Suat Karakaya

Design and Implementation of a Multi Sensor Based Brain Computer Interface for a Robotic Wheelchair

In this study, design and implementation of a multi sensor based brain computer interface for disabled and/or elderly people is proposed. Developed system consists of a wheelchair, a high-power motor controller card, a Kinect camera, electromyogram (EMG) and electroencephalogram (EEG) sensors and a computer. The Kinect sensor is installed on the system to provide safe navigation for the system. Depth frames, captured by the Kinect’s infra-red (IR) camera, are processed with a custom image processing algorithm in order to detect obstacles around the wheelchair. A Consumer grade EMG device (Thalmic Labs) was used to obtain eight channels of EMG data. Four different hand movements: Fist, release, waving hand left and right are used for EMG based control of the robotic wheelchair. EMG data is first classified using artificial neural network (ANN), support vector machines and random forest schemes. The class is then decided by a rule-based scheme constructed on the individual outputs of the three classifiers. EEG based control is adopted as an alternative controller for the developed robotic wheelchair. A wireless 14-channels EEG sensor (Emotiv Epoch) is used to acquire real time EEG data. Three different cognitive tasks: Relaxing, math problem solving, text reading are defined for the EEG based control of the system. Subjects were asked to accomplish the relative cognitive task in order to control the wheelchair. During experiments, all subjects were able to control the robotic wheelchair by hand movements and track a pre-determined route with a reasonable accuracy. The results for the EEG based control of the robotic wheelchair are promising though vary depending on user experience.

Image processing based indoor localization system

In this study, image processing based low cost indoor localization system was developed. Image processing algorithm was developed in C programming language and Open CV image processing library. Frames were captured by a USB camera which was designed for operating at 850 nm wave length to eliminate environmental disturbances. A narrow band pass filter was integrated to camera in order to detect retro reflective labels only. Retro reflective labels were placed ceiling of indoor area with pre-determined equal spaced grids. Approximate location of mobile robot was obtained by label identity and exact location of mobile robot was obtained with detected labels position at image coordinate system. Developed system was tested on a mobile robot platform and it was observed that system is operating successfully in real time.

Development of a human tracking indoor mobile robot platform

In this paper, a differential drive mobile robot platform was developed in order to perform indoor mobile robot researches. The mobile robot was localized and remote controlled. The remote control consists of a pair of 2.4 GHz transceivers. Localization system was developed by using infra-red reflectors, infra-red leds and camera system. Real time localization system was run on an industrial computer placed on the mobile robot. The localization data of the mobile robot is transmitted by a UDP communication program. The transmitted localization information can be received any computer or any other UDP device. In addition, a LIDAR (Light Detection and Ranging; or Laser Imaging Detection and Ranging) and a Kinect three-dimensional depth sensor were adapted on the mobile robot platform. LIDAR was used for obstacle and heading direction detection operations and Kinect for eliminating depth data of close environment. In this study, a mobile robot platform which has specialties as mentioned was developed and a human tracking application was realized real time in MATLAB and C# environment.

Obstacle and optimal heading direction detection algorithm on a mobile robot platform

In this study, Sick-LMS100 Lidar was used for detecting the obstacles around a mobile robot platform and finding the best heading direction. The computer and the LIDAR were communicated via Ethernet TCP/IP in order to gather position information of the objects around. The algorithm, which was developed in Visual Basic 6.0 environment, chose the optimal heading direction relative to the positions of the obstacles. The gathered path information was then sent to a DSP for motor control via serial port. A mobile robot platform was developed during the study and the optimum heading direction finding algorithm was tested on this mobile robot platform in real time. The results which were gathered in several conditions were compared.

Real time control of a wheelchair based on EMG and Kinect for the disabled people

In this study, EMG based control of a wheelchair was explored. A high power motor control card was designed and installed on wheelchair. Real time EMG data was processed in MATLAB. Kinect sensor was mounted on wheelchair to provide safe navigation to the system. Depth frames which were captured from Kinect, were processed by the developed image processing algorithm in order to avoid possible collisions. It was observed that, one can easily control the developed system.

Kinematic model based path tracking algorithm for differential drive mobile robots

In this study, it was studied on a path tracking method which is based on fuzzy logic, PI and P control for 4-wheeled differential drive autonomous mobile robots. Major problem is to force the mobile robot which is assumed to be located on a static map, to track a path that was planned by planning algorithms on the same map. Therefore, a mobile robot simulator was developed regarding a real mobile robots mechanical and physical specs. The developed method was tested on this simulator by using the control algorithms. Performance criterions were given as the length of the route taken by the robot and tracking duration.

A New Mobile Robot Toolbox for Matlab

In this study, a wheeled mobile robot navigation toolbox for Matlab is presented. The toolbox includes algorithms for 3D map design, static and dynamic path planning, point stabilization, localization, gap detection and collision avoidance. One can use the toolbox as a test platform for developing custom mobile robot navigation algorithms. The toolbox allows users to insert/remove obstacles to/from the robot’s workspace, upload/save a customized map and configure simulation parameters such as robot size, virtual sensor position, Kalman filter parameters for localization, speed controller and collision avoidance settings. It is possible to simulate data from a virtual laser imaging detection and ranging (LIDAR) sensor providing a map of the mobile robot’s immediate surroundings. Differential drive forward kinematic equations and extended Kalman filter (EKF) based localization scheme is used to determine where the robot will be located at each simulation step. The LIDAR data and the navigation process are visualized on the developed virtual reality interface. During the navigation of the robot, gap detection, dynamic path planning, collision avoidance and point stabilization procedures are implemented. Simulation results prove the efficacy of the algorithms implemented in the toolbox.

A dynamic path planning method for wheeled mobile robots (Dyna-bug).

In this study, a hybrid path-planning scheme is presented. The main contribution of this paper is merging the static grid costs of the global map and the immediate environmental structure of the local map. The stationary condition of the map and the instant local goal is weighted by certain coefficients in order to determine the next move of the wheeled mobile robot (WMR). Thus, the cost function is defined in terms of the grid costs and the dynamic parameters. The main assumption is that the WMR on which this scheme is executed must be equipped with a field scanning sensor. The sensor readings in each processing cycle are pre-processed before plugging in the cost function. The passages in the local map are extracted from the sensor data, then the optimal collision-free point lying on the passages is obtained via the cost function.

Virtual-reality-based remote-controlled mobile robot platform

In this paper, a mobile robot system, which consists of a moving base and a built-in weapon platform, was developed. The base is controlled manually using a wireless joystick in which the remote hand weapon manipulates the built-in weapon platform. A stereo vision camera is mounted on the front plane of the built-in weapon platform. Real-time video of the battle zone is recorded by the stereo camera module and is simultaneously monitored on virtual reality glasses. The glasses are worn by the person who will control the built-in weapon. The remote hand weapon is also held by the same person, and the real-time motion directories of the hand weapon are transmitted to the main platform via user datagram protocol. The builtin weapon is fired when the remote user triggers the hand weapon. The weapon platform is locked to the target, regardless of the moving base of the mobile robot.

Classification of motor imaginary in EEG using random

Although the motor-imagery-based brain computer interface (BCI) has become popular in recent years, its practical application is limited due to the classification accuracy of methods. In this study, a new classification scheme is proposed for the classification of multi-class motor imaginary in EEG using random forest (RF) classifier. In the proposed scheme, a fourstage binary classification tree is constructed. An RF model is trained for each stage of decision tree using features extracted from the EEG channels. The EEG band powers of each channel are the extracted features from the EEG signal. The proposed classification scheme is applied on the BCI competition IV dataset 2a recordings. The EEG data is acquired from nine subjects and the proposed scheme is performed for each subject independently. The kappa values of the proposed scheme are calculated to compare the results with the methods in the literature. It is demonstrated that the proposed classification scheme has higher kappa values than the methods in the literature.