# A Semi-Automatic Wheelchair with Navigation Based on Virtual-Real 2D Grid Maps and EEG Signals
## 1 Introduction
Mapping algorithms were gradually developed as Simultaneous Localization and Mapping (SLAM) algorithms and were applied to draw 3D maps [12], in which the computational problem of constructing or updating a map of an unknown environment is represented, with simultaneously keeping track of an agent's location within it. An image processing method was employed to identify fixed artificial landmarks built in moving space [13]. Thus, these fixed artificial landmarks were applied for determining the current location of a wheelchair on a map built in advance during its movement. [PERSON] et al. proposed a powerful and fast method of positioning a wheelchair based on computer vision, in which image features were extracted and then combined with map components to provide a current position of self-propelled robots [14,15]. In fact, mapping for mobile robots in the environment is a major challenge due to the data obtained from the environment and the algorithm applied on them [16, 17, 18]. Landmark information for mobile robots plays an important role, in which types of landmarks [19, 20, 21, 22] such as doors, stairs, walls, ceilings, and floors were selected and features were extracted for identification. Therefore, to detect the landmarks with their features, one could be based on color, texture, brightness, and obstacle size. In recent years, the Reinforcement Learning (RL) method has achieved great success in many tasks including games [23] and simulation control agents [24]. Applications of the RL method in robot manufacturing are mostly limited in operation methods [25], in which the workspace could be fully observed and very stable. With mobile robots, complex environments can expand the sample space, while the RL method often takes action samples from a separate space for simpler processing of problems [26]. In [27], the RL method was applied for autonomous navigation based on the input of image information and has achieved significant success. The authors of this research analyzed agent behavior in static mazes with complex geometries, starting positions, and random orientations, while target positions could vary. The results showed that this RL method could allow the agent to navigate in large and intuitive environments, in which there were starting and target positions which were changed frequently, but the maze layout was always static. In [28], [PERSON] et al. tried to find the sequence of actions with minimum distance to move an agent from its current position to the target specified by the RGB image. This means that they have to collect a large number of different images to process before training the navigation model. Finding a path on a static grid map is a well-known issue and well researched in AI communities, in which planners and robots with lots of methods and algorithms have been proposed to date [29; 30]. Most of these algorithms are based on heuristic searching in the state space created by grid cells. In general, one prominent issue is that Neural Networks (NNs) work well with all types of tasks with data collected from sensors or images and then they are used as an input of the NN. The cells contain only two types of cells, including movable and non-movable, and they look like a perfect input for Modern Artificial Neural Network (MANN) architectures, such as a Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) [31; 32; 33; 34]. In [23], the DQN algorithm was applied due to solving many challenges related to autonomous control [35]. In particular, this algorithm combines the Q-Learning algorithm with neural networks, in which DQNs solve problems with high-dimensional observation spaces using neural networks to estimate Q-values for corresponding actions. Mostly, deep RL training including DQNs and their variants is performed in a virtual environment, because the process of training using a trial and error method can lead to damage to real robots in typical tasks.