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 on the driving operation techniques during the development of QR code-aware indoor mobility robots.

본 연구에서는 2D 화면과 3D화면으로 각각 제시된 운전 시뮬레이션 환경에서 운전자의 종적 차량통제, 주관 적 피로감 및 지각된 현실감에서의 차이를 비교하였다. 본 연구의 결과들을 요약하면 다음과 같다. 첫째, 실험참 가자들은 미리 정해진 네 가지 수준의 목표속도(60, 80, 100 및 120km/h)를 유지할 때 3D 조건보다는 2D 조건에 서, 그리고 목표속도가 낮을수록 목표속도에 비해 더 빠르게 운전하였고, 이러한 경향은 목표속도 조건과 상관없 이 일정하였다. 둘째, 선행차량과의 차간거리 유지수행에 대한 분석 결과, 2D 조건에 비해 3D 조건에서 실험참가 자들은 선행차량과 더 근접한 차간거리를 유지하며 주행하였는데, 특히 선행차량의 주행속도가 비교적 느렸던 조건(즉, 60km/h)에 비해 비교적 빨랐던 조건(즉, 80 및 100km/h)에서 이러한 경향이 두드러졌다. 셋째, 속도 유지 과제와 선행차량과의 차간거리 유지수행 모두에서 2D 조건에 비해 3D 조건에서 실험참가자들이 경험하는 피로 감의 수준이 더 높았으나 주관적 현실감에 대한 평가에서는 두 가지 과제 모두에서 2D와 3D 조건에 따라 유의 한 차이가 관찰되지 않았다.

The purpose of this study was to quantitatively evaluate the effects of the secondary task with unexpected situation during simulated driving using the variable indicating control of vehicle. The subjects were participated 50s people including 15 males with 29.5±6.7 years of driving experiences and 15 females with 20.1±5.6 years of driving experiences. All subjects were instructed to keep a certain distance (30m) from the car ahead and a constant speed (80km/hr or 100km/hr). Sending text message(STM) and Searching navigation(SN) were selected as the secondary task. Experiment consisted of driving alone for 1 minute and driving with secondary task for 1 minute. It was defined driving phase and unexpected situation phase respectively. Medial-lateral coefficient of variation(MLCV) of car movement was analyzed for evaluating lane keeping in this study. In the results, MLCV was increased by 118.3% at 100km/hr. In the case of secondary task, MLCV in STN and in SN were increased by 235.1% and 290.3%, respectively. There was no significant difference between male and female. In case of driving at high-speed and with secondary task, it may be disturbed constant control of the vehicle when unexpected situation appeared suddenly.

본 연구의 목적은 차량 통제 변인과 동작의 부드러움 변인을 이용하여 동시 과제 수행이 운전 수행 능력에 미치는 영향을 정량적으로 제시하는 것이다. 1~2년의 운전 경력을 가진 피험자 20명이 실험에 참여하였다. 피험자는 동작분석을 위해 상지(shoulder, elbow, wrist) 및 하지(knee, ankle, toe)에 9개의 마커를 부착한 후, 운전 시뮬레이터를 이용하여 80km/hr로 주행하는 선행 차량과 30m의 간격을 유지하며 직선 주행하도록 하였다. 동시과제는 문자 메시지 보내기와 네비게이션 검색으로 선정하였다. 실험 시간은 2분으로 운전 시작 후 1분은 운전만을, 다음 1분은 운전과 동시과제를 함께 실시하도록 하였고, 각각 운전구간과 동시과제구간으로 정의하였다. 차간거리(Anterior-Posterior Coefficient of Variation, APCV) 및 차선이격거리의 분산계수(Medial-Lateral Coefficient of variation, MLCV)와 저크비용함수(Jerk-cost function, JC)를 이용하여 운전 수행 능력을 평가하였다. APCV는 운전구간에 비해 운전 중 네비게이션 검색 시 222.1% 증가하였다. MLCV는 문자 메시지 전송 과제를 수행할 경우, 318.2%, 네비게이션 검색 과제를 수행할 경우 309.4%가 증가하였다. JC는 운전구간에 비해 동시과제 수행 시, 팔꿈치, 무릎, 발목, 발가락에서 유의하게 증가하였고, 하지마커 전체의 평균값은 문자과제 수행 시 218.2%, 네비게이션 과제 수행 시 294.7%가 증가하였다. 운전 중 동시과제의 수행은 JC를 증가시켜 운전자의 동작의 부드러움을 감소시키고, APCV와 MLCV를 증가시켜 차량의 횡적 종적 통제를 어렵게 한다고 결론 내릴 수 있다.

이상과 같이 엑튜에이터의 동특성 해석을 행하고 PID 제어기 및 최적 제어기를 설계하여 응답시뮬레이션을 한 결과 다음과 같은 결론을 얻었다. 1) 엑튜에이터 부의 시정수는 엔진 부의 시정수에 비해 아주 작아 생략하여 제어계를 구성할 수 있다. 2) 한계 감도법에 의해 PID 제어기를 시뮬레이션 한 결과 PID 제어 가버너는 전반적으로 오버슈트가 크고 중속 및 고속 상태에서는 정정시간이 비교적 짧지만 저속에서는 정상 상태에 도달하는데 상당한 시간이 걸린다. 3) 중량 matrix를 적당히 선택하여 최적 피이드 백 게인을 구한 후 마이크로 프로세서에 저장하여 제어기를 구성하면 PID 제어기 보다 양호한 응답 특성을 갖는 제어기를 설계 할 수 있다.

The aims of this paper is to develop a modular agricultural robot and its autonomous driving algorithm that can be used in field farming. Actually, it is difficult to develop a controller for autonomous agricultural robot that transforming their dynamic characteristics by installation of machine modules. So we develop for the model based control algorithm of rotary machine connected to agricultural robot. Autonomous control algorithm of agricultural robot consists of the path control, velocity control, orientation control. To verify the developed algorithm, we used to analytical techniques that have the advantage of reducing development time and risks. The model is formulated based on the multibody dynamics methods for high accuracy. Their model parameters get from the design parameter and real constructed data. Then we developed the co-simulation that is combined between the multibody dynamics model and control model using the ADAMS and Matlab simulink programs. Using the developed model, we carried out various dynamics simulation in the several rotation speed of blades.

In this paper, an adaptive mechanism based on immune algorithm is designed and it is applied to the driving control of the autonomous guided vehicle(AGV). When the immune algorithm is applied to the PID controller, there exists the case that the plant is damaged by the abrupt change of PID parameters since the parameters are adjusted almost randomly. To solve this problem, a neural network used to model the plant and the parameter tuning of the model is performed by the immune algorithm. After the PID parameters are determined through this off-line manner, these parameters are then applied to the plant for the on-line control using immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough initially, the weighting parameters are adjusted more accurately through the on-line fine tuning. The experiment for the control of steering and speed of AGV is performed. The results show that the proposed controller provides better performances than other conventional controllers.