In this article, we develop a robust sliding-mode nonlinear predictive controller for brain-controlled robots with enhanced performance, safety, and robustness. First, the kinematics and dynamics of a mobile robot are...
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In this article, we develop a robust sliding-mode nonlinear predictive controller for brain-controlled robots with enhanced performance, safety, and robustness. First, the kinematics and dynamics of a mobile robot are built. After that, the proposed controller is developed by cascading a predictive controller and a smooth sliding-mode controller. The predictive controller integrates the human intention tracking with safety guarantee objectives into an optimization problem to minimize the invasion to human intention while maintaining robot safety. The smooth sliding-mode controller is designed to achieve robust desired velocity tracking. The results of human-in-the-loop simulation and robotic experiments both show the efficacy and robust performance of the proposed controller. This work provides an enabling design to enhance the future research and development of brain-controlled robots.
Functional near-infrared spectroscopy (fNIRS) is an emerging non-invasive technology applied in brain-computer interface (BCI). This study investigates fNIRS based imagined hand-clenching tasks, indicating that the co...
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Functional near-infrared spectroscopy (fNIRS) is an emerging non-invasive technology applied in brain-computer interface (BCI). This study investigates fNIRS based imagined hand-clenching tasks, indicating that the combinations of speed and force have distinct patterns which can be decoded to develop a BCI system. Twelve healthy participants are instructed to perform imagined left or right hand-clenching tasks;oxy-hemoglobin (HbO2) and deoxy-hemoglobin (Hb) concentrations are acquired from motor cortex using a multi-channel fNIRS system. Feature selection method based on mutual information is employed to select the optimal features for classification, and support vector machine (SVM) is used as a classifier resulting in average accuracies of 84.9% and 86.1% for classifying left and right imagined movements. Compared with traditional fNIRS-BCI system, this study provides a possibility to generate a new control pattern for brain-controlled robots, e.g., speed or force control. There is a potential application to combine fNIRS-BCI system with exoskeleton for rehabilitation.
Human-in-the-loop robotic system is an emerging technique in recent years. Human intelligence as well as machine intelligence are incorporated to accomplish tasks efficiently and effectively. However, grasp-and-lift (...
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Human-in-the-loop robotic system is an emerging technique in recent years. Human intelligence as well as machine intelligence are incorporated to accomplish tasks efficiently and effectively. However, grasp-and-lift (GAL) tasks through human-robot interactions are still a problem in an unstructured environment like urban search and rescue. Human assistive GAL tasks enable robots to complete search or rescue procedures quickly and accurately. brain-machine interface (BMI) controlledrobots have demonstrated promising applications in human-robot collaborative manipulations. In this study, an architecture of human-robot team is proposed for performing GAL tasks in BMI-based human-robot systems. The proposed architecture contains several workflows from both human and robot aspects to improve performance. In addition, human brain activities are generally considered as non-stationary signals with varying spatial and temporal distributions. To enhance robustness and stability of brain-controlled robot's GAL tasks, a new method via adaptive boosting mechanism is proposed. The proposed multiple subjects' adaptive boosting is able to suppress noisy data and outliers in multiple subjects' electroencephalogram signals, and therefore enhance accuracy and robustness of intention and sensation signal classification in GAL tasks. Preliminary results show that the new architecture is feasible with ethical establishment and the proposed method can outperform traditional methods.
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