Architecture design for performing grasp-and-lift tasks in brain–machine-interface-based human-in-the-loop robotic system

• Main Author: Yuchou Chang
• Format: Article
• Language: English
• Published: Wiley 2019-04-01
• Series: IET Cyber-Physical Systems
• Subjects: electroencephalography; medical signal processing; neurophysiology; learning (artificial intelligence); medical robotics; brain-computer interfaces; human-robot interaction; mobile robots; hardware-in-the loop simulation; end effectors; grippers; human-in-the-loop robotic system; human intelligence; machine intelligence; grasp-and-lift tasks; human–robot interactions; human assistive GAL tasks; brain–machine interface controlled robots; human–robot collaborative manipulations; BMI-based human–robot systems; human brain activities; brain-controlled robot; brain–machine-interface-based human-in-the-loop robotic system; nonstationary signals
• Online Access: https://digital-library.theiet.org/content/journals/10.1049/iet-cps.2018.5066

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) controlled robots 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.