Teodiano Freire Bastos

Human-robot interaction based on wearable IMU sensor and laser range finder

Service robots are not only expected to navigate within the environment, as they also will may with people. Human tracking by mobile robots is essential for service robots and human interaction applications. In this work, the goal is to add a more natural robot-human following in front based on the normal human gait model. This approach proposes implementing and evaluating a human-robot interaction strategy, using the integration of a LRF (Laser Range Finder) tracking of human legs with wearable IMU (Inertial Measurement Unit) sensors for capturing the human movement during the gait. The work was carried out in four stages: first, the definition of the model of human-robot interaction and the control proposal were developed. Second, the parameters based on the human gait were estimated. Third, the robot and sensor integration setup are also proposed. Finally, the description of the algorithm for parameters detection is presented. In the experimental study, despite of the continuous oscillation during the walking, the parameters estimation was precise and unbiased, showing also repeatability with human linear velocities changes. The controller was evaluated with an eight-shaped curve, showing the stability of the controller even with sharp changes in the human path during real experiments.

Robotic wheelchair commanded by SSVEP, motor imagery and word generation

This work presents a robotic wheelchair that can be commanded by a Brain Computer Interface (BCI) through Steady-State Visual Evoked Potential (SSVEP), Motor Imagery and Word Generation. When using SSVEP, a statistical test is used to extract the evoked response and a decision tree is used to discriminate the stimulus frequency, allowing volunteers to online operate the BCI, with hit rates varying from 60% to 100%, and guide a robotic wheelchair through an indoor environment. When using motor imagery and word generation, three mental task are used: imagination of left or right hand, and imagination of generation of words starting with the same random letter. Linear Discriminant Analysis is used to recognize the mental tasks, and the feature extraction uses Power Spectral Density. The choice of EEG channel and frequency uses the Kullback-Leibler symmetric divergence and a reclassification model is proposed to stabilize the classifier.

SLAM-based Cross-a-Door Solution Approach for a Robotic Wheelchair

This paper proposes a solution to the cross-a-door problem in unknown environments for a robotic wheelchair commanded through a Human-Machine Interface (HMI). The problem is solved by a dynamic path planning algorithm implementation based on successive frontier points determination. An adaptive trajectory tracking control based on the dynamic model of the robotic wheelchair is implemented on the vehicle to direct the wheelchair motion along the path in a smooth movement. An EKF feature-based SLAM is also implemented on the vehicle which gives an estimate of the wheelchair pose inside the environment. The SLAM allows the map reconstruction of the environment for safe navigation purposes. The whole system steers satisfactorily the wheelchair with smooth movements through common doorways which are narrow considering the size of the vehicle. Implementation results validating the proposal are also shown in this work.

Switching adaptive control of mobile robots

A novel tracking and positioning adaptive control for mobile robots is designed using Lyapunov theory. Then, a switching control that changes the parameter updating law is used to improve the adaptive control in the sense of reducing high accelerations of the robot and avoiding the parameter drifting. Experimental results show a good performance of the adaptive control and its improvement with the switching control.

Comparison of the influence of stimuli color on Steady-State Visual Evoked Potentials

IntroductionThe main idea of a traditional Steady State Visually Evoked Potentials (SSVEP)-BCI is the activation of commands through gaze control. For this purpose, the retina of the eye is excited by a stimulus at a certain frequency. Several studies have shown effects related to different kind of stimuli, frequencies, window lengths, techniques of feature extraction and even classification. So far, none of the previous studies has performed a comparison of performance of stimuli colors through LED technology. This study addresses precisely this important aspect and would be a great contribution to the topic of SSVEP-BCIs. Additionally, the performance of different colors at different frequencies and the visual comfort were evaluated in each case.MethodsLEDs of four different colors (red, green, blue and yellow) flickering at four distinct frequencies (8, 11, 13 and 15 Hz) were used. Twenty subjects were distributed in two groups performing different protocols. Multivariate Synchronization Index (MSI) was the technique adopted as feature extractor.ResultsThe accuracy was gradually enhanced with the increase of the time window. From our observations, the red color provides, in most frequencies, both highest rates of accuracy and Information Transfer Rate (ITR) for detection of SSVEP.ConclusionAlthough the red color has presented higher ITR, this color was turned in the less comfortable one and can even elicit epileptic responses according to the literature. For this reason, the green color is suggested as the best choice according to the proposed rules. In addition, this color has shown to be safe and accurate for an SSVEP-BCI.

Maps managing interface design for a mobile robot navigation governed by a BCI

In this paper, a maps managing interface is proposed. This interface is governed by a Brain Computer Interface (BCI), which also governs a mobile robots movements. If a robot is inside a known environment, the user can load a map from the maps managing interface in order to navigate it. Otherwise, if the robot is in an unknown environment, a Simultaneous Localization and Mapping (SLAM) algorithm is released in order to obtain a probabilistic grid map of that environment. Then, that map is loaded into the map database for future navigations. While slamming, the user has a direct control of the robots movements via the BCI. The complete system is applied to a mobile robot and can be also applied to an autonomous wheelchair, which has the same kinematics. Experimental results are also shown.

SLAM-based robotic wheelchair navigation system designed for confined spaces

In the present work, a robotic wheelchair navigation system which is specially designed for confined spaces is proposed. In confined spaces, the movements of wheelchairs are restricted by the environment more than other unicycle type vehicles. For example, if the wheelchair is too close to a wall, it can not rotate freely because the front or back may collide with the wall. The navigation system is composed by a path planning module and a control module; both use the environment and robot information provided by a SLAM algorithm to attain their objectives. The planning strategy uses the Monte Carlo technique to find a minimum path within the confined environment and takes into account the variance propagation in the predicted path for ensuring the safe driving of the robot. The objective of the navigation system is to drive the robotic wheelchair within the confined environment in order to reach a desired orientation or posture.

Educational robotics as a learning aid for disabled children

Severe disabled children have little chance of environmental and social exploration and discovery, and due this lack of interaction and independency, it may lead to an idea that they are unable to do anything by themselves. This idea is called “learned helplessness” and is very negative for the child cognitive development and social development as well. With this entire situation it is very likely that the self-steam and mood of this child. Trying to help these children on this situation, educational robotics can offer and aid, once it can give them a certain degree of independency in exploration of environment. The system developed in this work allows the child to transmit the commands to a robot. Sensors placed on the childs body can obtain information from head movement or muscle pulses to command the robot to carry the tasks. Also, this system can be used with a variety of robots, being necessary just a previous configuration. It is expected that, with the usage of this system, the disabled children have a better cognitive development and social interaction, balancing in a certain way, the negative effects of their disabilities.

Proposal of Brain-Computer Interface architecture to command a robotic wheelchair

This paper presents a Brain-Computer Interface architecture that is being implemented in a robotic wheelchair. The interface uses electroencephalographic signals and works with three mental tasks, which are the imagination of right or left hand movements and generation of words beginning with the same random letter. This research uses a data set to perform a simulation of real-time classification, which is the pseudo-online technique, in order to have a preliminary view of the performance of the proposed BCI architecture. Linear Discriminant Analysis is used to recognize the mental tasks. The feature extraction uses the Power Spectral Density and the choice of EEG channel and frequency uses the Kullback-Leibler symmetric divergence. A reclassification model is proposed to stabilize the classifier, and the Sammon map is used to visualize the class separation.

Knee Impedance Modulation to Control an Active Orthosis Using Insole Sensors

Robotic devices for rehabilitation and gait assistance have greatly advanced with the objective of improving both the mobility and quality of life of people with motion impairments. To encourage active participation of the user, the use of admittance control strategy is one of the most appropriate approaches, which requires methods for online adjustment of impedance components. Such approach is cited by the literature as a challenge to guaranteeing a suitable dynamic performance. This work proposes a method for online knee impedance modulation, which generates variable gains through the gait cycle according to the users’ anthropometric data and gait sub-phases recognized with footswitch signals. This approach was evaluated in an active knee orthosis with three variable gain patterns to obtain a suitable condition to implement a stance controller: two different gain patterns to support the knee in stance phase, and a third pattern for gait without knee support. The knee angle and torque were measured during the experimental protocol to compare both temporospatial parameters and kinematics data with other studies of gait with knee exoskeletons. The users rated scores related to their satisfaction with both the device and controller through QUEST questionnaires. Experimental results showed that the admittance controller proposed here offered knee support in 50% of the gait cycle, and the walking speed was not significantly different between the three gain patterns (p = 0.067). A positive effect of the controller on users regarding safety during gait was found with a score of 4 in a scale of 5. Therefore, the approach demonstrates good performance to adjust impedance components providing knee support in stance phase.