This thesis presents an adaptive and reconfigurable mobile robot: the Dual Mobile Robot (DMR). It is driven by two adaptive track-wheel driving modules that combine wheels and tracks to allow real-time interchangeability according to terrain condition. The DMR can automatically convert from a wheel-based robot into a track-based robot by rotating the track-wheel driving modules by 90 degrees, either only tracks or wheels contact with the ground without any interference. It can be driven as a wheel-based robot when operating over a paved road to achieve higher speed and low energy consumption, and as a track-based robot over uneven terrain. In addition, unlike most state-of-the-art mobile robot designs that have an integrated architecture, this design provides a modular architecture which allows modifications and upgrades to be performed via simple replacements or local changes of modules. To establish the modular architecture, this research utilized a unique design paradigm, “Design for product adaptability”. A function-based design process for product adaptability has been conducted in the conceptual design stage. By following the design process, two types of design alternatives of the DMR have been created. After the best product configuration was chosen through evaluation and prioritization, the selected configuration has been implemented by detail design. The DMR prototype was developed and tested to demonstrate its adaptability and advanced mobility functions in real-world environments. The experimental results successfully validated the hypothesis of the proposed robot with its track-wheel interchangeable ability, significantly exceeding the capability of other existing systems.

Orientador: Paulo Roberto Gardel Kurka / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-20T13:27:04Z (GMT). No. of bitstreams: 1 DelgadoVargas_JaimeArmando_M.pdf: 4350704 bytes, checksum: 8e7dab5b1630b88bde95e287a62b2f7e (MD5) Previous issue date: 2012 / Resumo: Este trabalho apresenta a implementação de um sistema de navegação com visão estereoscópica em um robô móvel, que permite a construção de mapa de ambiente e localização. Para isto é necessário conhecer o modelo cinemático do robô, técnicas de controle, algoritmos de identificação de características em imagens (features), reconstrução 3D com visão estereoscópica e algoritmos de navegação. Utilizam-se métodos para a calibração de câmera desenvolvida no âmbito do grupo de pesquisa da FEM/UNICAMP e da literatura. Resultados de análises experimentais e teóricas são comparados. Resultados adicionais mostram a validação do algoritmo de calibração de câmera, acurácia dos sensores, resposta do sistema de controle, e reconstrução 3D. Os resultados deste trabalho são de importância para futuros estudos de navegação robótica e calibração de câmeras / Abstract: This paper presents a navigation system with stereoscopic vision on a mobile robot, which allows the construction of environment map and location. In that way must know the kinematic model of the robot, algorithms for identifying features in images (features) as a Sift, 3D reconstruction with stereoscopic vision and navigation algorithms. Methods are used to calibrate the camera developed within the research group of the FEM / UNICAMP and literature. Results of experimental and theoretical analyzes are compared. Additional results show the validation of the algorithm for camera calibration, accuracy of sensors, control system response, and 3D reconstruction. These results are important for future studies of robotic navigation and calibration of cameras / Mestrado / Mecanica dos Sólidos e Projeto Mecanico / Mestre em Engenharia Mecânica

This thesis aims to design an appropriate human-following solution for a mobile robot. The research can be characterised as interactive model building for a Human Robot Interaction (HRI) scenario. It studies possible proposals for the robot system that learns to accomplish the task autonomously, based on the human preference about the positions and movements of the robot during the interaction. A multilayered feedforward network framework with backpropagation is the adopted learning strategy. The research breaks the task of following a human into three independent behaviours: social positioning, human avoidance and obstacle avoidance. Social positioning is the behaviour that moves the robot, via reasonable paths, to the most appropriate location to follow the human. Both the location and the paths reflect the preference of the human, which varies by individual. The main body of the research therefore proposes a using-while-learning system for this behaviour such that the robot can adapt to the human’s preference autonomously. This research investigated multilayered feedforward networks with backpropagation learning to fulfil the social learning task. This learning model is less used in HRI because a complete set of correct training data doesn’t exist as the human preference is initially unknown. The research proposes a novel method to generate the training data during the operation of learning and introduces the concept of adaptive and reactive learning. A novel training scheme that combines the two learning threads has been proposed, in which the learning is fast, robust and able to adapt to new features of the human preference online. The system enables the behaviour to be a real using-while-learning system as no pre-training of any form is needed to ensure the successful performance of the behaviour. Extensive simulations and interactive experiments with humans have also been conducted to prove the robustness of the system.

Este trabalho apresenta o desenvolvimento dos algoritmos de controle de um robô móvel autônomo para coleta de lixo. O objetivo do robô é coletar latas de refrigerante espalhadas pelo chão. O sistema de navegação do robô foi implementado utilizando a arquitetura denominada \"Motor-Schema\". Essa arquitetura fornece um método para projetar comportamentos primitivos que atuam em forma paralela para realizar ações robóticas inteligentes em resposta aos estímulos do ambiente. O sistema de controle apresentado foi constituído por vários comportamentos primitivos que, coordenados, permitiram ao robô explorar de forma segura um ambiente desconhecido, detectar e coletar o lixo e levá-lo num depósito determinado. Os algoritmos desenvolvidos foram testados utilizando uma ferramenta de simulação 2D denominada Player/Stage. Os resultados obtidos mostraram que a solução apresentada é adequada para resolver a aplicação robótica de coleta de lixo. / This work presents the control system for an autonomous mobile robot for soda can collection. The navigation system is implemented using a reactive architecture called \"Motor-Schema\". This architecture provides a methodology to design primitive behaviors that can act in a distributed and parallel manner to yield intelligent robotic actions in response to environmental stimuli. The control system is composed of several primitive behaviors, which enable the robot explore an unknown environment, detect and collect the soda cans and navigate toward a soda can reservoir. The algorithms are tested using Player/Stage, a software for 2D simulation. The results show that the solution is suitable for soda can collection.

CoordenaÃÃo de AperfeÃoamento de Pessoal de NÃvel Superior / This work proposes a study and application of advanced controller to trajectory tracking of wheeled mobile robots. This kind of problem is a challenger for controllers once its models has two inputs and three outputs and is a non-linear model. In the literature there are various solutions to wheeled mobile robots trajectory tracking, among them the Model Predictive Control (MPC) with linearization model and a non linear control which in this work will be nominated as Klancar Controller. The Predictive Controllers can be applied efficiently in plants which has multiple inputs an multiple outputs, in situation that a future reference trajectory is known and systems with input and output constraints . However, the main disadvantage of MPC is the high computational effort which limits its practical application. Thus, this specific controller uses the plants linearization model. On the other hand, the Klancar Controller may be more efficient than the ones based on linear models, once the model is non linear. However, its solution, by definition, does not match the optimized criteria which can be a disadvantage mainly in systems that has constrains and a known future reference. Furthermore, this work proposes the application of the Predictive Control Extended Prediction Self Adaptive Control (EPSAC) to wheeled mobile robot trajectory tracking. This control strategy uses explicitly the non linear robot model, future reference, constraints on the variables and has a optimized solution. And, to the matter of this work, it has not been found reports of the EPSAC applied in mobile robotics, and is thus an unprecedented application. Simulation results are presented comparing the controllers studied using performance indices. Else, the controllers were applied in a mobile robot. / Este trabalho propÃe o estudo e aplicaÃÃo de controladores avanÃados ao seguimento de trajetÃrias de robÃs mÃveis com rodas. Este tipo de problema à bastante desafiador do ponto de vista de controle uma vez que o modelo tem duas entradas e trÃs saÃdas, alÃm disso, trata-se de um modelo nÃo linear. Na literatura existem diversas soluÃÃes para o controle de trajetÃria de robÃs mÃveis, dentre eles tem-se o Controle Preditivo Baseado em Modelo (MPC) por meio de modelos linearizados e um controlador nÃo linear denominado neste trabalho de controlador de Klancar. Os controladores preditivos podem ser aplicados de forma eficiente em plantas com modelos multivariÃveis, em situaÃÃes na qual a trajetÃria futura de referÃncia à conhecida e em sistemas com restriÃÃes nas vaiÃveis de entrada e de saÃda. PorÃm, a principal desvantagem do MPC linearizado à o alto custo computacional o que limita as aplicaÃÃes prÃticas. AlÃm disso, esse controlador especÃfico utiliza um modelo linearizado da planta. Por outro lado, o controlador de Klancar pode ser mais eficiente que os baseados em modelos lineares, devido Ãs nÃo linearidades inerentes do modelo. No entanto, a sua soluÃÃo, por definiÃÃo, nÃo corresponde a critÃrios Ãtimos o que pode representar uma desvantagem principalmente em sistemas com restriÃÃes e referÃncia futura conhecida. AlÃm disso, neste trabalho à proposta a aplicaÃÃo do controle preditivo EPSAC (Extended Prediction Self Adaptive Control) para o controle de seguimento de trajetÃrias. Esta estratÃgia utiliza de forma explÃcita o modelo nÃo linear do robÃ, a referÃncia futura, as restriÃÃes nas variÃveis do robà e soluÃÃo corresponde a um critÃrio Ãtimo. Atà onde foi pesquisado pelo autor deste trabalho, nÃo existem relatos da utilizaÃÃo do EPSAC na robÃtica mÃvel, sendo desta forma uma aplicaÃÃo inÃdita. Resultados de simulaÃÃo sÃo apresentados comparando os controladores estudados, utilizando Ãndices de desempenhos. AlÃm disso, os mesmo foram implementados em um robà mÃvel.

When animals explore a new environment, they do not acquire a precise map of the places visited. In fact, research has shown that learning is a recurring process. Over time, new information helps the animal to update their perception of the locations it has visited. Yet, they are still able to use the fuzzy and often incomplete representation to find their way home. This process has been termed the cognitive mapping process. The work presented in this thesis uses a mobile robot equipped with sonar sensors to investigate the nature of such a process. Specifically, what is the information that is fundamental and prevalent in spatial navigation? Initially, the robot is instructed to compute a “cognitive map” of its environment. Since a robot is not a cognitive agent, it cannot, by definition, compute a cognitive map. Hence the robot is used as a test bed for understanding the cognitive mapping process. Yeap’s (1988) theory of cognitive mapping forms the foundation for computing the robot’s representation of the places it has visited. He argued that a network of local spaces is computed early in the cognitive mapping process. Yeap coined these local spaces as Absolute Space Representations (ASRs). However, ASR is not just a process of partitioning the environment into smaller local regions. The ASRs describe the bounded space that one is in, how one could leave that space (exits) and how the exits serves to link the ASRs to form a network that serves as the cognitive map (see Jefferies (1999)). Like the animal’s cognitive map, ASRs are not precise geometrical maps of the environment but rather, provide a rough shape or feel of the space the robot is currently in. Once the robot computes its “cognitive map”, it is then, like foraging and hoarding animals, instructed to find its way home. To do so, the robot uses two crucial pieces of information: distance between exits of ASRs and relative orientation of adjacent ASRs. A simple animal-like strategy was implemented for the robot to locate home. Results from the experiments demonstrated the robot’s ability to determine its location within the visited environment along its journey. This task was performed without the use of an accurate map. From these results and reviews of various findings related to cognitive mapping for various animals, we deduce that: Different animals have different sensing capabilities. They live in different environments and therefore face unique challenges. Consequently, they evolve to have different navigational strategies. However, we believe two crucial pieces of information are inherent in all animals and form the fundamentals of navigation: distance and orientation. Higher level animals may encode and may even prefer richer information to enhance the animal’s cognitive map. Nonetheless, distance and orientation will always be computed as a core process of cognitive mapping. We believe this insight will help future research to better understand the complex nature of cognitive mapping.

Navigation and guidance of mobile robots towards steady or maneuvering objects (targets) is one of the most important areas of robotics that has attracted a lot of attention in recent decades. However, in most of the existing methods, both the line-of-sight angle (bearing) and the relative distance (range) are assumed to be available for navigation and guidance algorithms. There is also a relatively large body of research on navigation and guidance with bearings-only measurements. In contrast, only a few results on navigation and guidance towards an unknown target using range-only measurements have been published. Various problems of navigation, guidance, location estimation and target tracking based on range-only measurements often arise in new wireless networks related applications. Recent advances in these applications allow us to use inexpensive transponders and receivers for range-only measurements which provide information in dynamic and noisy environments without the necessity of line-of-sight. To take advantage of these sensors, algorithms must be developed for range-only navigation. The main part of this thesis is concerned with the problem of real-time navigation and guidance of Wheeled Mobile Robots (WMRs) towards an unknown stationary or moving target using range-only measurements. The range can be estimated using the signal strength and the robust extended Kalman filtering. Several similar algorithms for navigation and guidance termed Equiangular Navigation and Guidance (ENG) laws are proposed and mathematically rigorous proofs of convergence and stability of the proposed guidance laws are given. The experimental investigation into the use of range data for a WMR navigation is documented and the results and discussions on the performance of the proposed guidance strategies are presented, where a wheeled robot successfully approach a stationary or follow a maneuvering target. In order to safely navigate and reliably operate in populated environments, ENG is then modified into Augmented-ENG (AENG), which enables the robot to approach a stationary target or follow an unpredictable maneuvering object in an unknown environment, while keeping a safe distance from the target, and simultaneously preserving a safety margin from the obstacles. Furthermore, we propose and experimentally investigate a new biologically inspired method for local obstacle avoidance and give the mathematically rigorous proof of the idea. In order for the robot to avoid collision and bypass the enroute obstacles in this method, the angle between the instantaneous moving direction of the robot and a reference point on the surface of the obstacle is kept constant. The proposed idea is combined with the ENG law, which leads to a reliable and fast long-range navigation. The performance of both navigation strategy and local obstacle avoidance techniques are confirmed with computer simulations and several experiments with ActivMedia Pioneer 3-DX wheeled robots. The second part of the thesis investigates some challenging problems in the area of wheeled robot navigation. We first address the problem of bearing-only guidance of an autonomous vehicle following a moving target with smaller minimum turning radius compared to that of the follower and propose a simple and constructive navigation law. In compliance with the increasing research on decentralized control laws for groups of mobile autonomous robots, we consider the problems of decentralized navigation of network of WMRs with limited communication and decentralized stabilization of formation of WMRs. New control laws are presented and simulation results are provided to illustrate the control laws and their applications.

In this paper we describe a working, implemented controller for a real, physical mobile robot arm. The controller is composed of a collection of 15 independent behaviors which run, in real time, on a set of 8 loosely coupled on-board 8-bit microprocessors. We describe how these behaviors cooperate to actually seek out and retrieve objects using local sensory data. We also discuss the methodology used to decompose this collection task and the types of spatial representation and reasoning used by the system.

The goal of this work is to navigate through an office environmentsusing only visual information gathered from four cameras placed onboard a mobile robot. The method is insensitive to physical changes within the room it is inspecting, such as moving objects. Forward and rotational motion vision are used to find doors and rooms, and these can be used to build topological maps. The map is built without the use of odometry or trajectory integration. The long term goal of the project described here is for the robot to build simple maps of its environment and to localize itself within this framework.

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.