A Semi-Automatic Wheelchair with Navigation Based on Virtual-Real 2D Grid Maps and EEG Signals

Abstract

A semi-automatic wheelchair allows disabled people to possibly control in an indoor environment with obstacles and targets. The paper proposes an EEG-based control system for the wheelchair based on a grid map designed to allow disabled people to reach any preset destination. In particular, the grid map is constructed by dividing it into grid cells that may contain free spaces or obstacles. The map with the grid cells is simulated to find the optimal paths to the target positions using a Deep Q-Networks (DQNs) model with the Parametric Rectified Linear Unit (PReLU) activation function, in which a novel algorithm for finding the optimal path planning by converting wheelchair actions is applied using the output parameters of the DQNs. For the wheelchair movement in one real indoor environment corresponding to the virtual 2D grid map, the initial position of the wheelchair will be determined based on natural landmarks and a graphical user interface designed for on-screen display can support disabled people in selecting the desired destination from a list of predefined locations using Electroencephalogram (EEG) signals by blinking eyes. Therefore, one user can easily and safely control the wheelchair using an EEG system to reach the desired target when the wheelchair position and destination are determined in the indoor environment. As a result, a grid map was developed and experiments for the semi-automatic wheelchair control were performed in real indoor environments to illustrate the effectiveness of the proposed method. In addition, the system is a platform to develop different types of controls depending on the types of user disabilities and different environmental maps built.

Positioning an electric wheelchair in 2D grid map based on natural landmarks for navigation using Q-learning

“…Only the wheelchair position is calculated in Q-learning for determining the movement route and producing specific actions for the wheelchair's state. The wheelchair's direction d relative to the 2D grid map origin will feed into a block for converting the predicted activities from the Q-learning a (Up, Down, Left, Right) into actual actions for the wheelchair's movement aw (forward, backward, leftforward, right-forward) because the electric wheelchair is not an omnidirectional control model [27]. Specifically, for a starting point and a destination point on a 2D grid map, the Q-learning method will produce a series of actions.…”

Electric Wheelchair Hybrid Operating System Coordinated with Working Range of a Robotic Arm

“…Recently, such technology was also utilized in electric wheelchairs. 2D/3D mapping-based system is one of them [31][32][33][34]. In such a system, a 2D/3D map is built from the information acquired by wheelchair-mounted 2D/3D LiDAR [35].…”