Romeda Blog

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Development of a Human Tracking Indoor Mobile Robot Platform

Gürkan Küçükyıldız, Suat Karakaya

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 infrared reflectors, infrared 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 threedimensional 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.

Romeda Blog - 04/25/2017

Obstacle and Optimal Heading Direction Detection Algorithm On a Mobile Robot Platform

Gürkan Küçükyıldız, Suat Karakaya

In this study, SickLMS100 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.

Romeda Blog - 04/25/2017

Kinematic Model Based Path Tracking Algorithm for Differential Drive Mobile Robots

Gürkan Küçükyıldız, Suat Karakaya

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 robot's 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

Romeda Blog - 04/25/2017

A Hybrid Indoor Localization System Based on Infra-red Imaging and Odometry

Gürkan Küçükyıldız, Suat Karakaya

In this study, a real-time indoor localization system was developed by using a camera and passive landmarks. A narrow band-pass infra-red (IR) filter was inserted to the back of the camera lens for capturing IR images. The passive landmarks were placed on the ceiling at pre-determined locations and consist of IR retro-reflective tags that have binary coded unique ID’s. An IR projector emits IR rays at the tags on the ceiling. The tags then reflect the rays back to the camera sensor creating a digital image. An image processing algorithm was developed to detect and decode the landmarks in captured images. The proposed algorithm successfully estimates the position and the orientation angle based on relative position and orientation with respect to the detected tags. To further improve the accuracy of the estimates, extended Kalman filter (EKF) was adapted to the measurement algorithm. The proposed method initially estimates the position of a mobile robot based on odometry and kinematic model. EKF was then used to update the estimates given the measurement obtained from the image processing system. Real time experiments were performed to test the performance of the system. The results prove that the proposed indoor localization system can effectively estimate position with an error less than 5cm.

Romeda Blog - 04/25/2017

A Bug-Based Local Path Planning Method for Static and Dynamic Environments

Gürkan Küçükyıldız, Suat Karakaya

In this study, a local path planning method was proposed for both static and dynamic environments. In obstacle-free cases, the mobile robot was forced to basic motion-togoal movement. In case where direct movement towards the global target is not possible, the algorithm searches for possible gaps which satisfy certain clearance criteria. The gaps were detected by taking the gradient of one dimensional distance vector acquired from the SICK LMS100 Light Detection and Ranging (LIDAR) sensor. The detected gaps were filtered by various methods which finally led to the optimal gap. Points on the line passing through the optimal gap were evaluated through a cost function and the point having the minimum cost was assumed to be the current local target. The points which were close to the two opposite corners of the gap less than a certain threshold were discarded to avoid collision. The threshold was determined based on the robot size and the kinematic model. Proportional and integral (PI) speed controller for left and right steered wheels was adapted to the proposed method. A graphical user interface (GUI) was developed to visualize the outputs of the method. On the GUI, offline LMS100 vectors and location data were visualized considering differential drive kinematic constraints for the mobile robot. The algorithm was developed at MATLAB environment.

Romeda Blog - 04/25/2017

A Hybrid Posture Stabilization Method for Mobile Robots

Gürkan Küçükyıldız, Suat Karakaya

In this study, a point stabilization scheme which takes into account static obstacles around wheeled mobile robots is proposed. Novelty of the algorithm lies under the consideration of the static obstacles and corporation with the static path planning method exact Euclidian distance transform (EEDT). For a given start point, goal point and static obstacle configuration, the EEDT algorithm determines the shortest path. This path is in an open-polygonal form due to the robot’s grid-based workspace. Tangent values of each vertex of the open-polygon are given to conventional model prediction control (MPC) based posture stabilization scheme as sub-start and sub-goal points. These sub points are given to MPC in a shiftedhorizon strategy to determine the stabilized trajectories between the vertex coordinates. Overall stabilized static trajectory is determined by combining the sub-trajectories independently calculated by MPC based posture stabilization algorithm. The experimental results which are performed in a 3D virtual reality interface, confirms that the developed scheme satisfies the posture stabilization criterion successfully in presence of static obstacles.

Romeda Blog - 04/25/2017

Detection of Obstacle-Free Gaps for Mobile Robot Applications Using 2-D LIDAR Data

Suat Karakaya

Mobile robotics is one of the most studied scientific and technological fields, which is still in progress. Several research interests such as path planning, point stabilization, localization, obstacle avoidance and passable gap detection are commonly studied fields. Gap detection task affects the path planning characteristics of a mobile robot. Especially under presence of limited information about robot’s environment, passable gap detection is necessary for steering the mobile robot towards a goal autonomously. This paper concentrates on passable gap detection for unconstructed environments, which contain only positive obstacles. The method considers specific obstacle configurations such as presence of wall-type obstacle, maze type environments and random placed small sized obstacles. The method proposed in this study is based on reading distance of the obstacles in a certain range and detecting the borders of passable gaps. The detected gaps are re-organized depending on the priority assigned by the robot’s passage order of the gaps. The proposed scheme not only utilizes simple derivation of the measurement data but also extracts hidden gaps in the environment. The proposed scheme assumes the mobile robot is equipped with laser range sensor (LIDAR). A real LIDAR is modelled and adapted to the developed algorithm. The algorithm was developed in Matlab.

Romeda Blog - 04/25/2017

Speed Control Adaptation On Static Trajectories

Suat Karakaya

In this study, a speed vector is defined for static trajectories for mobile robots. In many conventional path-planning methods, the major criterion is to plan the trajectory through obstacle-free regions to satisfy safety. The obstacle configuration is not the main concern for controlling the speed of the mobile robot. This issue is handled as a sub-procedure under path tracking scheme rather than a standalone operation. Thus, it is performed that planning a speed vector within the planned static trajectory. Directory lines are fitted on consecutive path coordinates to check whether an obstacle is available on the motion direction of the mobile robot or not. This procedure is operated continuously to control the instant average speed of the mobile robot.

Romeda Blog - 04/25/2017

Brain Computer Interface with Low Cost Commercial EEG Device

Gürkan Küçükyıldız, Suat Karakaya

In this study, a brain computer interface (BCI) system was explored. Instead of high cost EEG devices, a low cost commercial EEG device (EMOTIV) was used. Raw EEG data was obtained by using research edition SDK of EMOTIV. EMOTIV EEG device has 14 channels (10-20 placement) for EEG and two channels (x and y axis gyro: GYROX, GYROY) for head movements. Head movements and eye-blink can affect the EEG data and are usually referred to as artifacts. In this study, raw EEG data was pre-processed using the x and the y axis gyro data and the two front EEG channels, namely AF4, F8, in order to determine whether the data is artifact free or not. EEG data was collected from subjects that were asked to accomplish two cognitive tasks: pushing a cube and relaxing. Subjects performed each task for a duration of five seconds during 20 trials. The acquired EEG data was divided into 0.25 second epochs. Epochs that were determined to have artifacts were discarded. Power spectral density (PSD) and time domain based features were extracted from artifact free epochs. The features were then used to train a Support Vector Machine (SVM) to determine the corresponding task. The performance of the SVM classifier was compared to that of an Artifical Neural Network (ANN) based one. Experimental results show the efficacy of the SVM based scheme.

Romeda Blog - 04/25/2017

Vision Based Control of Magnetic Levitation System

Gürkan Küçükyıldız

This work presents vision based control of a single-axis magnetic levitation system. The system is the fundamental of the high speed maglev trains and magnetic bearings. A ferro magnetic object is levitated at a desired position in the air gap by applying electromagnetic force against to gravity. The system consists of five main components: a position sensor and a camera, a coil, a controller, a driver and a ferro-magnetic object. Current on the coil, which causes electromagnetic force, was controlled according to position feedback. The mathematical model of the system was obtained based on Newton’s theory and verified by experimental data. The nonlinear relationships between force, current and air gap were found experimentally. The controller was designed using feedback linearization technique based on the nonlinear relationships. In the literature, light based sensors have mostly been used to detect the object’s position. Despite the preferred usage of conventional sensors, some disadvantages are encountered such as: calibration requirement, non-linearity, noise and non robustness. These disadvantages inevitably cause disturbances on the system. In this study, the position of the magnetic object was detected using image processing methods in order to overcome the disadvantages associated with the light based sensors.

Romeda Blog - 04/25/2017

Image Processing Based Package Volume Detection with Kinect

Gürkan Küçükyıldız, Suat Karakaya

In this study, an image processing based package volume detection scheme that utilizes Kinect depth sensor was developed in Matlab environment. Background subtraction method was used to obtain the foreground image that contains the package to be measured from the Kinect depth image. Connected components labeling method was used to segment the foreground image. Out of the components determined by connected components labeling, the one that has the maximum pixel area overlapping with the measuring plate was assumed to be the package of interest. Package orientation angle and center point were then determined. Hough transform was applied to the package image to obtain the lines that passes through package edges. The package corners were obtained by finding the four intersection points of the detected lines. Real world coordinates of the package corners were calculated using the Kinect’s intrinsic matrix. Package width and length were determined by finding the distance between the corners in the real world coordinate system. Finally, the package height was determined by differencing plate depth and average depth value of points on the package surface. It was observed that the algorithm performed successfully and the measurement error was within 1cm under presence of various disturbance effects.

Romeda Blog - 04/25/2017

Design and Navigation of a Robotic Wheel Chair

Gürkan Küçükyıldız, Suat Karakaya

In this study, design and navigation of a robotic wheelchair for disabled or elderly people was explored. Developed system consists of a wheelchair, high-power motor controller card, Kinect camera, RGB camera, EMG sensor, EEG sensor, and computer. Kinect camera was installed on the system in order to provide safe navigation of the system. Depth frames, captured by Kinect camera, were processed with developed image processing algorithm to detect obstacles around the wheelchair. RGB camera was mounted to system in order to detect head-movements of user. Head movement, has the highest priority for controlling of the system. If any head movement detected by the system, all other sensors were disabled. EMG sensor was selected as second controller of the system. Consumer grade an EMG sensor (Thalmic Labs) was used to obtain eight channels EMG data in real time. Four different hand movements: Fist, release, left and right were defined to control the system using EMG. EMG data was classified different classification algorithms( ANN,SVM and random forest) and most voted class was selected as result. EMG based control can be activated or disabled by user making a fist or release during three seconds. EEG based control has lowest priority for controlling the robotic wheelchair. A wireless 14 channels EEG sensor (Emotiv Epoch) was used to collect real time EEG data. Three different cognitive tasks: Solving mathematical problems, relaxing and social task were defined to control the system using EEG. If system could not detect a head movement or EMG signal, EEG based control is activated. In order to other to control user should accomplish the relative cognitive task. During experiments, all users could easily control the robotic wheelchair by head movements and EMG movements. Success of EEG based control of robotic wheelchair varies because of user experiments. Experienced users and un-experienced user changes the result of the system.

Romeda Blog - 04/25/2017

Encoder-Based Localization, Obstacle Detection on a Mobile Robot Platform

Gürkan Küçükyıldız, Suat Karakaya

In this study, a mobile robot which is sensitive to its environment was developed and the mobile robot was tested in different obstacle conditions. The mobile robot senses the obstacles via a laser range finder(Lidar) sensor mounted on its body. The developed mobile robot has two front wheels which are coupled with two separated DC motors and single caster as rear wheel. Real time location of the mobile robot was handled from the encoders coupled with the front wheels. This location info was plotted on a user interface which was developed in Visual C # 2010 environments. Obstacle and heading direction detection was developed in Visual Basic 6.0 environment.

Romeda Blog - 04/25/2017

DC Motorun Gerçek Zamanda Konum ve Hız Kontrolü

PID ve Bulanık Mantık ile DC Motorun Gerçek Zamanda DSPIC Tabanlı Konum ve Hız Kontrolü

Gürkan Küçükyıldız, Gökhan Taşçı

Bu çalışmada seri uyartımlı fırçalı bir DC motorun konum, hız kontrolleri PID ve Bulanık Mantık kontrol yöntemleri kullanılarak gerçek zamanda gerçekleştirilmiştir. Sistem girişine farklı referans değerleri uygulanarak konum ve hız değişkenlerinin istenilen referans değerinde tutulması amaçlanmıştır. Sistem için gerekli kodlar C18 ortamında geliştirilmiş olup DSPIC33FJ128MC804 isimli bir işlemci yardımıyla gerçek zamana aktarılmıştır. PID ve Bulanık mantık yöntemleriyle elde edilen deneysel veriler sonuçlar bölümünde karşılaştırılmıştır.

Romeda Blog - 04/25/2017

DSP Based Real Time Lane Detection Algorithm

Development And Optimization Of DSP Based Real Time Lane Detection Algorithm On A Mobile Robot Platform

Gürkan Küçükyıldız

In this study, development and optimization of a Hough transform based real time lane detection algorithm was explored. Finding lane marks by using Hough transform on captured video frames was the main goal of the system. Image processing code was developed on Visual DSP 5.0 environment and the code was run on BF-561 processor embedded in ADSP BF561 EZ KIT LITE evaluation board. The code was optimized into a form which is satisfactory for real time applications. A mobile robot platform was developed during the study and the image processing algorithm was tested on this platform. The experimental results which were obtained before and after the optimization of the code were compared.

Romeda Blog - 04/25/2017

Image Processing Based Indoor Localization System

Gürkan Küçükyıldız, Suat Karakaya

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 label’s 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.

Romeda Blog - 04/25/2017

Kamera Ve Lazer Kullanarak LIDAR Sistemi Geliştirilmesi

Gürkan Küçükyıldız, Suat Karakaya

Bu çalışmada kamera ve lazer kullanılarak ortamdaki cisimlerin anlık uzaklıklarının tespiti üzerinde çalışılmıştır. Geliştirilen sistemde kamera ve lazer sabit tutularak ikisinin de görüş açısını değiştirecek bir ayna kullanılmıştır. Ayna, kameranın odak çizgisine 45o’lik açı yapacak şekilde sisteme entegre edilmiştir. Sistemde bulunan aynayı döndürmek için bir adet redüktörlü DC motor kullanılmıştır. Bu sayede sistem 270olik bir alanda istenilen hızda ve çözünürlükte veri alabilmektedir. Sistem için gereken kodlar Phyton ortamında yazılmış olup sistemde bulunan DC motorun kontrolü için ise Atmel Atmega328p işlemcisi tabanlı bir geliştirme kartı kullanılmıştır. Yapılan deneylerde geliştirilen sistemin 360o’lik bir alanı 1.8 saniye içerisinde 3.30o çözünürlükle taradığı görülmüştür.

Romeda Blog - 04/25/2017

Kinect Tabanlı Robot Kolu Kontrolü

Gürkan Küçükyıldız, Suat Karakaya

Bu çalışmada Kinect ile robot kolunu anlık olarak hareket ettirmek üzerinde çalışılmıştır. Bu amaçla geliştirilen sistemde Kinect sensör ve bilgisayar kullanılmıştır. Ayrıca çalışma sırasında bir adet üç eksenli robot geliştirilmiş ve deneyler gerçek zamanlı olarak bu geliştirilen robot üzerinde gerçekleştirilmiştir. Üç eksenli robotun hareketi RC servo motorlar ile sağlanmış olup bu motorlar Arduino Uno R3 kartı ile kontrol edilmiştir. Eklem açılarını bulabilmek için Kinect kameradan elde edilen görüntü Processing 2.0b9 ortamında geliştirilen görüntü işleme programı aracılığıyla iskeletleştirilir. Açısı bulunmak istenilen insan uzuvları üzerine bir vektör çizdirilir. Çizilen bu vektörlerin uzunlukları trigonometrik işlemlerden geçirilerek uzuvlar arasındaki açıları vermektedir. Elde edilen açı değerleri seri haberleşme vasıtasıyla Arduino Uno R3 kartına gönderildikten sonra robotun hareketini sağlayan servo motorlar bu açı değerlerine göre döndürülerek sistemin hareketi sağlanmıştır. Yapılan deneyler sonucunda geliştirilen sistemin başarılı olarak çalıştığı ve robotun kollarının yapılan hareketleri anlık olarak taklit edebildiği gözlenmiştir.

Romeda Blog - 04/25/2017

Kinect based control of a Mobile Robot

Gürkan Küçükyıldız, Suat Karakaya

In this study Kinect based control of a mobile robot system was examined. A mobile robot platform was developed for his purpose and developed algorithms were tested on this platform in real time. Mobile robot was actuated by DC Motors. Frames captured from Kinect sensor, which was placed in front of the mobile robot, was processed in Visual Studio C# environment by developed image processing algorithm. Distance between Kinect sensor and detected skeleton was gathered by developed image processing algorithm. Results were sent to developed control card via serial port. Developed control card controlled actuators PD speed control algorithm. At result, it was observed that developed system is operating successfully and follows the skeleton successfully.

Romeda Blog - 04/25/2017