No description available.

The ability of robotic units to navigate various terrains is critical to the advancement of robotic operation in real world environments. Next generation robots will need to adapt to their environment in order to accomplish tasks that are either too hazardous, too time consuming, or physically impossible for human-beings. Such tasks may include accurate and rapid explorations of various planets or potentially dangerous areas on planet Earth. This research investigates a navigation control methodology for a wheel-legged robot based on active vision. The method presented is designed to control the reconfigurability of the robot (i.e. control the usage of the wheels and legs), depending upon the obstacle/terrain, based on perception. Surface estimation for robot reconfigurability is implemented using a region growing method and a characterization and traversability assessment generated from camera data. As a result, a mathematical approach that directs necessary navigation behavior is implemented to control robot mobility. The hybrid wheeled-legged rover possesses a four-legged or six-legged walking system as well as a four-wheeled mobility system.

Mobile robots are widely used in many industrial fields. Research on path planning for mobile robots is one of the most important aspects in mobile robots research. Path planning for a mobile robot is to find a collision-free route, through the robot’s environment with obstacles, from a specified start location to a desired goal destination while satisfying certain optimization criteria. Most of the existing path planning methods, such as the visibility graph, the cell decomposition, and the potential field are designed with the focus on static environments, in which there are only stationary obstacles. However, in practical systems such as Marine Science Research, Robots in Mining Industry, and RoboCup games, robots usually face dynamic environments, in which both moving and stationary obstacles exist. Because of the complexity of the dynamic environments, research on path planning in the environments with dynamic obstacles is limited. Limited numbers of papers have been published in this area in comparison with hundreds of reports on path planning in stationary environments in the open literature. Recently, a genetic algorithm based approach has been introduced to plan the optimal path for a mobile robot in a dynamic environment with moving obstacles. However, with the increase of the number of the obstacles in the environment, and the changes of the moving speed and direction of the robot and obstacles, the size of the problem to be solved increases sharply. Consequently, the performance of the genetic algorithm based approach deteriorates significantly. This motivates the research of this work. This research develops and implements a simulated annealing algorithm based approach to find the optimal path for a mobile robot in a dynamic environment with moving obstacles. The simulated annealing algorithm is an optimization algorithm similar to the genetic algorithm in principle. However, our investigation and simulations have indicated that the simulated annealing algorithm based approach is simpler and easier to implement. Its performance is also shown to be superior to that of the genetic algorithm based approach in both online and offline processing times as well as in obtaining the optimal solution for path planning of the robot in the dynamic environment. The first step of many path planning methods is to search an initial feasible path for the robot. A commonly used method for searching the initial path is to randomly pick up some vertices of the obstacles in the search space. This is time consuming in both static and dynamic path planning, and has an important impact on the efficiency of the dynamic path planning. This research proposes a heuristic method to search the feasible initial path efficiently. Then, the heuristic method is incorporated into the proposed simulated annealing algorithm based approach for dynamic robot path planning. Simulation experiments have shown that with the incorporation of the heuristic method, the developed simulated annealing algorithm based approach requires much shorter processing time to get the optimal solutions in the dynamic path planning problem. Furthermore, the quality of the solution, as characterized by the length of the planned path, is also improved with the incorporated heuristic method in the simulated annealing based approach for both online and offline path planning.

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.

This thesis proposes a new method of dividing a task between members of a robot team. This method is dynamic territoriality. Territoriality is an emergent method of distributing resources between animals- each animal independently marks and defends their own area. Territoriality has previously been used on robot teams with varying results; these robot territorial systems did little or no adaptation to the environment or task, unlike natural territorial systems. / A dynamic territorial system adapts the territories to fit the environment, and the task the robot team must perform. The algorithm used to generate the territories is a novel extension of the ant clustering algorithm. Given a topological map of the space, it divides a space into a pre-specified number of territories such that the territories have minimal contact and near-equal area. The dynamic territorial algorithm was tested using hand-generated topological maps, then on physical environments. A robot system was developed to traverse an area and generate a topological map usable by the algorithm. / Dynamic territoriality can be used by simple robot teams to organize tasks such as multi- robot cleaning, monitoring and surveillance. The territories divide a complex environment into several simpler environments; this makes many tasks easier to perform. As the dynamic territorial algorithm is designed to minimize connections between territories, robot surveillance teams can capture or track intruders most easily at the territorial boundaries. Predator-prey and robot simulations were used to demonstrate the effectiveness of this territorial system in multi-robot surveillance. / This thesis extends previous work in territorial robotics and biologically inspired algorithms to create a new multi-robot control system. This control system has been implemented in hardware using a new topological mapping system. The thesis shows that this new multi-robot control system can effectively survey an area. In particular, it can control the movement of large groups of targets, or targets that move faster than the robots. / Thesis (PhD)--University of South Australia, 2005.

Proefschrift Universiteit van Amsterdam. / Met lit. opg. - Met samenvatting in het Nederlands.

Thesis (M.S.)--Auburn University, 2007. / Abstract. Includes bibliographic references (ℓ. 92-94)

Thesis (M.S. & I.T.) -- Lincoln University, 2007. / "December 2007." Degree named as Master of Applied Computing on spine and use sheet. Also available via the World Wide Web.

Thesis (MScIng)--University of Stellenbosch, 2007. / Bibliography. Also available via the Internet.

Thesis (Ph.D.)--Auburn University, 2007. / Abstract. Vita. Includes bibliographic references (ℓ. 78-79)