes consist of. Follow-up chapters solve the implementation of electronics in construction design and also the mounting of the outside shell to the frame. Constructional design of autonomous robot Bender III is the main result of this thesis. All required design and functional specifications were met.

This thesis describes the design of a robotic inspection tool which is based on a differential track-drive platform. The robotic inspection tool is a one man-portable UGV that has been developed for the purpose of non-destructive evaluation (NDE) and internal inspection of environments where human penetration may be difficult or hazardous. Various NDE and sensing techniques are described in this paper but the focus is on the mechanical and electrical design of the platform itself. The platform is a versatile device for mobile robotics research and development that supports a wide variety of instrumentation and payloads. Variable height control of the payload is achieved with a scissor lift assembly that provides accurate positioning of equipped sensors and instrumentation. The architecture of the platform was designed to support future autonomous implementations. / Master of Science

This dissertation addresses the problem of motion planning and control for nonholonomic mobile robots, particularly wheeled and tracked mobile robots, working in extreme environments, for example, desert, forest, and mine. In such environments, the mobile robots are highly subject to external disturbances (e.g., slippery terrain, dusty air, etc.), which essentially introduce uncertainties to the robot systems. The complexity of the motion planning problem is due to taking both nonholonomic and uncertainty constraints into account simultaneously. As a result, none of the conventional nonholonomic motion planning can be directly applied. The control problem is even more challenging since state constraints posed by obstacles in the environments must also be considered along with the nonholonomic and uncertainty constraints. In this research, we systematically develop a new type of motion planning technique that determines an optimal path for a mobile robot in a given environment. This motion planning technique is based on the idea of a maximum allowable uncertainty, which is a number assigned to each free configuration in the environment. The optimal path is a path connecting given initial and goal configurations through a series of configurations respecting the nonholonomic constraint and possessing the highest maximum allowable uncertainty. Both linear and quadratic approximations of the maximum allowable uncertainty, including their corresponding motion planners, have been studied. Additionally, we develop the first real-time robust control algorithm for the mobile robot under uncertainty to follow given paths safely and accurately in cluttered environments. The control algorithm also utilizes the concept of the maximum allowable uncertainty as well as the robust control theory. The simulation results have shown the effectiveness and robustness of the control algorithm in steering the mobile robot along a given path amidst obstacles without collisions even when the level of robot uncertainty is high. / Ph. D.

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