Mobile robotics research is currently interested on exporting autonomous navigation results achieved in indoor environments, to more challenging environments, such as, for instance, urban pedestrian areas. Developing mobile robots with autonomous navigation capabilities in such urban environments supposes a basic requirement for a upperlevel service set that could be provided to an users community. However, exporting indoor techniques to outdoor urban pedestrian scenarios is not evident due to the larger size of the environment, the dynamism of the scene due to pedestrians and other moving obstacles, the sunlight conditions, and the high presence of three dimensional elements such as ramps, steps, curbs or holes. Moreover, GPS-based mobile robot localization has demonstrated insufficient performance for robust long-term navigation in urban environments. One of the key modules within autonomous navigation is localization. If localization supposes an a priori map, even if it is not a complete model of the environment, localization is called map-based. This assumption is realistic since current trends of city councils are on building precise maps of their cities, specially of the most interesting places such as city downtowns. Having robots localized within a map allows for a high-level planning and monitoring, so that robots can achieve goal points expressed on the map, by following in a deliberative way a previously planned route. This thesis deals with the mobile robot map-based localization issue in urban pedestrian areas. The thesis approach uses the particle filter algorithm, a well-known and widely used probabilistic and recursive method for data fusion and state estimation. The main contributions of the thesis are divided on four aspects: (1) long-term experiments of mobile robot 2D and 3D position tracking in real urban pedestrian scenarios within a full autonomous navigation framework, (2) developing a fast and accurate technique to compute on-line range observation models in 3D environments, a basic step required by the real-time performance of the developed particle filter, (3) formulation of a particle filter that integrates asynchronous data streams and (4) a theoretical proposal to solve the global localization problem in an active and cooperative way, defining cooperation as either information sharing among the robots or planning joint actions to solve a common goal. / Actualment, la recerca en robòtica mòbil té un interés creixent en exportar els resultats de navegació autònoma aconseguits en entorns interiors cap a d'altres tipus d'entorns més exigents, com, per exemple, les àrees urbanes peatonals. Desenvolupar capacitats de navegació autònoma en aquests entorns urbans és un requisit bàsic per poder proporcionar un conjunt de serveis de més alt nivell a una comunitat d'usuaris. Malgrat tot, exportar les tècniques d'interiors cap a entorns exteriors peatonals no és evident, a causa de la major dimensió de l'entorn, del dinamisme de l'escena provocada pels peatons i per altres obstacles en moviment, de la resposta de certs sensors a la il.luminació natural, i de la constant presència d'elements tridimensionals tals com rampes, escales, voreres o forats. D'altra banda, la localització de robots mòbils basada en GPS ha demostrat uns resultats insuficients de cara a una navegació robusta i de llarga durada en entorns urbans. Una de les peces clau en la navegació autònoma és la localització. En el cas que la localització consideri un mapa conegut a priori, encara que no sigui un model complet de l'entorn, parlem d'una localització basada en un mapa. Aquesta assumpció és realista ja que la tendència actual de les administracions locals és de construir mapes precisos de les ciutats, especialment dels llocs d'interés tals com les zones més cèntriques. El fet de tenir els robots localitzats en un mapa permet una planificació i una monitorització d'alt nivell, i així els robots poden arribar a destinacions indicades sobre el mapa, tot seguint de forma deliberativa una ruta prèviament planificada. Aquesta tesi tracta el tema de la localització de robots mòbils, basada en un mapa i per entorns urbans peatonals. La proposta de la tesi utilitza el filtre de partícules, un mètode probabilístic i recursiu, ben conegut i àmpliament utilitzat per la fusió de dades i l'estimació d'estats. Les principals contribucions de la tesi queden dividides en quatre aspectes: (1) experimentació de llarga durada del seguiment de la posició, tant en 2D com en 3D, d'un robot mòbil en entorns urbans reals, en el context de la navegació autònoma, (2) desenvolupament d'una tècnica ràpida i precisa per calcular en temps d'execució els models d'observació de distàncies en entorns 3D, un requisit bàsic pel rendiment del filtre de partícules a temps real, (3) formulació d'un filtre de partícules que integra conjunts de dades asíncrones i (4) proposta teòrica per solucionar la localització global d'una manera activa i cooperativa, entenent la cooperació com el fet de compartir informació, o bé com el de planificar accions conjuntes per solucionar un objectiu comú.

In today's industry it is of mayor concern to keep the manufacturing processes as effective and flexible as possible. The usage of robots and automatic technology is a much known way to achieve the goals of rationalization. The disadvantage lays in the fact that implementation of robots is usually a very resource consuming task. However, in some circumstances a solution to this matter may be to simply implement mobile robots instead of fixed robots. The task of this project is to successfully control and understand the system of a mobile robot in a automatic manufacturing process.

Mobile robots are used on many areas and its demand on extreme terrain, hazardous area, or life-threatening place is increasing to reduce the loss of life. A good decision making capability is essential for successful navigation of autonomous robot and it affect finding the shortest or optimal path within given condition. The wavefront algorithm is simple to apply, yet yield an optimal path for a robot to follow in many different configurations. Although the path created using wavefront algorithm is an optimal in the sense that every node has the same cost, the result is not the best result in global perspective because of the algorithm is inconsiderate on the surrounding condition. To solve this issue and create the best result on global perspective, risk factor analysis method was implemented on the wavefront algorithm to improve the performance. In this work, the relationship between the wavefront algorithm and dynamic programming will be explained to show that the wavefront algorithm obeys the principle of optimality. The simulation result displays better performance on safety, while keeping the travelling distance minimum, if the risk factor is used on the wavefront algorithm and the robot on actual test behave accordingly. This work will contribute on decision making of mobile robot using risk factor method to create a most desirable and safe path. In addition to that, it will demonstrate how the risk factor method can be applied to the mobile robot navigation.

This research is mainly about autonomously navigation of an agricultural wheeled mobile robot in an unstructured outdoor setting. This project has four distinct phases defined as: (i) Navigation and control of a wheeled mobile robot for a point-to-point motion. (ii) Navigation and control of a wheeled mobile robot in following a given path (path following problem). (iii) Navigation and control of a mobile robot, keeping a constant proximity distance with the given paths or plant rows (proximity-following). (iv) Navigation of the mobile robot in rut following in farm fields. A rut is a long deep track formed by the repeated passage of wheeled vehicles in soft terrains such as mud, sand, and snow. To develop reliable navigation approaches to fulfill each part of this project, three main steps are accomplished: literature review, modeling and computer simulation of wheeled mobile robots, and actual experimental tests in outdoor settings. First, point-to-point motion planning of a mobile robot is studied; a fuzzy-logic based (FLB) approach is proposed for real-time autonomous path planning of the robot in unstructured environment. Simulation and experimental evaluations shows that FLB approach is able to cope with different dynamic and unforeseen situations by tuning a safety margin. Comparison of FLB results with vector field histogram (VFH) and preference-based fuzzy (PBF) approaches, reveals that FLB approach produces shorter and smoother paths toward the goal in almost all of the test cases examined. Then, a novel human-inspired method (HIM) is introduced. HIM is inspired by human behavior in navigation from one point to a specified goal point. A human-like reasoning ability about the situations to reach a predefined goal point while avoiding any static, moving and unforeseen obstacles are given to the robot by HIM. Comparison of HIM results with FLB suggests that HIM is more efficient and effective than FLB. Afterward, navigation strategies are built up for path following, rut following, and proximity-following control of a wheeled mobile robot in outdoor (farm) settings and off-road terrains. The proposed system is composed of different modules which are: sensor data analysis, obstacle detection, obstacle avoidance, goal seeking, and path tracking. The capabilities of the proposed navigation strategies are evaluated in variety of field experiments; the results show that the proposed approach is able to detect and follow rows of bushes robustly. This action is used for spraying plant rows in farm field. Finally, obstacle detection and obstacle avoidance modules are developed in navigation system. These modules enables the robot to detect holes or ground depressions (negative obstacles), that are inherent parts of farm settings, and also over ground level obstacles (positive obstacles) in real-time at a safe distance from the robot. Experimental tests are carried out on two mobile robots (PowerBot and Grizzly) in outdoor and real farm fields. Grizzly utilizes a 3D-laser range-finder to detect objects and perceive the environment, and a RTK-DGPS unit for localization. PowerBot uses sonar sensors and a laser range-finder for obstacle detection. The experiments demonstrate the capability of the proposed technique in successfully detecting and avoiding different types of obstacles both positive and negative in variety of scenarios.

In this thesis, an algorithm that improves the performance of the extended Kalman filter (EKF) on the mobile robot localization issue is proposed, which is aided by the cooperation of neural network and fuzzy logic. An EKF is used to fuse the information acquired from both the robot optical encoders and the external sensors in order to estimate the current robot position and orientation. Then the error covariance of the EKF is tracked by the covariance matching technique. When the output of the matching technique does not meet the desired condition, a fuzzy logic is employed to adjust the error covariance matrix to modify it back to the desired value range. Since the fuzzy logic is lack of the capability of learning, a neural network is presented in the algorithm to train the EKF. The simulation results demonstrate that, with the comparison to the odometry and the standard EKF method under the same error divergence condition, the proposed extended Kalman filter effectively improves the accuracy of the localization of the mobile robot system and effectively prevents the filter divergence.

This thesis consists of two parts united by the theme of path/action planning for a mobile robot. Part I presents the Second Opinion Planner (SOP), and Part II presents a new paradigm for navigating, growing, and planning on a Network of Reusable Paths (NRP). Path/action planning is common to both parts in that the planning algorithm must choose the terrain assessment or localization technique at the path-planning stage. Terrain-assessment algorithms follow the trend of low-fidelity at low-cost and high-fidelity at high-cost. Using a high-fidelity method on all the raw terrain data can drastically increase a robot's total path cost (cost of driving, planning, and doing the terrain assessment). SOP is a path-planning algorithm that uses a hierarchy of terrain-assessment methods, from low-fidelity to high-fidelity, and seeks to limit high-cost assessment to areas where it is beneficial. The decision to assess some terrain with a higher-fidelity method is made considering potential path benefits and the cost of assessment. SOP provides a means to triage large amounts of terrain data. The system is demonstrated on simulated problems and in real terrain from an experimental field test carried out on Devon Island, Canada. The SOP plans are quite close to the minimum possible cost. Growing a NRP is an approach to navigation that allows a mobile robot to autonomously explore unmapped, GPS-denied environments. This new paradigm results in closer goal acquisition and a more robust approach to exploration with a mobile robot, when compared to a classic approach to guidance, navigation, and control. A NRP is a simple Simultaneous Localization And Mapping system that can be shown to be a physical embodiment of a Rapidly-exploring Random Tree planner. Simulation results are presented, as well as the results from two different robotic test systems that were tested in planetary analogue environments. NRP offers benefits to planetary exploration by allowing a rover to be used for the parallel scientific investigations. This increases the number of sites that can be investigated in a short time, as compared to a serial approach to exploration. Two mock missions were carried out at planetary analogue sites.

This thesis consists of two parts united by the theme of path/action planning for a mobile robot. Part I presents the Second Opinion Planner (SOP), and Part II presents a new paradigm for navigating, growing, and planning on a Network of Reusable Paths (NRP). Path/action planning is common to both parts in that the planning algorithm must choose the terrain assessment or localization technique at the path-planning stage. Terrain-assessment algorithms follow the trend of low-fidelity at low-cost and high-fidelity at high-cost. Using a high-fidelity method on all the raw terrain data can drastically increase a robot's total path cost (cost of driving, planning, and doing the terrain assessment). SOP is a path-planning algorithm that uses a hierarchy of terrain-assessment methods, from low-fidelity to high-fidelity, and seeks to limit high-cost assessment to areas where it is beneficial. The decision to assess some terrain with a higher-fidelity method is made considering potential path benefits and the cost of assessment. SOP provides a means to triage large amounts of terrain data. The system is demonstrated on simulated problems and in real terrain from an experimental field test carried out on Devon Island, Canada. The SOP plans are quite close to the minimum possible cost. Growing a NRP is an approach to navigation that allows a mobile robot to autonomously explore unmapped, GPS-denied environments. This new paradigm results in closer goal acquisition and a more robust approach to exploration with a mobile robot, when compared to a classic approach to guidance, navigation, and control. A NRP is a simple Simultaneous Localization And Mapping system that can be shown to be a physical embodiment of a Rapidly-exploring Random Tree planner. Simulation results are presented, as well as the results from two different robotic test systems that were tested in planetary analogue environments. NRP offers benefits to planetary exploration by allowing a rover to be used for the parallel scientific investigations. This increases the number of sites that can be investigated in a short time, as compared to a serial approach to exploration. Two mock missions were carried out at planetary analogue sites.

The recent advent of consumer grade Brain-Computer Interfaces (BCI) provides a new revolutionary and accessible way to control computers. BCI translate cognitive electroencephalography (EEG) signals into computer or robotic commands using specially built headsets. Capable of enhancing traditional interfaces that require interaction with a keyboard, mouse or touchscreen, BCI systems present tremendous opportunities to benefit various fields. Movement restricted users can especially benefit from these interfaces. In this thesis, we present a new way to interface a consumer-grade BCI solution to a mobile robot. A Red-Green-Blue-Depth (RGBD) camera is used to enhance the navigation of the robot with cognitive thoughts as commands. We introduce an interface presenting 3 different methods of robot-control: 1) a fully manual mode, where a cognitive signal is interpreted as a command, 2) a control-flow manual mode, reducing the likelihood of false-positive commands and 3) an automatic mode assisted by a remote RGBD camera. We study the application of this work by navigating the mobile robot on a planar surface using the different control methods while measuring the accuracy and usability of the system. Finally, we assess the newly designed interface’s role in the design of future generation of BCI solutions.

Autonomy is the most enabling and the least developed robot capability. A mobile robot is autonomous if capable of independently attaining its objectives in unpredictable environment. This requires interaction with the environment by sensing, assessing, and responding to events. Such interaction has not been achieved. The core problem consists in limited understanding of robot autonomy and its aspects, and is exacerbated by the limited resources available in a small autonomous mobile robot such as energy, information, and space. This thesis describes an efficient biomorphic visual capability that can provide purposeful interaction with environment for a small autonomous mobile robot. The method used for achieving this capability comprises synthesis of an integral paradigm of a purposeful autonomous mobile robot, formulation of requirements for the visual capability, and development of efficient algorithmic and technological solutions. The paradigm is a product of analysis of fundamental aspects of the problem, and the insights found in inherently autonomous biological organisms. Based on this paradigm, analysis of the biological vision and the available technological basis, and the state-of-the-art in vision algorithms, the requirements were formulated for a biomorphic visual capability that provides the situation awareness capability for a small autonomous mobile robot. The developed visual capability is comprised of a sensory and processing architecture, an integral set of motion vision algorithms, and a method for visual ranging of still objects that is based on them. These vision algorithms provide motion detection, fixation, and tracking functionality with low latency and computational complexity. High temporal resolution of CMOS imagers is exploited for reducing the logical complexity of image analysis, and consequently the computational complexity of the algorithms. The structure of the developed algorithms conforms to the arithmetic and memory resources available in a system on a programmable chip (SoPC), which allows complete confinement of the high-bandwidth datapath within a SoPC device and therefore high-speed operation by design. The algorithms proved to be functional, which validates the developed visual capability. The experiments confirm that high temporal resolution imaging simplifies image motion structure, and ultimately the design of the robot vision system.

A mobile robot system provides a grounded platform for a wide variety of interactive systems to be developed and deployed. The mobility provided by the robot presents unique challenges as it must observe the state of the surroundings while observing the state of itself with respect to the environment. The scope of the discipline includes the mechanical and hardware issues, which limit and direct the capabilities of the software considerations. The systems that are integrated into the mobile robot platform include both specific task oriented and fundamental modules that define the core behaviour of the robot. While the earlier can sometimes be developed separately and integrated at a later stage, the core modules are often custom designed early on to suit the individual robot system depending on the configuration of the mechanical components. This thesis covers the issues encountered and the resolutions that were implemented during the development of a low cost mobile robot platform using off the shelf sensors, with a particular focus on the algorithmic side of the system. The incrementally developed modules target the localisation and mapping aspects by incorporating a number of different sensors to gather the information of the surroundings from different perspectives by simultaneously or sequentially combining the measurements to disambiguate and support each other. Although there is a heavy focus on the image processing techniques, the integration with the other sensors and the characteristics of the platform itself are included in the designs and analyses of the core and interactive modules. A visual odometry technique is implemented for the localisation module, which includes calibration processes, feature tracking, synchronisation between multiple sensors, as well as short and long term landmark identification to calculate the relative pose of the robot in real time. The mapping module considers the interpretation and the representation of sensor readings to simplify and hasten the interactions between multiple sensors, while selecting the appropriate attributes and characteristics to construct a multi-attributed model of the environment. The modules that are developed are applied to realistic indoor scenarios, which are taken into consideration in some of the algorithms to enhance the performance through known constraints. As the performance of algorithms depends significantly on the hardware, the environment, and the number of concurrently running sensors and modules, comparisons are made against various implementations that have been developed throughout the project.