Mobile Robot

Romeda / Mobile Robot

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 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

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

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

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