Abstract-In this paper, a human-machine shared control strategy is proposed for the navigation control of a wheelchair, employing both brain-machine control mode and autonomous control mode. In the brain-machine control mode, contrary to the traditional four-direction control signals, a novel brain-machine interface using steadystate visual evoked potentials is presented, which utilizes two brain signals to produce a polar polynomial trajectory. The produced trajectory is continuous in curvature without violating dynamic constraints of the wheelchair. In the autonomous control mode, the synthesis of angle-based potential field and vision-based simultaneous localization and mapping technique is proposed to guide the robot navigating among the obstacles. Extensive experiments have been conducted to test the developed shared control wheelchair in several scenarios with a number of volunteers, and the results have verified the effectiveness of the proposed shared control scheme.Index Terms-Angle potential field, brain-machine interface (BMI), shared control, vision-based simultaneous localization and mapping (SLAM).
This paper presents a new shared-control approach for brain-actuated intelligent wheelchair by means of a noninvasive Brain-computer Interface (BCI). The problem caused by the sparse and unsteady feature of BCI command, a two-layer shared-control strategy is proposed to steer the intelligent wheelchair. The first one is a machine decision layer which responsible for enabling/disabling the BCI command in a certain context, such as bifurcations and multiple-directions caused by new obstacles in the environment or deadlocks. The second one is a human intention matching layer which is used to generate suitable motion command with consideration of the human user's ability of driving the wheelchair, as well as the situation awareness of potential directions in a known environment. To achieve efficient navigation and position under condition of decoding uncertainly of BCI, the paper provides a navigation system to validate user's commands. And a steady state visual evoked potential (SSVEP) of the BCI as the human machine interface (HMI), the canonical correlation analysis (CCA) is applied to analyze the frequency components of SSVEP in electroencephalogram(EEG). Experiments have been performed by a number of able-bodied volunteers in a structured known environment. The experiment results show that all volunteers are able to successfully operate the wheelchair with a high level of robustness.
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