Path planning is necessary for mobile robots to perform precise and rapid tasks. A collision avoidance function must be included so that the robot can move safely during work, and it must be able to create an optimal path to reduce work execution time and save energy. In this paper, we propose a smart route generation algorithm that searches for global route with an algorithm that can speed up route search and integrates the TEB algorithm that can search for regional optimum routes in real time according to the situation. The performance of the proposed algorithm was verified through actual driving experiments of mobile robots.

In this study, we present an algorithm for indoor robot position estimation. Estimating the position of an indoor robot using a fixed imaging device obviates the need for complex sensors or hardware, enabling easy estimation of absolute position through marker recognition. However, location estimation becomes impossible when the device moves away from the surrounding obstacles or the screen, presenting a significant drawback. To solve this problem, we propose an algorithm that improves the precision of robot indoor location estimation using a Gaussian Mixture Model(GMM) and a Kalman filter estimation model. We conducted an actual robot operation experiment and confirmed accurate position estimation, even when the robot was out of the image.

선박은 화물 운송의 효율을 증대시키기 위해 대형화되는 추세이다. 선박 대형화는 선박 작업자의 이동시간 증가, 업무 강도 증가 및 작업 효율 저하 등으로 이어진다. 작업 업무 강도 증가 등의 문제는 젊은 세대의 고강도 노동 기피 현상과 맞물러 젊은 세대의 노동력 유 입을 감소시키고 있다. 또한 급속한 인구 노령화도 젊은 세대의 노동력 유입 감소와 복합적으로 작용하면서 해양산업 분야의 인력 부족 문 제는 극심해지는 추세이다. 해양산업 분야는 인력 부족 문제를 극복하기 위해 지능형 생산설계 플랫폼, 스마트 생산 운영관리 시스템 등의 기술을 도입하고 있으며, 스마트 자율물류 시스템도 이러한 기술 중의 하나이다. 스마트 자율물류 시스템은 각종 물품들을 지능형 이동로봇 을 활용하여 전달하는 기술로서 라이다, 카메라 등의 센서를 활용해 로봇 스스로 주행이 가능하도록 하는 것이다. 이에 본 논문에서는 이동 로봇이 선박 갑판의 통행로를 감지하여 stop sign이 있는 곳까지 자율적으로 주행할 수 있는지를 확인하였다. 자율주행은 Nvidia의 End-to-end learning을 통해 학습한 데이터를 기반으로, 이동로봇에 장착된 카메라를 통해 선박 갑판의 통행로를 감지하여 수행하였다. 이동로봇의 정지 는 SSD MobileNetV2를 이용하여 stop sign을 확인하여 수행하였다. 실험은 약 70m 거리의 선박 갑판 통행로를 이동로봇이 이탈 없이 주행 후 stop sign을 확인하여 정지하는지를 5회 반복 실험하였으며, 실험 결과 경로이탈 없이 주행하는 결과를 얻을 수 있었다. 이 결과를 적용한 스 마트 자율물류 시스템이 산업현장에 적용된다면 작업자가 작업 시 안정성, 노동력 감소, 작업 효율이 향상될 것으로 사료된다.

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. This representative case is the KIVA robot in Amazon. KIVA robots are only operated inside Amazon, but information about them is not exposed to the outside world, so a variety of similar robots are developed and operated in several places around the world. They are applied in various fields such as education, medical, silver, military, parking, construction, marine, and agriculture, creating a variety of application robots. 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 indoor mobile robot position recognition and driving experiment using QR Code during the development of QR Code-aware indoor mobility robots.

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. This representative case is the KIVA robot in Amazon. KIVA robots are only operated inside Amazon, but information about them is not exposed to the outside world, so a variety of similar robots are developed and operated in several places around the world. They are applied in various fields such as education, medical, silver, military, parking, construction, marine, and agriculture, creating a variety of application robots. 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 the driving control of indoor mobile robot during the development of QR Code-aware indoor mobility robots.

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.

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. This representative case is the KIVA robot in Amazon. KIVA robots are only operated inside Amazon, but information about them is not exposed to the outside world, so a variety of similar robots are developed and operated in several places around the world. They are applied in various fields such as education, medical, silver, military, parking, construction, marine, and agriculture, creating a variety of application robots. 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 a study on the speculative navigation using auxiliary encoder 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.

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. This representative case is the KIVA robot in Amazon. KIVA robots are only operated inside Amazon, but information about them is not exposed to the outside world, so a variety of similar robots are developed and operated in several places around the world. They are applied in various fields such as education, medical, silver, military, parking, construction, marine, and agriculture, creating a variety of application robots. 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 a suggestion of control method in QR Code-aware indoor mobility robots.

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. This representative case is the KIVA robot in Amazon. KIVA robots are only operated inside Amazon, but information about them is not exposed to the outside world, so a variety of similar robots are developed and operated in several places around the world. They are applied in various fields such as education, medical, silver, military, parking, construction, marine, and agriculture, creating a variety of application robots. 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 the driving operation techniques during the development of QR code-aware indoor mobility robots.

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. This representative case is the KIVA robot in Amazon. KIVA robots are only operated inside Amazon, but information about them is not exposed to the outside world, so a variety of similar robots are developed and operated in several places around the world. They are applied in various fields such as education, medical, silver, military, parking, construction, marine, and agriculture, creating a variety of application robots. 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 the moving control model during the development of QR code-aware indoor mobility robots.

Robots for a wide range of purposes have been developed along with the rapid industrialization. On the basis of higher convenience, the robots have been creating new industrial environment. The robots are generally classified into service robots and industrial robots. Robots in various shapes have been developed on the basis of the autonomous mobile robots. The autonomous mobile robots have the possibility to crash against any object in their moving range. This paper suggests a collision avoidance method to prevent collision of robots. The collision avoidance method analyzes the road context data and makes a robot move to a safe area. The collision avoidance method proposed in this paper converts the road context data into the information value. The collision avoidance method analyzes the present risk on the basis of the converted information value. The collision avoidance method makes a robot move to a safe area when crash is estimated by the information 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.

The predictive control system using model-based predictive control is a very effective way to optimize the present inputs considering the states and future errors of the reference trajectory, but it has a drawback in that a control input matrix must be repeatedly calculated with a long calculation time at every sampling for minimizing future errors in a predictive interval. In this study, we applied the neural network simulating the predictive control method for the trajectory tracking control of the mobile robot to reduce complex control method and computation time which are the disadvantage of predictive control. In addition, the neural network showed excellent performance by the generalization even for a different reference trajectory. Therefore, The controller is designed by modeling the model-based predictive control gains for the reference trajectory using a neural networks. Through the computer simulation, the proposed control method showed better performance than the general predictive control method.

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.

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.