The role of QR Code robots in smart logistics is great. Cognitive robots, such as logistics robots, were mostly used to adjust routes and search for peripheral sensors, cameras, and recognition signs attached to walls. However, recently, the ease of making QR Codes and the convenience of producing and attaching a lot of information within QR Codes have been raised, and many of these reasons have made QR Codes recognizable as visions and others. In addition, there have been cases in developed countries and Korea that control several of these robots at the same time and operate logistics factories smartly. In this work, we are developing a robot that can recognize its current position, move and control in the directed direction through two-dimensional QR Codes with the same horizontal and vertical sides, and the error is to create a QR Code robot with accuracy to reach within 3mm. This paper focuses on experimental environments for testbeds during the development of QR Code-aware indoor mobility robots.

With the recent development of autonomous driving technology, many researchers have studied autonomous mobile robots. Accordingly, they are developing diverse mobile robot actuators. However, most actuators mainly use reducers made of chains, belts, multi-stage gears, etc. So the volume and size of the actuators increase, and power transmission efficiency tends to be relatively low. Therefore, this study has proposed the reducer of the mobile robot actuator using a complex planetary gear train with small volume and high power transmission efficiency, and has confirmed the stability of the proposed reducer through finite element analysis.

This paper proposes a method to reduce the pose error and to solve the dead reckoning issue which occurs when the mobile robot with continuous-tracks travels in the unstructured environments. When the continuous-track type mobile platform travels on terrain such as sand, gravel, stairs and etc., slippage occurs and thus the driving state of the mobile robot cannot be recognized normally. To compensate for this pose error, the proposed method utilizes optical flow estimation detected by camera. This method is tested through experiment. Finally, This method reduces the pose error detected on inertia measurement unit within some limit, while the pose error of without compensation increases without limit during robot move.

본 연구에서는 해상에서 선박의 외측 표면 검사를 위한 모바일 로봇의 개발에 대해 언급하였다. 해상에서 선체 측면에 대한 검사를 육안으로 진행하기 어려우며 이러한 검사를 효과적으로 수행하기 위해 모바일 로봇은 선체 측면에 부착되어 주행할 수 있는 기능을 갖추어야 한다. 이를 위해 선체 측면과의 부착력을 발생시키기 위해 영구 자석 모듈을 도입하였고, 곡면 주행 시 자기력의 변화를 최소화하는 구조로 설계를 하였다. 이러한 설계를 바탕으로 4개의 네오디움 자석, 4개의 구동바퀴, 영상 획득 모듈로 구성되는 모바일 로봇을 제작하였다. 제작된 로봇에 대해 선체와의 부착력을 확인하기 위한 하중 실험을 실시하였고, 주행이후 정지 시 측면 미끄럼 실험과 주행 속도 측정 실험을 실시하였다. 실험 결과 13 [Kgf]까지 선체와의 부착력을 유지할 수 있었고, 미끄러짐이 없는 하중은 8 [Kgf]까지였다. 주행 실험에서는 6.5 [A]의 전류에 대해 0.82 [m/s]의 속도로 주행할 수 있는 것을 확인하였다. 선박의 표면 검사를 위해 개발한 모바일 로봇의 특성 실험을 통해 로봇의 유용성을 확인할 수 있었다.

In this research we investigate motion controller performance for mobile robots according to changes in the control loop sampling time. As a result, we suggest a proper range of the sample time, which can minimize final posture errors while improving tracking capability of the controller. For controller implementation into real mobile robots, we use a smooth and continuous motion controller, which can respect robot’s path curvature limitation. We examine motion control performance in experimental tests while changing the control loop sampling time. Toward this goal, we compare and analyze experimental results using two different mobile robot platforms; one with real-time control and powerful hardware capability and the other with non-real-time control and limited hardware capability.

본 논문에서는 지능을 대신하는 센서들 특히 시각을 채용한 이동형 로봇을 설계하였으며, 다양한 서비스 제공에 필요한 장애물 회피와 추적방법등을 다룬다. 사용된 알고리즘 중 색상의 정보와 움직임 정보를 처리하는 알고리즘과 형태 정보를 알아내기 위한 알고리즘을 사용하였으며, 색상 정보 데이터와 형태정보 데이터를 합성하여 보다 정확한 물체의 정보를 인식할 수 있게 하였다. 또한, 이동로봇의 기능을 수행하기 위하여 이동 하드웨어를 구현하여 노트북을 위에 얹는 형식으로 지능형 이동 로봇으로의 기능을 수행할 수 있게 하였다. 이동 하드웨어 위에 노트북을 얹어서 로봇의 제어 속도를 높이고 신호의 잡음을 없애고 정확하게 정보를 전달 받게 하기 위해 직렬 통신으로 연결하여 제작하였다.

In this paper, we propose a modified ORB-SLAM (Oriented FAST and Rotated BRIEF Simultaneous Localization And Mapping) for precise indoor navigation of a mobile robot. The exact posture and position estimation by the ORB-SLAM is not possible all the times for the indoor navigation of a mobile robot when there are not enough features in the environment. To overcome this shortcoming, additional IMU (Inertial Measurement Unit) and encoder sensors were installed and utilized to calibrate the ORB-SLAM. By fusing the global information acquired by the SLAM and the dynamic local location information of the IMU and the encoder sensors, the mobile robot can be obtained the precise navigation information in the indoor environment with few feature points. The superiority of the modified ORB-SLAM was verified to compared with the conventional algorithm by the real experiments of a mobile robot navigation in a corridor environment.

In this paper, we present a finite-time sliding mode control (FSMC) with an integral finitetime sliding surface for applying the concept of graph theory to a distributed wheeled mobile robot (WMR) system. The kinematic and dynamic property of the WMR system are considered simultaneously to design a finite-time sliding mode controller. Next, consensus and formation control laws for distributed WMR systems are derived by using the graph theory. The kinematic and dynamic controllers are applied simultaneously to compensate the dynamic effect of the WMR system. Compared to the conventional sliding mode control (SMC), fast convergence is assured and the finite-time performance index is derived using extended Lyapunov function with adaptive law to describe the uncertainty. Numerical simulation results of formation control for WMR systems shows the efficacy of the proposed controller.

This paper proposes a robust image stabilization system for a mobile robot using an Extended Kalman Filter (EKF). Though image information is one of the most efficient data used for robot navigation, it is subjected to noise which is the result of internal vibration as well as external factors such as uneven terrain, stairs, or marshy surfaces. The camera vibration deteriorates the image resolution by destroying the image sharpness, which seriously prevents mobile robots from recognizing their environment for navigation. In this paper, an inclinometer was used to measure the vibration angle of the camera system mounted on the robot to obtain a reliable image by compensating for the angle of the camera vibration. In addition the angle prediction obtained by using the EKF enhances the image response analysis for real time performance. The experimental results show the effectiveness of the proposed system used to compensate for the blurring of the images.

Various robot platforms have been designed and developed to perform given tasks in a hazardous environment for the purpose of surveillance, reconnaissance, search and rescue, and etc. We have considered a terrain adaptive hybrid robot platform which is equipped with rapid navigation on flat floors and good performance on overcoming stairs or obstacles. Since our special consideration is posed to its flexibility for real application, we devised a design of a transformable robot structure which consists of an ordinary wheeled structure to navigate fast on flat floor and a variable tracked structure to climb stairs effectively. Especially, track arms installed in front side, rear side, and mid side are used for navigation mode transition between flatland navigation and stairs climbing. The mode transition is determined and implemented by adaptive driving mode control of mobile robot. The wheel and track hybrid mobile platform apparatus applied off-road driving mechanism for various professional service robots is verified through experiments for navigation performance in real and test-bed environment.

In this paper, we present a global localization and position error compensation method in a known indoor environment using magnet hall sensors. In previous our researches, it was possible to compensate the pose errors of xe, ye, θe correctly on the surface of indoor environment with magnets sets by regularly arrange the magnets sets of identical pattern. To improve the proposed method, new strategy that can realize the global localization by changing arrangement of magnet pole is presented in this paper. Total six patterns of the magnets set form the unique landmarks. Therefore, the virtual map can be built by using the six landmarks randomly. The robots search a pattern of magnets set by rotating, and obtain the current global pose information by comparing the measured neighboring patterns with the map information that is saved in advance. We provide experimental results to show the effectiveness of the proposed method for a differential drive wheeled mobile robot.

This paper proposes a low-complexity indoor localization method of mobile robot under the dynamic environment by fusing the landmark image information from an ordinary camera and the distance information from sensor nodes in an indoor environment, which is based on sensor network. Basically, the sensor network provides an effective method for the mobile robot to adapt to environmental changes and guides it across a geographical network area. To enhance the performance of localization, we used an ordinary CCD camera and the artificial landmarks, which are devised for self-localization. Experimental results show that the real-time localization of mobile robot can be achieved with robustness and accurateness using the proposed localization method.

This paper proposes a novel face detection method that finds tiny faces located at a long range even with low-resolution input images captured by a mobile robot. The proposed approach can locate extremely small-sized face regions of 12x12 pixels. We solve a tiny face detection problem by organizing a system that consists of multiple detectors including a mean-shift color tracker, short- and long-rage face detectors, and an omega shape detector. The proposed method adopts the long-range face detector that is well trained enough to detect tiny faces at a long range, and limiting its operation to only within a search region that is automatically determined by the mean-shift color tracker and the omega shape detector. By focusing on limiting the face search region as much as possible, the proposed method can accurately detect tiny faces at a long distance even with a low-resolution image, and decrease false positives sharply. According to the experimental results on realistic databases, the performance of the proposed approach is at a sufficiently practical level for various robot applications such as face recognition of non-cooperative users, human-following, and gesture recognition for long-range interaction.

Most omni-directional mobile robots have to change their trajectory for avoiding obstacles regardless of the size of the obstacles. However, an omni-directional mobile robot having kinematic redundancy can maintain the trajectory while the robot avoids small obstacles. This works deals with the kinematic modeling and motion planning of an omni-directional mobile robot with kinematic redundancy. This robot consists of three wheel mechanisms. Each wheel mechanism is modeled as having four joints, while only three joints are necessary for creating the omni-directional motion. Thus, each chain has one kinematic redundancy. Two types of wheel mechanisms are compared and its kinematic modeling is introduced. Finally, several motion planning algorithms using the kinematic redundancy are investigated. The usefulness of this robot is shown through experiment.

Recently, robotic automation in clinical laboratory becomes of keen interest as a fusion of bio and robotic technology. In this paper, we present a new robotic platform for clinical tests suitable for small or medium sized laboratories using mobile robots. The mobile robot called Mobile Agent is designed as transfer system of blood samples, reagents, microplates, and any instruments. Also, the developed mobile agent can perform diverse tests simultaneously based on its cooperative and distributed ability. The driving circuits for the mobile agent are embedded in the robot, and each mobile agent communicates with other agents by using Bluetooth communication. The RFID system is used to recognize patient information. Also, the magnetic hall sensor is embedded to remove and compensate the cumulated error of locomotion at the bottom of mobile agent. The proposed mobile agent can be easily used for various applications because it is designed to be compatible with general software development tools. The Mobile agents are manufactured, and feasibility of the robot and localization of the agents using magnetic hall sensor are validated by preliminary experiments.

A robust position-sensing system is proposed in this paper for ubiquitous mobile robots which move indoor as well as outdoor. The Differential GPS (DGPS) which has position estimation error of less than 5 m is a general solution when the mobile robots are moving outdoor, while an active beacon system (ABS) with embedded ultrasonic sensors is selected as an indoor positioning system. The switching from the outdoor to indoor or vice versa causes unstable measurements on account of the reference and algorithm changes. To minimize the switching time in the position estimation and to stabilize the measurement, a robust position-sensing system is proposed. In the system, to minimize the switching delay, the door positions are stored and updated in a database. The reliability and accuracy of the robust positioning system based on DGPS and ABS are verified through the real experiments using a mobile robot prepared for this research and demonstrated.

Though the final goal of mobile robot navigation is to be autonomous, operators intelligent and skillful decisions are necessary when there are many scattered obstacles. There are several limitations even in the camera-based tele-operation of a mobile robot, which is very popular for the mobile robot navigation. For examples, shadowed and curved areas acnnot be viewed using a narrow view-angle camera, especially in bad weather such as on snowy or rainy days. Therefore, it is necessary to have other sensory information for reliable tele-operations. In this paper, sixteen ultrasonic sensors are attached around a mobile robot in a ring pattern to measure the distances to obstacles. Acollision vector is introduced in this paper as a new tool for obstacle avoidance, which is defined as a normal vector from an obstacle to the mobile robot. Based on this collision vector, a virtual reflection force is generated to avoid the obstacles and then the reflection force is transferred to an operator who is holding a joystick to control the mobile robot. Relying on the reflection force, the operator can control the mobile robot more smoothly and safely. For this bi-directional tele-operation, a master joystick system using a hall sensor was designed to resolve the existence of nonlinear sections, which are usual for a general joystick with two motors and potentiometers. Finally, the efficiency of a force reflection joystick is verified through the comparison of two vision-based tele-operation experiments, with and without force reflection.