Personal robotics applications require autonomous mobile robot navigation methods that are safe, robust, and inexpensive. Two requirements for autonomous use of robots for such applications are an automatic motion planner to select paths and a robust way of ensuring that the robot can follow the selected path given the unavoidable odometer and control errors that must be dealt with for any inexpensive robot. Additional difficulties are faced when there is more than one robot involved. In this dissertation, we describe a new roadmapbased method for mobile robot navigation. It is suitable for partially known indoor environments and requires only inexpensive range sensors. The navigator selects paths from the roadmap and designates localization points on those paths. In particular, the navigator selects feasible paths that are sensitive to the needs of the application (e.g., no sharp turns) and of the localization algorithm (e.g., within sensing range of two features). We present a new sectorbased localizer that is robust in the presence of sensor limitations and unknown obstacles while still maintaining computational efficiency. We extend our approach to teams of robots focusing on quickly sensing ranges from all robots while avoiding sensor crosstalk, and reducing the pose uncertainties of all robots while using a minimal number of sensing rounds. We present experimental results for mobile robots and describe a webbased route planner for the Texas A&M campus that utilizes our navigator.

Personal robotics applications require autonomous mobile robot navigation methods that are safe, robust, and inexpensive. Two requirements for autonomous use of robots for such applications are an automatic motion planner to select paths and a robust way of ensuring that the robot can follow the selected path given the unavoidable odometer and control errors that must be dealt with for any inexpensive robot. Additional difficulties are faced when there is more than one robot involved. In this dissertation, we describe a new roadmapbased method for mobile robot navigation. It is suitable for partially known indoor environments and requires only inexpensive range sensors. The navigator selects paths from the roadmap and designates localization points on those paths. In particular, the navigator selects feasible paths that are sensitive to the needs of the application (e.g., no sharp turns) and of the localization algorithm (e.g., within sensing range of two features). We present a new sectorbased localizer that is robust in the presence of sensor limitations and unknown obstacles while still maintaining computational efficiency. We extend our approach to teams of robots focusing on quickly sensing ranges from all robots while avoiding sensor crosstalk, and reducing the pose uncertainties of all robots while using a minimal number of sensing rounds. We present experimental results for mobile robots and describe a webbased route planner for the Texas A&M campus that utilizes our navigator.

In this thesis we considered a simulation platform for mobile robots algorithms: Sycamore. We implemented several new features for Sycamore and we tested them while studying three different algorithms to achieve gathering by robots with limited visibility: a deterministic well known algorithm, a simple new probabilistic algorithm, and a combination of the two. The deterministic algorithm is known to achieve exact gathering when there are no faults; we tested it for the first time in presence of crashes and observed interesting and unexpected behaviors. We then performed extensive simulations with the probabilistic solution to identify the cause of an unexpected high rate of success, the simulations help us identify the relation between the rate of success and the initial configuration. Finally, we combined the two designing a hybrid solution. This work resulted in improvements of Sycamore, which can now be better employed to study mobile robots algorithms, as well as in empirical observations leading to new theoretical problems to be investigated.

The purpose of this thesis is to design and implement four-wheel mobile robot control which will be used in future in the field of mapping and localization. Concretely, it will be a design of drive control with microcontrollers Xmega, which will also process the signals of the sensors. Communication with the PC will ensure the BlueTooth module. In view of the future use of the robot, there will be designed and carried out modifications of the mechanical part. Correctness and functionality of all parts of the robot will be verified by carrying out basic movements.

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This thesis deals with the Simultaneous Localisation and Mapping algorithm as it pertains to the deployment of mobile systems in unknown environments. Simultaneous Localisation and Mapping (SLAM) as defined in this thesis is the process of concurrently building up a map of the environment and using this map to obtain improved estimates of the location of the vehicle. In essence, the vehicle relies on its ability to extract useful navigation information from the data returned by its sensors. The vehicle typically starts at an unknown location with no a priori knowledge of landmark locations. From relative observations of landmarks, it simultaneously computes an estimate of vehicle location and an estimate of landmark locations. While continuing in motion, the vehicle builds a complete map of landmarks and uses these to provide continuous estimates of the vehicle location. The potential for this type of navigation system for autonomous systems operating in unknown environments is enormous. One significant obstacle on the road to the implementation and deployment of large scale SLAM algorithms is the computational effort required to maintain the correlation information between features in the map and between the features and the vehicle. Performing the update of the covariance matrix is of O(n�) for a straightforward implementation of the Kalman Filter. In the case of the SLAM algorithm, this complexity can be reduced to O(n�) given the sparse nature of typical observations. Even so, this implies that the computational effort will grow with the square of the number of features maintained in the map. For maps containing more than a few tens of features, this computational burden will quickly make the update intractable - especially if the observation rates are high. An effective map-management technique is therefore required in order to help manage this complexity. The major contributions of this thesis arise from the formulation of a new approach to the mapping of terrain features that provides improved computational efficiency in the SLAM algorithm. Rather than incorporating every observation directly into the global map of the environment, the Constrained Local Submap Filter (CLSF) relies on creating an independent, local submap of the features in the immediate vicinity of the vehicle. This local submap is then periodically fused into the global map of the environment. This representation is shown to reduce the computational complexity of maintaining the global map estimates as well as improving the data association process by allowing the association decisions to be deferred until an improved local picture of the environment is available. This approach also lends itself well to three natural extensions to the representation that are also outlined in the thesis. These include the prospect of deploying multi-vehicle SLAM, the Constrained Relative Submap Filter and a novel feature initialisation technique. Results of this work are presented both in simulation and using real data collected during deployment of a submersible vehicle equipped with scanning sonar.

<p>Autonomous robots typically rely on internal representations of the environment, or maps, to plan and execute their tasks. Several types of maps have been proposed in the literature, and there is general consensus that different types have different advantages and limitations, and that each type is more suited to certain tasks and less to others. Because of these reasons, it is becoming common wisdom in the field of mobile robotics to use hybrid maps that integrate several representations, usually of different types. Hybrid maps provide scalability and multiple views, allowing for instance to combine robot-centered and human-centered representations. There is, however, little understanding of the general principles that can be used to combine different maps into a hybrid one, and to make it something more than the sum of its parts. There is no systematic analysis of the different ways in which different maps can be combined, and how they can be made to cooperate. This makes it difficult to evaluate and compare different systems, and precludes us from getting a clear understanding of how a hybrid map can be designed or improved.</p><p>The investigation presented in this thesis aims to contribute to fill this foundational gap, and to get a clearer understanding of the nature of hybrid maps. To help in this investigation, we develop two tools: The first one is a conceptual tool, an analytical framework in which the main ingredients of a hybrid map are described; the second one is an empirical tool, a new hybrid map that allows us to experimentally verify our claims and hypotheses.</p><p>While these tools are themselves important contributions of this thesis, our investigation has resulted in the following additional outcomes:</p><p>• A set of concepts that allow us to better understand the structure and operation of hybrid maps, and that help us to design them, compare them, identify their problems, and possibly improve them;</p><p>• The identification of the notion of synergy as the fundamental way in which component maps inside a hybrid map cooperate.</p><p>To assess the significance of these outcomes, we make and validate the following claims:</p><p>1. Our framework allows us to classify and describe existing maps in a uniform way. This claim is validated constructively by making a thorough classification of the hybrid maps reported in the literature.</p><p>2. Our framework also allows us to enhance an existing hybrid map by identifying spots for improvement. This claim is verified experimentally by modifying an existing map and evaluating its performance against the original one.</p><p>3. The notion of synergy plays an important role in hybrid maps. This claim is verified experimentally by testing the performance of a hybrid map with and without synergy.</p>

We report on experiments with a mobile robot using one vision process (forward motion vision) to calibrate another (stereo vision) without resorting to any external units of measurement. Both are calibrated to a velocity dependent coordinate system which is natural to the task of obstacle avoidance. The foundations of these algorithms, in a world of perfect measurement, are quite elementary. The contribution of this work is to make them noise tolerant while remaining simple computationally. Both the algorithms and the calibration procedure are easy to implement and have shallow computational depth, making them (1) run at reasonable speed on moderate uni-processors, (2) appear practical to run continuously, maintaining an up-to-the-second calibration on a mobile robot, and (3) appear to be good candidates for massively parallel implementations.

A mobile robot must be able to determine its own position in the world. To support truly autonomous navigation, we present a system that builds and maintains its own models of world locations and uses these models to recognize its world position from stereo vision input. The system is designed to be robust with respect to input errors and to respond to a gradually changing world by updating the world location models. We present results from tests of the system that demonstrate its reliability. The model builder and recognition system fit into a planned world modeling system that we describe.

Representing the world in terms of visible surfaces and the freespacesexisting between these surfaces and the viewer is an important problemsin robotics. Recently, researchers have proposed using the 3DsDelaunay Tessellation for representing 3D stereo vision data and thesfreespace determined therefrom. We discuss problems with using thes3D Delaunay Tessellation as the basis of the representation andspropose an alternative representation that we are currentlysinvestigating. This new representation is appropriate for planningsmobile robot navigation and promises to be robust when using stereosdata that has errors and uncertainty.