Self-adaptive shared control with brain state evaluation network for human-wheelchair cooperation

Abstract

Objective. For the shared control systems, how to trade off the control weight between robot autonomy and human operator is an important issue, especially for BCI-based systems. However, most of existing shared controllers have paid less attention to the effects caused by subjects with different levels of brain control ability. Approach. In this paper, a brain state evaluation network, termed BSE-NET, is proposed to evaluate subjects’ brain control ability online based on quantized attention-gated kernel reinforcement learning. With the output of BSE-NET (confidence score), a shared controller is designed to dynamically adjust the control weight between robot autonomy and human operator. Main results. The experimental results show that most of subjects achieved high and stable experimental success rate of approximately 90%. Furthermore, for subjects with different accuracy on EEG decoding, a proper confidence score can be dynamically generated to reflect their levels of brain control ability, and the proposed system can effectively adjust the control weight in all-time shared control. Significance. We discuss how our proposed method shows promise for BCI applications that can evaluate subjects’ brain control ability online as well as provide a method for the research on self-adaptive shared control to adaptively balance control weight between subject’s instruction and robot autonomy.

Continuous shared control of a mobile robot with brain–computer interface and autonomous navigation for daily assistance

“…It is still a challenge to drive robotic devices directly through BCI because this puts a large mental burden on users. To improve the performance of brain-actuated systems, the shared control strategy has been widely discussed and investigated [3] , [19] , [20] , [21] . Generally, a shared control architecture is conceived to integrate user commands (generated by cognitive intention) and robotic autonomy [17] .…”

“…Shared control is a control strategy that combines human control intention and robotic autonomous intelligence and can adapt to environmental changes, allowing users to participate in the interaction more effectively and with less workload [18] . Shared control arbitrators have been designed to use fixed weights [3] , distance-based weights [19] , and other methods [20] , [21] to fuse different control signals. These studies have demonstrated that the adoption of shared control could take advantage of human intention and robotic autonomous intelligence to improve the performance of BCI systems in a complex environment.…”

Design of an Assisted Driving System for Obstacle Avoidance Based on Reinforcement Learning Applied to Electrified Wheelchairs

“…From the admissible velocities within this dynamic window, the combination of translational and rotational velocities is selected by maximizing an objective function. While there have been a few attempts to apply this approach to wheelchairs [51][52][53][54][55][56][57][58], notable drawbacks include increased computational costs and latency, particularly as the environment becomes more complex. Another widely employed approach for mobile robots is the potential fields method (PFM), with a prominent implementation known as the virtual force field (VFF).…”