Homography-based pose estimation to guide a miniature helicopter during 3D-trajectory tracking

This work proposes a pose-based visual servoing control, through using planar homography, to estimate the position and orientation of a miniature helicopter relative to a known pattern. Once having the current flight information, the nonlinear underactuated controller presented in one of our previous works, which attends all flight phases, is used to guide the rotorcraft during a 3Dtrajectory tracking task. In the sequel, the simulation framework and the results obtained using it are presented and discussed, validating the proposed controller when a visual system is used to determine the helicopter pose information.Fil: Brandão, Alexandre . Universidade Federal Do Espirito Santo. Centro Tecnologico. Departamento de Ingenieria Electrica; BrasilFil: Sarapura, Jorge Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; Argentina. Universidad Nacional de San Juan; ArgentinaFil: Sarcinelli Filho, Mario . Universidade Federal Do Espirito Santo. Centro Tecnologico. Departamento de Ingenieria Electrica; BrasilFil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; Argentina. Universidad Nacional de San Juan; Argentin

Human-machine interfaces based on EMG and EEG applied to robotic systems

<p>Abstract</p> <p>Background</p> <p>Two different Human-Machine Interfaces (HMIs) were developed, both based on electro-biological signals. One is based on the EMG signal and the other is based on the EEG signal. Two major features of such interfaces are their relatively simple data acquisition and processing systems, which need just a few hardware and software resources, so that they are, computationally and financially speaking, low cost solutions. Both interfaces were applied to robotic systems, and their performances are analyzed here. The EMG-based HMI was tested in a mobile robot, while the EEG-based HMI was tested in a mobile robot and a robotic manipulator as well.</p> <p>Results</p> <p>Experiments using the EMG-based HMI were carried out by eight individuals, who were asked to accomplish ten eye blinks with each eye, in order to test the eye blink detection algorithm. An average rightness rate of about 95% reached by individuals with the ability to blink both eyes allowed to conclude that the system could be used to command devices. Experiments with EEG consisted of inviting 25 people (some of them had suffered cases of meningitis and epilepsy) to test the system. All of them managed to deal with the HMI in only one training session. Most of them learnt how to use such HMI in less than 15 minutes. The minimum and maximum training times observed were 3 and 50 minutes, respectively.</p> <p>Conclusion</p> <p>Such works are the initial parts of a system to help people with neuromotor diseases, including those with severe dysfunctions. The next steps are to convert a commercial wheelchair in an autonomous mobile vehicle; to implement the HMI onboard the autonomous wheelchair thus obtained to assist people with motor diseases, and to explore the potentiality of EEG signals, making the EEG-based HMI more robust and faster, aiming at using it to help individuals with severe motor dysfunctions.</p

A Path-Following Controller for Guiding a Single Robot or a Multi-robot System

A solution to the problem of path-following by a team of terrestrial mobile robots is proposed in this paper. The proposal, in this case, is a formation controller dealing with three robots navigating in a coordinate way (as a formation). Based on the controller proposed to guide the formation to follow a prescribed path, an extension to the case of path-following with a single terrestrial mobile robot is also proposed. When regarding a formation of mobile robots, the proposed solution consists in applying a path-following controller to the center of mass of the formation, dealt with as a single virtual robot, and trajectory-tracking controllers to the individual robots in the formation. The advantage of such approach is that it allows planning the motion of the desired formation without specifying how each robot should move. The movement is specified for the formation as a whole, using a representation called cluster-space, and the movement of the individual robots is derived from the specification of the formation movement directly, using transformations from the cluster-space to the space of the robots and vice versa. In the sequel, the path-following controller designed for the virtual robot is analyzed in detail, now dealing with the possibility of being also used as a path-following controller applied to a single real robot. Theoretical stability analysis is presented, as well as some experimental results, whose analysis allows claiming that the proposed controller is suitable to guide either a single robot or a multi-robot formation when following a path.Fil: Resende, Cassius Zanetti. Science And Technology of Espírito Santo; BrasilFil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Sarcinelli Filho, Mario. Universidade Federal do Espírito Santo; Brasi

OPTICAL FLOW ESTIMATION USING DATA FUSION

Abstract: In this work an approach is presented for the calculation of the optic flow using a data fusion method. The method searches for the value of the optical flow vector with the smallest variance. This is not a new algorithm for optical flow calculation. Rather, a well known method is used for estimating such vectors, in terms of a statistical model (mean and variance values). Then, such statistical model is used in connection to a data fusion engine, so that a better estimate is then generated for such vectors. The data fusion engine used is a Kalman filter, which guarantees the optimal data fusion. Thus, the variance of the obtained optic flow estimate will be smaller than the smallest variance entering the data fusion engine. This way, a very good estimate of the optic flow is obtained