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.

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.

The authors present a method for estimating the position and orientation of a ground vehicle in an environment with landmarks. From the geometric relationships, a set of linear equations is derived with a quadratic constraint, which forms the basis for an optimisation problem. The problem is also extended to associating two sets of measurements taken at two successive locations to improve the navigation accuracy. This method is efficient and the performance is robust against large measurement errors.

Beyond the interest of robotics laboratories for the development of dedicated strategies for single vehicle navigation, several laboratories around the world are more and more involved in the general challenging field of cooperative multi-robot navigation. In this context, this work deals with the navigation in formation of a group of Unmanned Ground Vehicles (UGVs) dedicated to structured environments. The complexity of this Multi-Robot System (MRS) does not permit the direct use of neither classical perception nor control techniques. To overcome this problem, this work proposes to break up the overall control dedicated to the achievement of the complex task into a group of accurate and reliable elementary behaviors/controllers (e.g., obstacles avoidance, trajectory tracking, target reaching, navigation in formation, formation reconfiguration, etc.). These behaviors are linked to different information given by the sensors to the actions of vehicles. To guarantee the performances criteria (e.g., stability, convergence, state errors) aimed by the control architecture, the potentialities of hybrid controllers (which controlling continuous systems in the presence of discrete events) are considered. This control architecture is validated for a single vehicle to perform safe and flexible autonomous navigation using an appropriate strategy of navigation through suitable set of waypoints. This flexible navigation allows different vehicle maneuvers between waypoints (e.g., target reaching or obstacle avoidance) without using any trajectory planning nor replanning. The designed control law based on Lyapunov synthesis guarantees the convergence to assigned waypoint while performing safe trajectories. Furthermore, an algorithm to select suitable waypoints' positions, named Optimal Multi-criteria Waypoint Selection (OMWS), in structured environments while taking into account the safe and reliable vehicle movements, and vehicle constraints and uncertainties is proposed. Subsequently, the control architecture is extended to Multi-Robot Formation (MRF) using a combination of Leader-Follower and behavior-based approaches. An important cooperative MRS issues in this thesis is the dynamic reconfiguration of the formation according to the context of navigation (e.g., to pass from a triangle configuration towards a line if the width of the navigation way is not sufficient). The proposed Strategy for Formation Reconfiguration (SFR) guarantees the stability and the safety of the MRS at the time of the transitions between configuration (e.g., line towards square, triangle towards line, etc.). Therefore, a safe, reactive and dynamic MRF is obtained. Moreover, the degrees of multi-robot safety, stability and reliability of the system are quantified via suitable metrics. Simulations and experiments using urban vehicles (VIPALABs) of the Institut Pascal laboratory allow to perform exhaustive experiments of the proposed control architecture for the navigation in formation of a group of UGVs.

This thesis discusses the evaluation, implementation, and testing of several navigation algorithms and feature extraction algorithms using an inertial measurement unit (IMU) and an image capture device (camera) mounted on a ground robot and a quadrotor UAV. The vision-aided navigation algorithms are implemented on data-collected from sensors on an unmanned ground vehicle and a quadrotor, and the results are validated by comparison with GPS data. The thesis investigates sensor fusion techniques for integrating measured IMU data with information extracted from image processing algorithms in order to provide accurate vehicle state estimation. This image-based information takes the forms of features, such as corners, that are tracked over multiple image frames. An extended Kalman filter (EKF) in implemented to fuse vision and IMU data. The main goal of the work is to provide navigation of mobile robots in GPS-denied environments such as indoor environments, cluttered urban environments, or space environments such as asteroids, other planets or the moon. The experimental results show that combining pose information extracted from IMU readings along with pose information extracted from a vision-based algorithm managed to solve the drift problem that comes from using IMU alone and the scale problem that comes from using a monocular vision-based algorithm alone.

Outdoor sensor-based operation of autonomous robots has revealed to be an extremely challenging problem, mainly because of the difficulties encountered when attempting to represent the many uncertainties which are always present in the real world. These uncertainties are primarily due to sensor imprecisions and unpredictability of the environment, i.e., lack of full knowledge of the environment characteristics and dynamics. Two basic principles, or philosophies, and their associated methodologies are proposed in an attempt to remedy some of these difficulties. The first principle is based on the concept of ''minimal model'' for accomplishing given tasks and proposes to utilize only the minimum level of information and precision necessary to accomplish elemental functions of complex tasks. This approach diverges completely from the direction taken by most artificial vision studies which conventionally call for crisp and detailed analysis of every available component in the perception data. The paper will first review the basic concepts of this approach and will discuss its pragmatic feasibility when embodied in a behaviorist framework. The second principle which is proposed deals with implicit representation of uncertainties using Fuzzy Set Theory-based approximations and approximate reasoning, rather than explicit (crisp) representation through calculation and conventional propagation techniques. A framework which merges these principles and approaches is presented, and its application to the problem of sensor-based outdoor navigation of a mobile robot is discussed. Results of navigation experiments with a real car in actual outdoor environments are also discussed to illustrate the feasibility of the overall concept.

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.

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: