Made available in DSpace on 2014-06-11T19:22:34Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-08-18Bitstream added on 2014-06-13T18:49:36Z : No. of bitstreams: 1 bigheti_ja_me_bauru.pdf: 1603477 bytes, checksum: 39e00820c0f650d5c640f29bc5870194 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / A proposta deste trabalho é dotar um robô móvel com a capacidade de mapear e se localizar no ambiente simultaneamente onde tal problema é conhecido na literatura clássica como SLAM (Simultaneous Localizaton and Mapping). Para operar, o robô deve ser capaz de manter uma estimativa da sua posição com base nos sensores embarcados veículo, adquirir e utilizar conhecimento sobre o mundo ao seu redor, possuir a habilidade de reconhecer obstáculos, e responder em tempo real as situações que possam ocorrer neste ambiente. Este trabalho propõe também a utilização de um sensor de ultra-som com varredura frontal de 180 graus, para detecção de landmarks (marcos) naturais em um ambiente interno para construção do mapa na memória do sistema de controle do robô. As informações do deslocamento do robô são fornecidas pelo sistema de odometria com encoder. Essas informações de deslocamento do robô a distância dos landmarks são combinadas através da aplicação do Filtro de Kalman Estendido (EKF), para o cálculode posição e orientação estimados do robô bem como a posição estimada dos landmarks (mapa). Trata-se de um trabalho com resultados preliminares, que tem como contribuição específica realizar a tarefa de localização e mapeamento simultaneamente (SLAM) usando um sensor de ultra-som rotativo. São apresentados também os resultados de simulação da técnica de localização e mapeamento simultâneo usando o Filtro de Kalman Estendido (EKT) e complementadas com avaliações experimentais em ambiente reais, aplicado a um robô móvel trabalhando como um transportador de materiais automatizado no chão de fábrica. Discussões são apresentadas sobre os sensores usados, a complexidade computacional, a associação de dados e a modelagem e controle do robô móvel / The purpose of this paper is to provide a mobile robot with the ability to simultaneously map and locate the environment. This problem is know in classical literature as SLAM (Simultaneous Localization and Mapping). To operate, the robot must be able to maintain an estimation of its position based on sensors attached to the vehicle, acquire and use knowledge about the world around it, have the ability to recognize obstacles and respond in real time situations that may occur in this environment. This paper also proposes the use of an ultrasonic sensor to scan an angle of 180 degrees, for detection of landmarks in a natural environment in order to build the internal map inside the robot's controller memory. The displacement information is provided by the robot odometry system with encoder. This information is combined through the application of Extended Kalmar filter (EKT). This is a preliminary work, which has the specific contribution the task of locating and mapping simultaneously (SLAM) using a rotating ultrasonic sensor. There is also presented the simulation of the technique of simultaneous localization and mapping using the extended Kalman filter (EKT) in addition of experimental evaluations in real environment, applied to a mobile robot working as an automated carried materials on the factory floor. Discussions are presented on the used sensors, the computational complexity, data combination and modeling and control of mobile robot

In the post-9/11 world, new and improved surveillance and information-gathering technologies have become a high-priority problem to be solved. Surveillance systems are often needed in areas too hostile or dangerous for a direct human presence. The field of robotics is being looked to for an autonomous mobile surveillance system. One major problem is the control and coordination of multiple cooperating robots. Researchers have looked to the distributed control strategies found in nature in the form of social insects as an inspiration for new control schemes. Swarm intelligence research centers around the interactions of such systems and how they can be applied to contemporary problems. In this thesis, a surveillance system of mobile autonomous robots based on the principles of swarm intelligence is presented. / Master of Science

<p>Swarm robotics and distributed control offer the promise of enhanced performance and robustness relative to that of individual and centrally-controlled robots, with decreased cost or time-to-completion for certain tasks. Having many degrees of freedom, swarm-related control and estimation problems are challenging specifically when the solutions depend on a great amount of communication among the robots. Swarm controllers minimizing communication requirements are quite desirable.</p><p>Swarms are inherently more robust to uncertainties and failures, including complete loss of individual agents, due to the averaging inherent in convergence and agreement problems. Exploitation of this robustness to minimize processing and communication complexity is desirable.</p><p>This research focuses on simple but robust controllers for swarming problems, maximizing the likelihood of objective success while minimizing controller complexity and specialized communication or sensing requirements.</p><p>In addition, it develops distributed solutions for swarm control by examining and exploiting graph theoretic constructs. Details of specific implementations, such as nonholonomic motion and and numerosity constraints, were explored with some unexpectedly positive results.</p><p>In summary, this research focused on the development of control strategies for the distributed control of a swarm of robots, and graph-theoretic analysis of these controllers. These control strategies specifically consider probabilistic connectivity functions, based on requirements for sensing or communication. The developed control strategies are validated in both simulation and experiment.</p> / Dissertation

Orientador: Marcelo Nicoletti Fanchin / Banca: Jackson Paul Matsuura / Banca: José Alfredo Covolan Ulson / Resumo: A proposta deste trabalho é dotar um robô móvel com a capacidade de mapear e se localizar no ambiente simultaneamente onde tal problema é conhecido na literatura clássica como SLAM (Simultaneous Localizaton and Mapping). Para operar, o robô deve ser capaz de manter uma estimativa da sua posição com base nos sensores embarcados veículo, adquirir e utilizar conhecimento sobre o mundo ao seu redor, possuir a habilidade de reconhecer obstáculos, e responder em tempo real as situações que possam ocorrer neste ambiente. Este trabalho propõe também a utilização de um sensor de ultra-som com varredura frontal de 180 graus, para detecção de landmarks (marcos) naturais em um ambiente interno para construção do mapa na memória do sistema de controle do robô. As informações do deslocamento do robô são fornecidas pelo sistema de odometria com encoder. Essas informações de deslocamento do robô a distância dos landmarks são combinadas através da aplicação do Filtro de Kalman Estendido (EKF), para o cálculode posição e orientação estimados do robô bem como a posição estimada dos landmarks (mapa). Trata-se de um trabalho com resultados preliminares, que tem como contribuição específica realizar a tarefa de localização e mapeamento simultaneamente (SLAM) usando um sensor de ultra-som rotativo. São apresentados também os resultados de simulação da técnica de localização e mapeamento simultâneo usando o Filtro de Kalman Estendido (EKT) e complementadas com avaliações experimentais em ambiente reais, aplicado a um robô móvel trabalhando como um transportador de materiais automatizado no chão de fábrica. Discussões são apresentadas sobre os sensores usados, a complexidade computacional, a associação de dados e a modelagem e controle do robô móvel / Abstract: The purpose of this paper is to provide a mobile robot with the ability to simultaneously map and locate the environment. This problem is know in classical literature as SLAM (Simultaneous Localization and Mapping). To operate, the robot must be able to maintain an estimation of its position based on sensors attached to the vehicle, acquire and use knowledge about the world around it, have the ability to recognize obstacles and respond in real time situations that may occur in this environment. This paper also proposes the use of an ultrasonic sensor to scan an angle of 180 degrees, for detection of landmarks in a natural environment in order to build the internal map inside the robot's controller memory. The displacement information is provided by the robot odometry system with encoder. This information is combined through the application of Extended Kalmar filter (EKT). This is a preliminary work, which has the specific contribution the task of locating and mapping simultaneously (SLAM) using a rotating ultrasonic sensor. There is also presented the simulation of the technique of simultaneous localization and mapping using the extended Kalman filter (EKT) in addition of experimental evaluations in real environment, applied to a mobile robot working as an automated carried materials on the factory floor. Discussions are presented on the used sensors, the computational complexity, data combination and modeling and control of mobile robot / Mestre

PhD / This thesis is concerned with planning and acting under uncertainty in partially-observable continuous domains. In particular, it focusses on the problem of mobile robot navigation given a known map. The dominant paradigm for robot localisation is to use Bayesian estimation to maintain a probability distribution over possible robot poses. In contrast, control algorithms often base their decisions on the assumption that a single state, such as the mode of this distribution, is correct. In scenarios involving significant uncertainty, this can lead to serious control errors. It is generally agreed that the reliability of navigation in uncertain environments would be greatly improved by the ability to consider the entire distribution when acting, rather than the single most likely state. The framework adopted in this thesis for modelling navigation problems mathematically is the Partially Observable Markov Decision Process (POMDP). An exact solution to a POMDP problem provides the optimal balance between reward-seeking behaviour and information-seeking behaviour, in the presence of sensor and actuation noise. Unfortunately, previous exact and approximate solution methods have had difficulty scaling to real applications. The contribution of this thesis is the formulation of an approach to planning in the space of continuous parameterised approximations to probability distributions. Theoretical and practical results are presented which show that, when compared with similar methods from the literature, this approach is capable of scaling to larger and more realistic problems. In order to apply the solution algorithm to real-world problems, a number of novel improvements are proposed. Specifically, Monte Carlo methods are employed to estimate distributions over future parameterised beliefs, improving planning accuracy without a loss of efficiency. Conditional independence assumptions are exploited to simplify the problem, reducing computational requirements. Scalability is further increased by focussing computation on likely beliefs, using metric indexing structures for efficient function approximation. Local online planning is incorporated to assist global offline planning, allowing the precision of the latter to be decreased without adversely affecting solution quality. Finally, the algorithm is implemented and demonstrated during real-time control of a mobile robot in a challenging navigation task. We argue that this task is substantially more challenging and realistic than previous problems to which POMDP solution methods have been applied. Results show that POMDP planning, which considers the evolution of the entire probability distribution over robot poses, produces significantly more robust behaviour when compared with a heuristic planner which considers only the most likely states and outcomes.

This thesis presents a novel state estimation structure, a hybrid extended Kalman filter/Kalman filter developed for a skid-steered, six-wheeled, ARGO® all-terrain vehicle (ATV). The ARGO ATV is a teleoperated unmanned ground vehicle (UGV) custom fitted with an inertial measurement unit, wheel encoders and a GPS. In order to enable the ARGO for autonomous applications, the proposed hybrid EKF/KF state estimator strategy is combined with the vehicle’s sensor measurements to estimate key parameters for the vehicle. Field experiments in this thesis reveal that the proposed estimation structure is able to estimate the position, velocity, orientation, and longitudinal slip of the ARGO with a reasonable amount of accuracy. In addition, the proposed estimation structure is well-suited for online applications and can incorporate offline virtual GPS data to further improve the accuracy of the position estimates. The proposed estimation structure is also capable of estimating the longitudinal slip for every wheel of the ARGO, and the slip results align well with the motion estimate findings.

Inertial sensors such as Gyroscope and Accelerometer show systematic as well as random errors in the measurement. Furthermore, double integration method shows accumulation of error in position estimation due to inherent accelerometer bias drift. The primary objective of this research was to evaluate ADXL 335 acceleration sensor for better position estimation using acceleration bias drift error model. In addition, measurement data was recorded with four point rotation test for investigation of error characteristics. The fitted model was validated by using nonlinear regression analysis. The secondary objective was to examine the effect of bias drift and scale factor errors by introducing error model in Kalman Filter smoothing algorithm. The study showed that the accelerometer may be used for short distance mobile robot position estimation. This research would also help to establish a generalized test procedure for evaluation of accelerometer in terms of sensitivity, accuracy and data reliability.

Current planetary rover localization techniques are lacking in autonomy and accuracy. An autonomous method of globally localizing a rover is proposed by matching features extractedvfrom a 3D orbital elevation map and rover-based 3D lidar scans. Localization can be further improved by including odometry measurements as well as orientation measurements from an inclinometer and sun sensor. The methodology was tested with real data from a Mars-Moon analogue site on Devon Island, Nunavut. By tying 23 real scans together with simulated odometry over a 10km traverse, the algorithm was able to localize with varying degrees of accuracy. Output uncertainties were large due to large input uncertainties, but these could be reduced in future experimentation by minimizing the use of simulated input data. It was concluded that the architecture could be used to accurately and autonomously localize a rover over long-range traverses.

Current planetary rover localization techniques are lacking in autonomy and accuracy. An autonomous method of globally localizing a rover is proposed by matching features extractedvfrom a 3D orbital elevation map and rover-based 3D lidar scans. Localization can be further improved by including odometry measurements as well as orientation measurements from an inclinometer and sun sensor. The methodology was tested with real data from a Mars-Moon analogue site on Devon Island, Nunavut. By tying 23 real scans together with simulated odometry over a 10km traverse, the algorithm was able to localize with varying degrees of accuracy. Output uncertainties were large due to large input uncertainties, but these could be reduced in future experimentation by minimizing the use of simulated input data. It was concluded that the architecture could be used to accurately and autonomously localize a rover over long-range traverses.

This thesis presents a novel state estimation structure, a hybrid extended Kalman filter/Kalman filter developed for a skid-steered, six-wheeled, ARGO® all-terrain vehicle (ATV). The ARGO ATV is a teleoperated unmanned ground vehicle (UGV) custom fitted with an inertial measurement unit, wheel encoders and a GPS. In order to enable the ARGO for autonomous applications, the proposed hybrid EKF/KF state estimator strategy is combined with the vehicle’s sensor measurements to estimate key parameters for the vehicle. Field experiments in this thesis reveal that the proposed estimation structure is able to estimate the position, velocity, orientation, and longitudinal slip of the ARGO with a reasonable amount of accuracy. In addition, the proposed estimation structure is well-suited for online applications and can incorporate offline virtual GPS data to further improve the accuracy of the position estimates. The proposed estimation structure is also capable of estimating the longitudinal slip for every wheel of the ARGO, and the slip results align well with the motion estimate findings.