A Navigation System For Indoor Outdoor Environments With An Unmanned Ground Vehicle Ugv

"This thesis presents an approach for solving the global navigation problem of wheeled mobile robots. The presented solution for outdoor navigation uses Extended Kalman Filter (EKF) to estimate the robot location based on the measurements from Global Positioning System (GPS), inertial measurement unit (IMU) and wheel encoders. For indoor navigation (where GPS signals are blocked) another probabilistic approach, based on Monte Carlo Localization (MCL), is used for localization. This algorithm utilizes the map of the environment to estimate the posterior of the robot using the depth measurements from a Kinect sensor. The output from the Kinect sensor is processed to imitate the output of a 2D laser scanner by projecting the points from a thin horizontal strip of pixels in the image plane to the corresponding real world 3D coordinates using the pin-hole camera model. Two different controllers based on Dynamic Feedback Linearization (DFL) and Input-Output State Feedback Linearization (I-O SFL) have been analyzed, simulated and compared. Based on the thesis objective and the simulated results, the I-O SFL method was chosen for solving the trajectory tracking problem. A set of test experiments was conducted to evaluate the performance of the proposed system in outdoor, indoor and a combination of both environments. The results show that the robot can successfully navigate through the way-points with a great accuracy in indoor environments, while the accuracy in outdoor environments is within the 3m position accuracy of the GPS."--Abstract.

This book captures the latest results and techniques for cooperative localization and navigation drawn from a broad array of disciplines. It provides the reader with a generic and comprehensive view of modeling, strategies, and state estimation methodologies in that fields. It discusses the most recent research and novel advances in that direction, exploring the design of algorithms and architectures, benefits, and challenging aspects, as well as a potential broad array of disciplines, including wireless communication, indoor localization, robotics, emergency rescue, motion analysis, etc.

Localization and mapping are the essence of successful navigation in mobile platform technology. Localization is a fundamental task in order to achieve high levels of autonomy in robot navigation and robustness in vehicle positioning. Robot localization and mapping is commonly related to cartography, combining science, technique and computation to build a trajectory map that reality can be modelled in ways that communicate spatial information effectively. This book describes comprehensive introduction, theories and applications related to localization, positioning and map building in mobile robot and autonomous vehicle platforms. It is organized in twenty seven chapters. Each chapter is rich with different degrees of details and approaches, supported by unique and actual resources that make it possible for readers to explore and learn the up to date knowledge in robot navigation technology. Understanding the theory and principles described in this book requires a multidisciplinary background of robotics, nonlinear system, sensor network, network engineering, computer science, physics, etc.

This volume aims to provide a reference to the development of robotic intelligence, built upon Semantic Computing, in terms of 'action' to realize the 'context' and 'intention' formulated by Semantics Computing during the 'thinking' or reasoning process. It addresses three core areas:

Multimodal Scene Understanding: Algorithms, Applications and Deep Learning presents recent advances in multi-modal computing, with a focus on computer vision and photogrammetry. It provides the latest algorithms and applications that involve combining multiple sources of information and describes the role and approaches of multi-sensory data and multi-modal deep learning. The book is ideal for researchers from the fields of computer vision, remote sensing, robotics, and photogrammetry, thus helping foster interdisciplinary interaction and collaboration between these realms. Researchers collecting and analyzing multi-sensory data collections – for example, KITTI benchmark (stereo+laser) - from different platforms, such as autonomous vehicles, surveillance cameras, UAVs, planes and satellites will find this book to be very useful. Contains state-of-the-art developments on multi-modal computing Shines a focus on algorithms and applications Presents novel deep learning topics on multi-sensor fusion and multi-modal deep learning

This final report describes multiple Unmanned Ground Vehicles (UGVs) and their autonomous navigation techniques developed by the US-Asian Team that can safely expedite reconnaissance in cluttered urban environments. The developed multi-UGV system consists of four high-performance UGVs with efficient and intelligent autonomous navigation capabilities. They can detect, locate, classify, recognize and track suspicious Objects of Interest (OOI) and then neutralize any confirmed threats while mapping the entire indoor and outdoor environments. As a result of the development and implementation of the hybrid feature-based/scan-matching Simultaneous Localization and Mapping (SLAM) technique, the multi-UGV system can localize OOI and build a map within the targeted accuracy of 25 cm. The (1) implementation of the model-predictive control and the hybrid SLAM and (2) the Occupancy Grid Mapping (OGM) on the Graphic Processing Unit (GPU) after formulating them within a single recursive Bayesian estimation framework, this enables autonomous navigation at speeds of at least 8 km/h. Performance tests in two buildings and one outdoor area have shown the efficacy of the developed multi-UGV system.

The ability for vehicles to navigate unknown environments is critical for autonomous operation. Mapping of a vehicle's environment and self-localization within that environment are especially difficult for an Unmanned Aerial Vehicle (UAV) due to the complexity of UAV attitude and motion dynamics, as well as interference from external influences such as wind. By using a stable vehicle platform and taking advantage of the geometric structure typical of most indoor environments, the complexity of the localization and mapping problem can be reduced. Interior wall and obstacle location can be measured using low-cost range sensors. Relative vehicle location within the mapped environment can then be determined. By alternating between mapping and localization, a vehicle can explore its environment autonomously. This paper examines available low-cost range sensors for suitability in solving the mapping and localization problem. A control system and navigation algorithm are developed to perform mapping of indoor environments and localization. Simulation and experimental results are provided to determine feasibility of the proposed approach to indoor navigation.

In this book, modeling and control design of electric motors, namely step motors, brushless DC motors and induction motors, are considered. The book focuses on recent advances on feedback control designs for various types of electric motors, with a slight emphasis on stepper motors. For this purpose, the authors explore modeling of these devices to the extent needed to provide a high-performance controller, but at the same time one amenable to model-based nonlinear designs. The control designs focus primarily on recent robust adaptive nonlinear controllers to attain high performance. It is shown that the adaptive robust nonlinear controller on its own achieves reasonably good performance without requiring the exact knowledge of motor parameters. While carefully tuned classical controllers often achieve required performance in many applications, it is hoped that the advocated robust and adaptive designs will lead to standard universal controllers with minimal need for fine tuning of control parameters.

This very provocative book takes the reader on a “think-out-of-the-box” journey through the development of a treatment regimen for multiple myeloma called “dtZ”. It is a firsthand account of how more than 50 patients with myeloma were given a non-toxic, precisely-targeted, anti-cancer treatment that was specifically adapted to their individual cancers. These Individualized Anti-Cancer Targeted Therapies (smart bombs) have produced amongst the best responses as well as survival rates for myeloma. Accordingly, the author argues that some patients might even have been “cured” of their cancers.The concepts and logic behind “dtZ” are carefully presented in simple language so that both doctors and patients can easily understand them. Numerous tables and figures are provided, together with clear and simple explanations. This book is a valuable resource for all patients with myeloma who want to get the most out of their treatment by individualizing treatment to suit their needs, particularly for patients who have just been diagnosed with myeloma and who are taking that very important first step in their treatment. It is also a useful guide for doctors, nurses and researchers who treat and/or study myeloma.