Uma arquitetura distribuída de hardware e software para controle de um robô móvel autônomo

In this work, we present a hardware-software architecture for controlling the autonomous mobile robot Kapeck. The hardware of the robot is composed of a set of sensors and actuators organized in a CAN bus. Two embedded computers and eigth microcontroller based boards are used in the system. One of the computers hosts the vision system, due to the significant processing needs of this kind of system. The other computer is used to coordinate and access the CAN bus and to accomplish the other activities of the robot. The microcontroller-based boards are used with the sensors and actuators. The robot has this distributed configuration in order to exhibit a good real-time behavior, where the response time and the temporal predictability of the system is important. We adopted the hybrid deliberative-reactive paradigm in the proposed architecture to conciliate the reactive behavior of the sensors-actuators net and the deliberative activities required to accomplish more complex tasks

Mapeamento robótico 2,5-D com representação em grade de ocupação-elevação

This work introduces a new method for environment mapping with three-dimensional information from visual information for robotic accurate navigation. Many approaches of 3D mapping using occupancy grid typically requires high computacional effort to both build and store the map. We introduce an 2.5-D occupancy-elevation grid mapping, which is a discrete mapping approach, where each cell stores the occupancy probability, the height of the terrain at current place in the environment and the variance of this height. This 2.5-dimensional representation allows that a mobile robot to know whether a place in the environment is occupied by an obstacle and the height of this obstacle, thus, it can decide if is possible to traverse the obstacle. Sensorial informations necessary to construct the map is provided by a stereo vision system, which has been modeled with a robust probabilistic approach, considering the noise present in the stereo processing. The resulting maps favors the execution of tasks like decision making in the autonomous navigation, exploration, localization and path planning. Experiments carried out with a real mobile robots demonstrates that this proposed approach yields useful maps for robot autonomous navigation

Mapeamento com Sonar Usando Grade de Ocupação baseado em Modelagem Probabilística

In this work, we propose a probabilistic mapping method with the mapped environment represented through a modified occupancy grid. The main idea of the proposed method is to allow a mobile robot to construct in a systematic and incremental way the geometry of the underlying space, obtaining at the end a complete environment map. As a consequence, the robot can move in the environment in a safe way, based on a confidence value of data obtained from its perceptive system. The map is represented in a coherent way, according to its sensory data, being these noisy or not, that comes from exterior and proprioceptive sensors of the robot. Characteristic noise incorporated in the data from these sensors are treated by probabilistic modeling in such a way that their effects can be visible in the final result of the mapping process. The results of performed experiments indicate the viability of the methodology and its applicability in the area of autonomous mobile robotics, thus being an contribution to the field

Localização de robôs móveis autônomos utilizando fusão sensorial de odometria e visão monocular

The development and refinement of techniques that make simultaneous localization and mapping (SLAM) for an autonomous mobile robot and the building of local 3-D maps from a sequence of images, is widely studied in scientific circles. This work presents a monocular visual SLAM technique based on extended Kalman filter, which uses features found in a sequence of images using the SURF descriptor (Speeded Up Robust Features) and determines which features can be used as marks by a technique based on delayed initialization from 3-D straight lines. For this, only the coordinates of the features found in the image and the intrinsic and extrinsic camera parameters are avaliable. Its possible to determine the position of the marks only on the availability of information of depth. Tests have shown that during the route, the mobile robot detects the presence of characteristics in the images and through a proposed technique for delayed initialization of marks, adds new marks to the state vector of the extended Kalman filter (EKF), after estimating the depth of features. With the estimated position of the marks, it was possible to estimate the updated position of the robot at each step, obtaining good results that demonstrate the effectiveness of monocular visual SLAM system proposed in this paper

Posicionamento e movimentação de um robô humanóide utilizando imagens de uma câmera móvel externa

This work proposes a method to localize a simple humanoid robot, without embedded sensors, using images taken from an extern camera and image processing techniques. Once the robot is localized relative to the camera, supposing we know the position of the camera relative to the world, we can compute the position of the robot relative to the world. To make the camera move in the work space, we will use another mobile robot with wheels, which has a precise locating system, and will place the camera on it. Once the humanoid is localized in the work space, we can take the necessary actions to move it. Simultaneously, we will move the camera robot, so it will take good images of the humanoid. The mainly contributions of this work are: the idea of using another mobile robot to aid the navigation of a humanoid robot without and advanced embedded electronics; chosing of the intrinsic and extrinsic calibration methods appropriated to the task, especially in the real time part; and the collaborative algorithm of simultaneous navigation of the robots

Localização de um robô móvel usando odometria e marcos naturais

Several methods of mobile robot navigation request the mensuration of robot position and orientation in its workspace. In the wheeled mobile robot case, techniques based on odometry allow to determine the robot localization by the integration of incremental displacements of its wheels. However, this technique is subject to errors that accumulate with the distance traveled by the robot, making unfeasible its exclusive use. Other methods are based on the detection of natural or artificial landmarks present in the environment and whose location is known. This technique doesnt generate cumulative errors, but it can request a larger processing time than the methods based on odometry. Thus, many methods make use of both techniques, in such a way that the odometry errors are periodically corrected through mensurations obtained from landmarks. Accordding to this approach, this work proposes a hybrid localization system for wheeled mobile robots in indoor environments based on odometry and natural landmarks. The landmarks are straight lines de.ned by the junctions in environments floor, forming a bi-dimensional grid. The landmark detection from digital images is perfomed through the Hough transform. Heuristics are associated with that transform to allow its application in real time. To reduce the search time of landmarks, we propose to map odometry errors in an area of the captured image that possesses high probability of containing the sought mark

Sistema de navegação para robôs móveis autônomos

The main task and one of the major mobile robotics problems is its navigation process. Conceptualy, this process means drive the robot from an initial position and orientation to a goal position and orientation, along an admissible path respecting the temporal and velocity constraints. This task must be accomplished by some subtasks like robot localization in the workspace, admissible path planning, trajectory generation and motion control. Moreover, autonomous wheeled mobile robots have kinematics constraints, also called nonholonomic constraints, that impose the robot can not move everywhere freely in its workspace, reducing the number of feasible paths between two distinct positions. This work mainly approaches the path planning and trajectory generation problems applied to wheeled mobile robots acting on a robot soccer environment. The major dificulty in this process is to find a smooth function that respects the imposed robot kinematic constraints. This work proposes a path generation strategy based on parametric polynomials of third degree for the 'x' and 'y' axis. The 'theta' orientation is derived from the 'y' and 'x' relations in such a way that the generated path respects the kinematic constraint. To execute the trajectory, this work also shows a simple control strategy acting on the robot linear and angular velocities

Estratégias baseadas em aprendizado para coordenação de uma frota de robôs em tarefas cooperativas

In multi-robot systems, both control architecture and work strategy represent a challenge for researchers. It is important to have a robust architecture that can be easily adapted to requirement changes. It is also important that work strategy allows robots to complete tasks efficiently, considering that robots interact directly in environments with humans. In this context, this work explores two approaches for robot soccer team coordination for cooperative tasks development. Both approaches are based on a combination of imitation learning and reinforcement learning. Thus, in the first approach was developed a control architecture, a fuzzy inference engine for recognizing situations in robot soccer games, a software for narration of robot soccer games based on the inference engine and the implementation of learning by imitation from observation and analysis of others robotic teams. Moreover, state abstraction was efficiently implemented in reinforcement learning applied to the robot soccer standard problem. Finally, reinforcement learning was implemented in a form where actions are explored only in some states (for example, states where an specialist robot system used them) differently to the traditional form, where actions have to be tested in all states. In the second approach reinforcement learning was implemented with function approximation, for which an algorithm called RBF-Sarsa($lambda$) was created. In both approaches batch reinforcement learning algorithms were implemented and imitation learning was used as a seed for reinforcement learning. Moreover, learning from robotic teams controlled by humans was explored. The proposal in this work had revealed efficient in the robot soccer standard problem and, when implemented in other robotics systems, they will allow that these robotics systems can efficiently and effectively develop assigned tasks. These approaches will give high adaptation capabilities to requirements and environment changes.

Modelagem linear e identificação do modelo dinâmico de um robô móvel com acionamento diferencial

This work presents a modelling and identification method for a wheeled mobile robot, including the actuator dynamics. Instead of the classic modelling approach, where the robot position coordinates (x,y) are utilized as state variables (resulting in a non linear model), the proposed discrete model is based on the travelled distance increment Delta_l. Thus, the resulting model is linear and time invariant and it can be identified through classical methods such as Recursive Least Mean Squares. This approach has a problem: Delta_l can not be directly measured. In this paper, this problem is solved using an estimate of Delta_l based on a second order polynomial approximation. Experimental data were colected and the proposed method was used to identify the model of a real robot

Desenvolvimento de solução para SLAM utilizando visão de teto

This work intends to show a new and few explored SLAM approach inside the simultaneous localization and mapping problem (SLAM). The purpose is to put a mobile robot to work in an indoor environment. The robot should map the environment and localize itself in the map. The robot used in the tests has an upward camera and encoders on the wheels. The landmarks in this built map are light splotches on the images of the camera caused by luminaries on the ceil. This work develops a solution based on Extended Kalman Filter to the SLAM problem using a developed observation model. Several developed tests and softwares to accomplish the SLAM experiments are shown in details

Determination of resonant frequencies of triangular and rectangular microstrip antennas, using artificial neural networks

This paper presents models that can be used in the design of microstrip antennas for mobile communications. The antennas can be triangular or rectangular. The presented models are compared with deterministic and empirical models based on artificial neural networks (ANN) presented in the literature. The models are based on Perceptron Multilayer (PML) and Radial Basis Function (RBF) ANN. RBF based models presented the best results. Also, the models can be embedded in CAD systems, in order to design microstrip antennas for mobile communications.

Weight vector estimation of circular arrays of antennas using Artificial Neural Networks

This paper presents a model for the control of the radiation pattern of a circular array of antennas, shaping it to address the radiation beam in the direction of the user, in order to reduce the transmitted power and to attenuate interference. The control of the array is based on Artificial Neural Networks (ANN) of the type RBF (Radial Basis Functions), trained from samples generated by the Wiener equation. The obtained results suggest that the objective was reached.

New contributions to the determination of resonance frequencies of rectangular microstrip antennas by neural networks

This paper uses artificial neural networks (ANN) to compute the resonance frequencies of rectangular microstrip antennas (MSA), used in mobile communications. Perceptron Multi-layers (PML) networks were used, with the Quasi-Newton method proposed by Broyden, Fletcher, Goldfarb and Shanno (BFGS). Due to the nature of the problem, two hundred and fifty networks were trained, and the resonance frequency for each test antenna was calculated by statistical methods. The estimate resonance frequencies for six test antennas were compared with others results obtained by deterministic and ANN based empirical models from the literature, and presented a better agreement with the experimental values.

Fault section estimation in automated distribution substations

In this paper, a methodology based on Unconstrained Binary Programming (UBP) model and Genetic Algorithms (GAs) is proposed for estimating fault sections in automated distribution substations. The UBP model, established by using the parsimonious set covering theory, looks for the match between the relays' protective alarms informed by the SCADA system and their expected states. The GA is developed to minimize the UBP model and estimate the fault sections in a swift and reliable manner. The proposed methodology is tested by utilizing a real-life automated distribution substation. Control parameters of the GA are tuned to achieve maximum computational efficiency and reduction of processing time. Results show the potential and efficiency of the methodology for estimating fault section in real-time at Distribution Control Centers. ©2009 IEEE.

Limitations of trust management schemes in VANET and countermeasures

Vehicular networks ensure that the information received from any vehicle is promptly and correctly propagated to nearby vehicles, to prevent accidents. A crucial point is how to trust the information transmitted, when the neighboring vehicles are rapidly changing and moving in and out of range. Current trust management schemes for vehicular networks establish trust by voting on the decision received by several nodes, which might not be required for practical scenarios. It might just be enough to check the validity of incoming information. Due to the ephemeral nature of vehicular networks, reputation schemes for mobile ad hoc networks (MANETs) cannot be applied to vehicular ad hoc networks (VANET). We point out several limitations of trust management schemes for VANET. In particular, we identify the problem of information cascading and oversampling, which commonly arise in social networks. Oversampling is a situation in which a node observing two or more nodes, takes into consideration both their opinions equally without knowing that they might have influenced each other in decision making. We show that simple voting for decision making, leads to oversampling and gives incorrect results. We propose an algorithm to overcome this problem in VANET. This is the first paper which discusses the concept of cascading effect and oversampling effects to ad hoc networks. © 2011 IEEE.