Controle de Manipulador Redundante Utilizando Realimentação Visual

In conventional robot manipulator control, the desired path is specified in cartesian space and converted to joint space through inverse kinematics mapping. The joint references generated by this mapping are utilized for dynamic control in joint space. Thus, the end-effector position is, in fact, controlled indirectly, in open-loop, and the accuracy of grip position control directly depends on the accuracy of the available kinematic model. In this report, a new scheme for redundant manipulator kinematic control, based on visual servoing is proposed. In the proposed system, a robot image acquired through a CCD camera is processed in order to compute the position and orientation of each link of the robot arm. The robot task is specified as a temporal sequence of reference images of the robot arm. Thus, both the measured pose and the reference pose are specified in the same image space, and its difference is utilized to generate a cartesian space error for kinematic control purposes. The proposed control scheme was applied in a four degree-of-freedom planar redundant robot arm, experimental results are shown

Navegação cooperativa de um robô humanóide e um robô com rodas usando informação visual

This work presents a cooperative navigation systemof a humanoid robot and a wheeled robot using visual information, aiming to navigate the non-instrumented humanoid robot using information obtained from the instrumented wheeled robot. Despite the humanoid not having sensors to its navigation, it can be remotely controlled by infra-red signals. Thus, the wheeled robot can control the humanoid positioning itself behind him and, through visual information, find it and navigate it. The location of the wheeled robot is obtained merging information from odometers and from landmarks detection, using the Extended Kalman Filter. The marks are visually detected, and their features are extracted by image processing. Parameters obtained by image processing are directly used in the Extended Kalman Filter. Thus, while the wheeled robot locates and navigates the humanoid, it also simultaneously calculates its own location and maps the environment (SLAM). The navigation is done through heuristic algorithms based on errors between the actual and desired pose for each robot. The main contribution of this work was the implementation of a cooperative navigation system for two robots based on visual information, which can be extended to other robotic applications, as the ability to control robots without interfering on its hardware, or attaching communication devices

Desenvolvimento de esquema de controle com realimentação visual para um robô manipulador

This work proposes a kinematic control scheme, using visual feedback for a robot arm with five degrees of freedom. Using computational vision techniques, a method was developed to determine the cartesian 3d position and orientation of the robot arm (pose) using a robot image obtained through a camera. A colored triangular label is disposed on the robot manipulator tool and efficient heuristic rules are used to obtain the vertexes of that label in the image. The tool pose is obtained from those vertexes through numerical methods. A color calibration scheme based in the K-means algorithm was implemented to guarantee the robustness of the vision system in the presence of light variations. The extrinsic camera parameters are computed from the image of four coplanar points whose cartesian 3d coordinates, related to a fixed frame, are known. Two distinct poses of the tool, initial and final, obtained from image, are interpolated to generate a desired trajectory in cartesian space. The error signal in the proposed control scheme consists in the difference between the desired tool pose and the actual tool pose. Gains are applied at the error signal and the signal resulting is mapped in joint incrementals using the pseudoinverse of the manipulator jacobian matrix. These incrementals are applied to the manipulator joints moving the tool to the desired pose

Sistema de supervisão aérea baseado em navegação visual para detecção de anomalias em instalações de petróleo e gás

This work deals with the development of a prototype of a helicopter quadrotor for monitoring applications in oil facilities. Anomaly detection problems can be resolved through monitoringmissions performed by a suitably instrumented quadrotor, i.e. infrared thermosensors should be embedded. The proposed monitoring system aims to reduce accidents as well as to make possible the use of non-destructive techniques for detection and location of leaks caused by corrosion. To this end, the implementation of a prototype, its stabilization and a navigation strategy have been proposed. The control strategy is based on dividing the problem into two control hierarchical levels: the lower level stabilizes the angles and the altitude of the vehicle at the desired values, while the higher one provide appropriate references signals to the lower level in order the quadrotor performs the desired movements. The navigation strategy for helicopter quadrotor is made using information provided by a acquisition image system (monocular camera) embedded onto the helicopter. Considering that the low-level control has been solved, the proposed vision-based navigation technique treats the problem as high level control strategies, such as, relative position control, trajectory generation and trajectory tracking. For the position control we use a control technique for visual servoing based on image features. The trajectory generation is done in a offline step, which is a visual trajectory composed of a sequence of images. For the trajectory tracking problem is proposed a control strategy by continuous servovision, thus enabling a navigation strategy without metric maps. Simulation and experimental results are presented to validate the proposal

Técnicas visuais de localização e mapeamento simultâneos sem extração de primitivas geométricas da imagem

In Simultaneous Localization and Mapping (SLAM - Simultaneous Localization and Mapping), a robot placed in an unknown location in any environment must be able to create a perspective of this environment (a map) and is situated in the same simultaneously, using only information captured by the robot s sensors and control signals known. Recently, driven by the advance of computing power, work in this area have proposed to use video camera as a sensor and it came so Visual SLAM. This has several approaches and the vast majority of them work basically extracting features of the environment, calculating the necessary correspondence and through these estimate the required parameters. This work presented a monocular visual SLAM system that uses direct image registration to calculate the image reprojection error and optimization methods that minimize this error and thus obtain the parameters for the robot pose and map of the environment directly from the pixels of the images. Thus the steps of extracting and matching features are not needed, enabling our system works well in environments where traditional approaches have difficulty. Moreover, when addressing the problem of SLAM as proposed in this work we avoid a very common problem in traditional approaches, known as error propagation. Worrying about the high computational cost of this approach have been tested several types of optimization methods in order to find a good balance between good estimates and processing time. The results presented in this work show the success of this system in different environments