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Robot Navigation from Nature: Simultaneous Localisation, Mapping, and Path Planning Based on Hippocampal Models
Abstract and Figures
This thesis describes the use of a hippocampal model as a basis for a complete robot mapping and navigation system. Computational models of animal navigation systems have traditionally had poor practical performance when implemented on robots. The aim of the work was to determine the extent to which hippocampal models can be used to provide a robot with functional mapping and navigation capabilities. The thesis starts with a review of the mapping and navigation problem and the robotic and biological approaches to providing a solution. The problem is a broad one and to completely solve it a robot must possess several key abilities. These include the ability to explore an unknown environment, perform Simultaneous Localisation And Mapping (SLAM), plan and execute routes to goals, and adapt to environment changes. The most successful conventional solutions are based on core probabilistic algorithms and use a variety of map representations ranging from metric to topological. These probabilistic methods have good SLAM performance, but few have been successfully integrated into a complete solution to the entire mapping and navigation problem. In contrast, biological systems are generally poorly understood and current models emulating them have poor practical performance. However, many animals solve the entire mapping and navigation problem without the precise sensors and high resolution maps that are typical of robotic methods. Models of the mapping and navigation process in relatively well understood animals such as rodents offer much potential for practical improvement. The thesis describes a series of studies which implemented computational models inspired by the mapping and navigation processes in the rodent hippocampus. The initial study was based on conventional theories of separate orientation and location representations in the rodent hippocampus. The model was tested in a range of experiments that revealed fundamental limitations in its practical usefulness as a SLAM system. A review of the literature revealed no strong evidence suggesting that rodents overcome these limitations and no mechanisms by which they might do so. Consequently the model was then modified to combine its separate representations of robot orientation and location into a single representation of robot pose. This new model, known as RatSLAM, was able to successfully perform SLAM on two different robot platforms in a range of indoor and outdoor environments. A goal memory system was added to RatSLAM to give the robot the ability to plan and execute routes to goals. However, in large complex environments the RatSLAM models pose representation exhibits a number of phenomena that cause the goal memory system to fail. These phenomena were removed or modified by the introduction of a new experience mapping algorithm. This algorithm uses the RatSLAM maps to create spatio-temporal-behavioural representations known as experience maps, which preserve the topological structure of the original map. When used in a number of indoor and outdoor experiments the algorithm generated representations that were globally topological and locally spatial. Subsequent work implemented methods for exploration, goal recall, and adaptation using the representations built by the RatSLAM model and experience mapping algorithm. Each of these processes was tested in a range of autonomous robot experiments. In the final experiments the robot, acting in a completely autonomous manner, explored an unknown environment while performing SLAM, then navigated to goal locations while adapting to simple lasting environment changes. The studies described in this thesis provide a detailed analysis of the mapping and navigation capabilities of current computational models of biological systems within the context of producing functional robot systems. Together the RatSLAM model and experience mapping algorithm bring a biologically inspired method into the realm of conventional robot mapping and navigation techniques for the first time. The combined system was developed heuristically and displayed successful, although sometimes suboptimal, performance in a range of real robot tests lasting up to two hours. The work stands as one of the few examples of a complete integrated attempt to solve all the major mapping and navigation challenges faced by mobile robots.
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