Controlling systems with multiple robots is quickly becoming the next large hurdle that must be overcome for groups of robots to successfully function as a team. An agent oriented approach for this problem is presented in this thesis. By using an agent oriented method, the robots can act independently yet still work together. To be able to establish communities of robots, a basic agent oriented control system for each robot must first be implemented. This thesis introduces a novel method to create Physical Robot Agents, promoting a separation of cognitive and reactive behaviours into a two layer system. These layers are further abstracted into key subsections that are required for the Physical Robot Agents to function. To test this architecture, experiments are performed with physical robots to determine the feasibility of this approach. <br /><br /> A real-time implementation of a Physical Robot Agent would greatly expand its field of use. The speed of internal communication is analyzed to validate the application of this architecture to real-time tasks. <br /><br /> It is concluded that the Physical Robot Agents are well suited for multiple robot systems and that real-time applications are feasible.

In recent years, due to the international competition, soaring cost of land and personnel, aging population, low birth rate¡Ketc, resulting in the recession of the competitiveness of traditional industries in Taiwan. Manpower is needed to monitor the manufacturing process, however, only a worker can¡¦t endure such kind of repetitive workload; on the other hand, it¡¦s not economic to hire more workers to share the workload. Therefore, we expect robots to replace human resources in the manufacturing process. With the advance of science and technology, the mobile robot must equip intelligent judgments. For instance, obstacle avoidance, a way to avoid damage being caused by collision with the obstacles. In general, there are some tables, chairs and the electrical equipment in the office. In the dynamic obstacles case, the robot is effective and immediate response to determine while the people are walking, the staff members to maintain a work efficiency, and security through complex environments. It is the primary topics of discussion. Another important function is path planning, such as the patrol, and the global path planning. Let the mobile robot reach the specified target successfully. In the remote monitoring case, let users know the actual situation of the mobile robot. For example, records of patrol information and specify the action type to move. Therefore, this thesis presents a project of the indoor integrated intelligent mobile robots, including obstacle avoidance, path planning, and remote monitoring of the unknown environment.

In this dissertation, the mapping of outdoor environments and localization of a mobile robot in that setting is considered. It is well known that in the absence of a map or precise pose estimates, localization and mapping is a coupled problem. However, in this dissertation this problem is decoupled in to two disjoint steps / mapping and localization on the acquired map. First the images of the outdoor environment is downloaded from a website such as Google Earth and then these images are processed by utilizing several artificial neural network topologies to create maps. Once these maps are obtained, the localization is done by using Monte Carlo localization. This dissertation addresses a solution for the information which is most of the time taken for granted in most studies / a prior map of environment. Mapping is solved by using a novel approach / the map of the environment is created by processing satellite images. Several global localization techniques are developed and evaluated to be used with these map so as to localize a mobile robot globally. The outcome of this novel approach presented here may serve as a virtual GPS. Mobile phone applications can localize a user within a circle of uncertainty without GPS. This crude localization may be used to download relevant satellite images of the local environment. Once the mobile robot is localized on the map created from the satellite images by using available techniques in the literature i.e. Monte Carlo localization, it may be claimed that it is localized on Earth.

Developing innovative ideas as part of engineering design can be limited by the field of technology and the engineer's or design team's understanding of the field. Without sufficient understanding of an emerging technical field, ideation may be hampered by reinventing the proverbial wheel or by a lack of knowledge of the underlying physical principles and state of technology. The research presented here seeks to develop a tool and methodology intended to strengthen a designer’s or design team’s understanding of a field and relevant technologies in order to foster creative and innovative solutions. The presented inductive methodology consists of conducting a thorough review of existing relevant developing or commercially available technologies in order to obtain characteristic property data to be used as a basis of understanding. Analysis of the plotted data may lead to understanding existing trends, identifying voids where opportunities exist to expand the design space and general insights into the field. The effectiveness of using empirical data to look for innovation is investigated in the domain of highly mobile robots. Senior cadets from USAFA and UT Austin perform concept generation sessions before and after utilizing the proposed methodology to validate the effectiveness of the approach. The study at UT Austin validates the proposed methodology by measuring the quantity, quality, and novelty of the concepts generated before and after exposure to the methodology. These experiments demonstrate that state-of-technology design tools provide an effective foundation and platform for designers to generate a larger quantity of concepts. To further investigate the effectiveness of the proposed methodology, it is used to develop a device within the field of highly mobile robotics. There exist applications of highly mobile robots which require innovative solutions with regard to overcoming obstacles, payload capacity, energy storage and minimizing power requirements. The methodology allows for the development of innovative concepts, and the embodiment and manufacture of a particular solution. The mechanical design solutions to multiple design challenges are presented, and the prototyped device proves capable of expanding the existing design space in terms of its performance with respect to the metrics mentioned above. / text

Mobile robots are increasingly being used to perform tasks in unknown environments. The potential of robots to undertake such tasks lies in their ability to intelligently and efficiently locate and interact with objects in their environment. My research focuses on developing algorithms to plan paths for mobile robots in a partially known environment observed by an overhead camera. The environment consists of dynamic obstacles and targets. A new methodology, Extrapolated Artificial Potential Field, is proposed for real time robot path planning. An algorithm for probabilistic collision detection and avoidance is used to enhance the planner. The aim of the robot is to select avoidance maneuvers to avoid the dynamic obstacles. The navigation of a mobile robot in a real-world dynamic environment is a complex and daunting task. Consider the case of a mobile robot working in an office environment. It has to avoid the static obstacles such as desks, chairs and cupboards and it also has to consider dynamic obstacles such as humans. In the presence of dynamic obstacles, the robot has to predict the motion of the obstacles. Humans inherently have an intuitive motion prediction scheme when planning a path in a crowded environment. A technique has been developed which predicts the possible future positions of obstacles. This technique coupled with the generalized Voronoi diagram enables the robot to safely navigate in a given environment.

We propose and solve a synchronized path following problem for a differential drive robot modeled as a dynamic unicycle and an Euler-Lagrange system. Each system is assigned a simple closed curve in its output space. The outputs of systems must approach and traverse their assigned curves while synchronizing their motions along the paths. The synchronization problems we study in this thesis include velocity synchronization and position synchronization. Velocity synchronization aims to force the velocities of the systems be equal on the desired paths. Position synchronization entails enforcing a positional constraint between the systems modeled as a constraint function on the paths. After characterizing feasible positional constraints, a finite-time stabilizing control law is used to enforce the position constraint.

Mobile robotics has become a rapidly developing field of interdisciplinary research within robotics. This promising field has attracted the attention of academicy, industry, several government agencies. Currently from security to personal service mobile robots are being used in a variety of tasks. The use of such robots is expected to only increase in the near future. In this study, it is aimed to design and manufacture a versatile robot base. This base is aimed to be the main driving unit for various applications performed both indoors and outdoors ranging from personal service and assistance to military applications. The study does not attempt to individually address any specific application, indeed it is aimed to shape up a robotic module that can be used in a wide range of application on different terrain with proper modification. The robot base is specifically designed for mixed terrain applications, yet this study attempts to provide some guidelines to help robot designers. The manufactured robot base is tested with tracks, wheels, and with both tracks and wheels, results are provided as guidelines to robot designers. Last but no the least, this study aims to obtain the know-how of building functional and flexible robots in Turkey by facilitating local resources as much as possible.

In this thesis, semantic mapping is understood to be the process of putting a tag or label on objects or regions in a map. This label should be interpretable by and have a meaning for a human. The use of semantic information has several application areas in mobile robotics. The largest area is in human-robot interaction where the semantics is necessary for a common understanding between robot and human of the operational environment. Other areas include localization through connection of human spatial concepts to particular locations, improving 3D models of indoor and outdoor environments, and model validation. This thesis investigates the extraction of semantic information for mobile robots in outdoor environments and the use of semantic information to link ground-level occupancy maps and aerial images. The thesis concentrates on three related issues: i) recognition of human spatial concepts in a scene, ii) the ability to incorporate semantic knowledge in a map, and iii) the ability to connect information collected by a mobile robot with information extracted from an aerial image. The first issue deals with a vision-based virtual sensor for classification of views (images). The images are fed into a set of learned virtual sensors, where each virtual sensor is trained for classification of a particular type of human spatial concept. The virtual sensors are evaluated with images from both ordinary cameras and an omni-directional camera, showing robust properties that can cope with variations such as changing season. In the second part a probabilistic semantic map is computed based on an occupancy grid map and the output from a virtual sensor. A local semantic map is built around the robot for each position where images have been acquired. This map is a grid map augmented with semantic information in the form of probabilities that the occupied grid cells belong to a particular class. The local maps are fused into a global probabilistic semantic map covering the area along the trajectory of the mobile robot. In the third part information extracted from an aerial image is used to improve the mapping process. Region and object boundaries taken from the probabilistic semantic map are used to initialize segmentation of the aerial image. Algorithms for both local segmentation related to the borders and global segmentation of the entire aerial image, exemplified with the two classes ground and buildings, are presented. Ground-level semantic information allows focusing of the segmentation of the aerial image to desired classes and generation of a semantic map that covers a larger area than can be built using only the onboard sensors.

El siguiente trabajo busca desarrollar y testear un controlador para un robot móvil con fines agrícolas. Enmarcado en un proyecto más grande, que actualmente desarrolla un prototipo de robot móvil con desplazamiento autónomo para colaborar en las tareas agropecuarias, este trabajo parte de las ecuaciones cinemáticas desarrolladas para este prototipo y desarrolla una estrategia de control mediante torque computado para el desplazamiento autónomo del vehículo en el medio y se realizan simulaciones de las mismas. Realizado este trabajo y obteniendo resultados certeros se deja todo pronto para continuar con la instancia experimental en el prototipo. / The following dissertation tries to develop and test a movil robot controller for agricultural purposes. Framed in a bigger proyect that is currently developing a mobile robot prototype with autonomous movement to help with agricultural work, this work starts in the kinematic equations developed for the prototype and develops a control strategy through computed torque control for the autonomous movement of the vehicle and simulations are performed of such computation. With this work finished and with the results obtained is ready to continue with the experimental instance in the prototype.

Cette thèse concerne la perception de l’environnement pour le guidage automatique d’un robot mobile. Lorsque l’on souhaite réaliser un système de navigation autonome, plusieurs éléments doivent être abordés. Parmi ceux-ci nous traiterons de la franchissabilité de l’environnement sur la trajectoire du véhicule. Cette franchissabilité dépend notamment de la géométrie et du type de sol mais également de la position du robot par rapport à son environnement (dans un repère local) ainsi que l’objectif qu’il doit atteindre (dans un repère global). Les travaux de cette thèse traitent donc de la perception de l’environnement d’un robot au sens large du terme en adressant la cartographie de l’environnement et la localisation du véhicule. Pour cela un système de fusion de données est proposé afin d’estimer ces informations. Ce système de fusion est alimenté par plusieurs capteurs dont une caméra, un télémètre laser et un GPS. L’originalité de ces travaux porte sur la façon de combiner ces informations capteurs. A la base du processus de fusion, nous utilisons un algorithme d’odométrie visuelle basé sur les images de la caméra. Pour accroitre la précision et la robustesse l’initialisation de la position des points sélectionnés se fait grâce à un télémètre laser qui fournit les informations de profondeur. De plus, le positionnement dans un repère global est effectué en combinant cette odométrie visuelle avec les informations GPS. Pour cela un procédé a été mis en place pour assurer l’intégrité de localisation du véhicule avant de fusionner sa position avec les données GPS. La cartographie de l’environnement est toute aussi importante puisqu’elle va permettre de calculer le chemin qui assurera au véhicule une évolution sans risque de collision ou de renversement. Dans cette optique, le télémètre laser déjà présent dans le processus de localisation est utilisé pour compléter la liste courante de points 3D qui matérialisent le terrain à l’avant du véhicule. En combinant la localisation précise du véhicule avec les informations denses du télémètre il est possible d’obtenir une cartographie précise, dense et géo-localisée de l’environnement. Tout ces travaux ont été expérimentés sur un simulateur robotique développé pour l’occasion puis sur un véhicule tout-terrain réel évoluant dans un monde naturel. Les résultats de cette approche ont montré la pertinence de ces travaux pour le guidage autonome de robots mobiles. / This thesis addresses the perception of the environment for the automatic guidance of a mobile robot. When one wishes to achieve autonomous navigation, several elements must be addressed. Among them we will discuss the traversability of the environment on the vehicle path. This traversability depends on the ground geometry and type and also the position of the robot in its environment (in a local coordinate system) taking into acount the objective that must be achieved (in a global coordinate system).The works of this thesis deal with the environment perception of a robot inthe broad sense by addressing the mapping of the environment and the location of the vehicle. To do this, a data fusion system is proposed to estimate these informations. The fusion system is supplied by several low cost sensors including a camera, a rangefinder and a GPS receiver. The originality of this work focuses on how to combine these sensors informations. The base of the fusion process is a visual odometry algorithm based on camera images. To increase the accuracy and the robustness, the initialization of the selected points position is done with a rangefinder that provides the depth information.In addition, the localization in a global reference is made by combining the visual odometry with GPS information. For this, a process has been established to ensure the integrity of localization of the vehicle before merging its position with the GPS data. The mapping of the environment is also important as it will allow to compute the path that will ensure an evolution of the vehicle without risk of collision or overturn. From this perspective, the rangefinder already present in the localization process is used to complete the current list of 3D points that represent the field infront of the vehicle. By combining an accurate localization of the vehicle with informations of the rangefinder it is possible to obtain an accurate, dense and geo-located map environment. All these works have been tested on a robotic simulator developed for this purpose and on a real all-terrain vehicle moving in a natural world. The results of this approach have shown the relevance of this work for autonomous guidance of mobile robots.