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A mobile robot must be able to determine its own position in the world. To support truly autonomous navigation, we present a system that builds and maintains its own models of world locations and uses these models to recognize its world position from stereo vision input. The system is designed to be robust with respect to input errors and to respond to a gradually changing world by updating the world location models. We present results from tests of the system that demonstrate its reliability. The model builder and recognition system fit into a planned world modeling system that we describe.

Representing the world in terms of visible surfaces and the freespacesexisting between these surfaces and the viewer is an important problemsin robotics. Recently, researchers have proposed using the 3DsDelaunay Tessellation for representing 3D stereo vision data and thesfreespace determined therefrom. We discuss problems with using thes3D Delaunay Tessellation as the basis of the representation andspropose an alternative representation that we are currentlysinvestigating. This new representation is appropriate for planningsmobile robot navigation and promises to be robust when using stereosdata that has errors and uncertainty.

Hexplorer is a six-legged walking robot developed at the University of Waterloo. The robot is controlled by a network of seven digital signal processors, six of which control three motors each, for a total of 18 motors. Brand new custom electronics were designed to house the digital signal processors and associated circuitry. A variable duty factor wave gait, developed by Yoneda et al. was simulated and implemented on the robot. Simulation required an in-depth kinematic analysis that was complicated by the mechanical design of parallel mechanism comprising the legs. These complications were handled in both simulation and implementation. However, due to mechanical issues Hexplorer walked for only one or two steps at a time.

Hexplorer is a six-legged walking robot developed at the University of Waterloo. The robot is controlled by a network of seven digital signal processors, six of which control three motors each, for a total of 18 motors. Brand new custom electronics were designed to house the digital signal processors and associated circuitry. A variable duty factor wave gait, developed by Yoneda et al. was simulated and implemented on the robot. Simulation required an in-depth kinematic analysis that was complicated by the mechanical design of parallel mechanism comprising the legs. These complications were handled in both simulation and implementation. However, due to mechanical issues Hexplorer walked for only one or two steps at a time.

Randomly scattered sensors may cause sensing holes and redundant sensors. In carrier-based sensor relocation, mobile robots (with limited capacity to carry sensors) pick up additional or redundant sensors and relocate them at sensing holes. In the only known localized algorithm, robots randomly traverse field and act based on identified pair of spare sensor and coverage hole. We propose a Market-based Sensor Relocation (MSR) algorithm, which optimizes sensor deployment location, and introduces bidding and coordinating among neighboring robots. Sensors along the boundary of each hole elect one of them as the representative, which bids to neighboring robots for hole filling service. Robot randomly explores by applying Least Recently Visited policy. It chooses the best bid according to Cost over Progress ratio and fetches a spare sensor nearby to cover the corresponding sensing hole. Robots within communication range share their tasks to search for better possible solutions. Simulation shows that MSR outperforms the existing competing algorithm G-R3S2 significantly on total robot traversed path and energy, and time to cover holes, slightly on number of sensors needed to cover the hole, and the cost of additional messages for bidding and deployment location sharing.

Randomly scattered sensors may cause sensing holes and redundant sensors. In carrier-based sensor relocation, mobile robots (with limited capacity to carry sensors) pick up additional or redundant sensors and relocate them at sensing holes. In the only known localized algorithm, robots randomly traverse field and act based on identified pair of spare sensor and coverage hole. We propose a Market-based Sensor Relocation (MSR) algorithm, which optimizes sensor deployment location, and introduces bidding and coordinating among neighboring robots. Sensors along the boundary of each hole elect one of them as the representative, which bids to neighboring robots for hole filling service. Robot randomly explores by applying Least Recently Visited policy. It chooses the best bid according to Cost over Progress ratio and fetches a spare sensor nearby to cover the corresponding sensing hole. Robots within communication range share their tasks to search for better possible solutions. Simulation shows that MSR outperforms the existing competing algorithm G-R3S2 significantly on total robot traversed path and energy, and time to cover holes, slightly on number of sensors needed to cover the hole, and the cost of additional messages for bidding and deployment location sharing.

Rugged terrains, including much of the earth's surface, other planets, and many man-made structures, are inaccessible to wheeled and tracked vehicles. This has inspired research into legged vehicles. Prior to the research described here, virtually all legged vehicle designs relied on the concept of mounting actuated leg-type mechanisms onto a single rigid frame or chassis. This dissertation research explores and advances a novel vehicle concept that uses passive legs attached to an actuated multibody structure. This new vehicle moves only its actuated body trunk to achieve locomotion; thus moving in a manner similar to that used by insect larvae known as caterpillars. The passive-legged design is termed a "crawling" vehicle, to differentiate it from "walking" vehicles, which have powered legs. A conceptual design for the proposed vehicle was developed using insights from observations of caterpillar specimen geometry, gaits, leg trajectories, and ranges of motion. The flexible, segmented body of the robot is realized using a series of actuated truss-like mechanisms, resulting in a configuration similar to the body structure of caterpillars. A computer simulation was developed to verify the concept and to assist in creating future designs. This simulation includes a parametric model of the robot structure, an efficient kinematics model, a motion programming method based on six-dimensional parametric cubic trajectories, static stability analysis, actuator velocity and acceleration analysis, wire-frame animations, and rendering, thus providing synthesis and analysis tools for this new class of vehicle. Results of this work show that by using properly designed Stewart-Gough platform mechanisms for the vehicle multibody structure, a range of motion very similar to that of caterpillars is achievable. Simulation tests showed that imitating the caterpillars" primary gait (or stepping sequence) yields superior speed and efficiency, with little reduction of stability, when compared to a simpler, more obvious gait. With proper controls, this crawling vehicle will, like its biological counterpart, be intrinsically stable and have excellent maneuverability over rough terrain. The crawling vehicle is shown to be a viable legged locomotion system that may prove to have superior rough terrain mobility to all previous types of man-made land vehicles. / Ph. D.

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.