Earticle

최근 아이들의 창의력 학습 및 놀이에 로봇이 활발하게 이용되고 있지만 대부분 로봇이 정형화된 형태를가지고 있으며 프로그램의 의존도가 높아 창의력 학습 및 놀이에 어려움이 있다 우리는 이러한 단점을 보완하기위해 정형화 되지 않은 모듈 형태의 로봇구조를 가지고 있으면서 결합을 쉽고 안정적으로 할 수 있도록 하였고하나의 버튼을 이용하여 사용자가 원하는 동작을 기억시키고 기억된 동작을 똑같이 재생하는 로봇을 제작 하였다또한 모듈 사이를 무선으로 연결하고 정보를 공유하여 다수의 모듈이 결합 되었을 경우 어느 모듈에서나 버튼을한번 누르면 결합된 모든 모듈의 동작을 쉽게 조정할 수 있도록 하였다. 실제 동작을 검증하기 위해 두 개, 3개 및5개의 모듈을 결합하여 자벌레 동작과 보행 로봇을 구현하여 제안된 구조와 알고리즘의 유용성을 보였다. 향 후 무선연결 방법을 보완하여 인터넷상에서 통제할 수 있는 지능화된 모듈라 로봇의 연구가 필요하다.

In recent years, robots have been actively used for children's creativity learning and play, but most robots have a stereotyped form and have a high dependency on the program, making it difficult to learn creativity and play. In order to compensate for these drawbacks, We have created a robot that can easily and reliably combine each other. The robot can memorize the desired operation and execute the memorized operation by using one button. Also, in case multiple modules are combined, pressing the button once on any module makes it possible to easily adjust the operation of all the combined modules. In order to verify the actual operation, two, three, and five modules are combined to demonstrate the usefulness of the proposed structure and algorithm by implementing a gobbling motion and a walking robot. It is required to study intelligent modular robots that can control over the Internet by supplementing the wireless connection method.

동작을 기억하는 모듈라 로봇은 손으로 직접 표현하기 때문에 창의적 구조물을 쉽게 만들고, 동작시킬 수 있다. 하지만 사용자에 의하여 만들어진 동작을 저장할 충분한 저장 공간이 모듈 내에 없어서 만들어진 동작을 재사용이 불가능하며 모듈라 로봇이 다시 동작을 기억할 시에 다른 동작으로 바뀌게 된다. 또한, 다수의 모듈라 로봇을 동시에 동작시킬 수 있는 주 제어기가 없어서 직접적으로 사용자가 모듈라 로봇에 입력해야하는 단점이 있다. 이러한 단점을 극복하고자, 유선 및 무선 네트웍을 이용하고 웹 서버 및 컴퍼넌트 기반 소프트웨어를 설계하여, 주변의 스마트 기기에서 동작시킬 수 있는 원격제어기를 제안하였다. 그리고 제안된 제어기의 하드웨어 개념 및 소프트웨어의 연결 관계를 자세히 제시 하였다. 제안된 방식은 모듈라 로봇에 연결하여 다양한 형태의 구조물을 만들어 동작시키고 저장한 후 다시 재생 동작을 수행하여 동작의 재현성을 보였으며 기존의 저장된 동작을 효과적으로 재생함으로써 유용성을 확인하였다. 아울러 다운로드한 궤적 데이터를 도해적으로 표현하고 실제 동작된 궤적과의 차이를 분석하여 신뢰성을 확인하였다. 향후에는 원격제어기에 저장된 궤적을 인공지능 기법을 이용하여 표준화시켜 모듈라 로봇의 동작을 손쉽게 구현 시킬 예정이다.

A modular robot that memorizes motion can be easily created and operated because it expresses by hand. However, since there is not enough storage space in the module to store the user-created operation, it is impossible to reuse the created operation, and when the modular robot again memorizes the operation, it changes to another operation. There is no main controller capable of operating a plurality of modular robots at the same time, and thus there is a disadvantage that the user must input directly to the modular robot. To overcome these disadvantages, a remote controller has been proposed that can be operated in the surrounding smart devices by designing web server and component based software using wired and wireless network. In the proposed method, various types of structures are created by connecting to a modular robot, and the reconstructed operation is performed again after storing, and the usefulness is confirmed by regenerating the stored operation effectively. In addition, the reliability of the downloaded trajectory data is verified by analyzing the difference between the trajectory data and the actual trajectory. In the future, the trajectory stored in the remote controller will be standardized using the artificial intelligence technique, so that the operation of the modular robot will be easily implemented.

One of key goals of disaster response and relief robots is to acquire knowledge about the situation that would be either dangerous or inaccessible to human operator. However, the robot is difficult to acquire tele-operations from the operator. A disconnected communication link and no-visual control situation are frequently happened in disaster areas. Moreover, disaster scenarios are typically spatially distributed, so we may need an intelligent robot that has functions such as autonomy, cooperation, and collective behaviors. Thus, we propose the bio-inspired modular robot named as ARTHROBOT so as to support emergency responders. ARTHROBOT can assemble or disassemble process based on the proposed mobile algorithms.

Design a modular robot teaching device, combine with wireless communication technology, and robot control technology. Introduce ZigBee nodes into the robot’s every module, transmit the current motion parameters through the wireless communication, adjust the robot motion state and realize the fuzzy control of the robot motion. This device has good openness, does not involve the establishment of the robot kinematics and dynamics model, and avoid a large number of algorithms in the same time. Its operation is simple, convenient, and more suitable for the teaching.

This paper deals with design of the intelligent mechanism and working algorithm for a cube-style modular robot. The modular robot changes own shape according to the working environment. Therefore it is suitable to work in the search and rescue area where requires the adaptation of situation with a shape of snake, legged robot or humanoid. Each of modular unit has to install own controller on the body and drive the body to give some mobility autonomously, also combine and separate their modules with docking and unlocking mechanism. In this paper we proposed design concept of our modular robot including the combination, control, and communication algorithm between modules, and also suggest some application movement with respect to various situations after torque consideration. Torque analysis according to the motion were accomplished by generation of trajectory and followed by simulation with respect to walking gait. The motions are verified with some simple action and shown their effectiveness through actual experiment for corporative motion of multiple modular robots such as inchworm, butterfly, crab, and bipedal motion.

Increasingly, innovations in miniaturization and embedded systems are making pervasive computing a reality. Meanwhile, similar advancements in robotics have made autonomous mobile robots easier to develop and more cost-efficient. However, since the field of ubiquitous robotics remains relatively unexplored, we develop SMARbot (Stevens Modular Autonomous Robot), as a tool to explore the pervasive capability of miniature robots. The SMARTBot was designed to be a highly modular, adaptable, inexpensive base model to integrate miniature mobile robots into the pervasive computing environment. The hardware and software architecture of the prototype are proposed. The experimental results demonstrate the feasibility and efficiency of the SMARbot in the real-world scenarios

Modular robot manipulator is a robotic system assembled from discrete joints and links into one of many possible manipulator configurations. This paper describes the design method of newly developed modular robot manipulator and the methodology of a task based reconfiguration of it. New locking mechanism is proposed and it provides quick coupling and decoupling. A parallel connection method is devised and it makes modular robot manipulator working well and the number of components on each module reduced. To automatically determine a sufficient or optimal arrangement of the modules for a given task, we also devise an algorithm that automatically generates forward and inverse manipulator kinematics, and we propose an algorithm which maps task specifications to the optimized manipulator configurations. Efficient genetic algorithms are generated and used to search for a optimal manipulator from task specifications. A few of design examples are shown.

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent´s dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to choose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input-output pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless ...

In this article we present modular neural control for a leg-wheel hybrid robot consisting of three legs with omnidirectional wheels. This neural control has four main modules having their functional origin in biological neural systems. A minimal recurrent control (MRC) module is for sensory signal processing and state memorization. Its outputs drive two front wheels while the rear wheel is controlled through a velocity regulating network (VRN) module. In parallel, a neural oscillator network module serves as a central pattern generator (CPG) controls leg movements for sidestepping. Stepping directions are achieved by a phase switching network (PSN) module. The combination of these modules generates various locomotion patterns and a reactive obstacle avoidance behavior. The behavior is driven by sensor inputs, to which additional neural preprocessing networks are applied. The complete neural circuitry is developed and tested using a physics simulation environment. This study verifies that the neural modules can serve a general purpose regardless of the robot's specific embodiment. We also believe that our neural modules can be important components for locomotion generation in other complex robotic systems or they can serve as useful modules for other module-based neural control applications.

본 연구에서는 교육 및 놀이를 통해 창의력 증진이 가능한 재구성 모듈러 로봇에 대하여 다루고 있다. 보통 모듈러 로봇은 각각 셀들의 조합으로 변형 재구성하고 다양한 형태의 구조를 만드는데 적합한 로봇으로 설계되어 있지만 이를 이용하여 교육과 놀이를 주제로 한 연구는 국외에 소수 있으며 국내에는 전무하다. 제안하는 형태는 기존 모듈러 로봇과 같이 같은 동작만 반복하는 모듈이 아니라 소리를 내는 소리모듈, 바퀴가 달린 바퀴모듈, 시각적 표현을 위한 LED모듈, 전원공급을 위한 전원모듈, 통신을 위한 블루투스모듈, 움직이는 동작을 구현 가능하도록 하는 관절모듈이 있는 구조로 설계되어 있다. 이와 같은 다른 능력이 부여된 각 모듈의 다양한 결합을 이용하여 창의적 활동이 가능하도록 하고, 아이들이 쉽게 사용 할 수 있도록 한다. 본 연구에서는 모듈별 각기 다른 능력을 부여하여 교육과 놀이가 가능하도록 하는 방식의 모듈러 로봇의 설계 및 동작을 다루고 있다. 그리고 연구된 동작은 실제 제작 및 실험을 통해 동작의 유용성을 보였다.

This study deals with reconfigurable modular robot with respect to the compact and capability of representing the various actions for promoting creativity through education and play. Generally modular robot can be designed as a suitable robot that is transformed to various structure by reconstructing each cells, However, there are only few research on the education and play using those robots in the world and still nothing domestically. Unlike the existing modular robots only having a repeating motion, the proposed modular works by individual module such as sound is produced by sound module, wheel is driven by wheel module, LED module controls the visual expression, power is supplied by battery module, bluetooth module for communication, and dynamic motion realization is possible by using joint module. By manipulating the abilities endowed by individual modules, diversity of creative activities is possible and thus made an easy access for children. This study deals with the design of modular robotic by using the variety of different modules to endowed the learning and playing ability. And the study showed the utility of the operating behavior over the actual production and testing.

쇼핑카트 부착 모듈형 로봇 'Cart-Rider'는 어드미턴스 제어를 통한 사용자의 힘 보조 기능, 딥러닝을 활용한 네비게이션 기능, GPS 를 활용한 도난 방지 기능을 제공하는 로봇으로 대형 마트에서 발생하는 안전사고 및 쇼핑카트 도난을 예방하는 동시에 사용자에게 편의성을 제공하는 로봇이다. 또한 여러 대를 겹쳐서 보관하는 기존의 카트 시스템을 유지하고 탈부착이 용이하도록 하드웨어를 제작하여 환경에 영향을 주지 않고 유지 및 보수가 용이하도록 제작했다.

본 연구는 모듈형 로봇 프로그래밍 교육이 중학교 정보교과 학습동기에 미치는 영향에 대하여 알아보고자 한다. 이를 위하여 실험집단 25명, 통제집단 25명을 대상으로 실험을 하였으며 연구도구로 모듈형 로봇 프로그래밍 교육, 학습동기 검사지를 사용하였다. 학습동기 검사지의 결과를 독립표본 t-검정과 대응표본 t-검정으로 자료를 처리한 결과, 실험집단은 통제집단 보다 학습동기가 9.36점 높았고, 실험집단 내에서도 사후검사가 사전검사보다 15.44점 높았다. 특히 정보교과 학습동기의 모든 하위요소 향상에 유의미한 영향을 주며, 그 중 주의집중, 관련성, 만족감 요소 향상에 큰 영향을 주었다. 결론적으로 모듈형 로봇 프로그래밍 교육이 학생들의 정보교과 학습동기 향상에 긍정적인 영향을 주는 것을 알 수 있다.

This study examines the impact of modular robot programming education on middle school informatics curriculum learning motivation. For this purpose, the experiment was conducted with a experimental group of 25 people and a control group of 25 people, and modular robot programming education and learning motivation test were used as research tools. As a result of processing the results of the learning motivation test paper with the independent sample t-test and the paired t-test, the experimental group had 9.36 points higher learning motivation than the control group and 15.44 points higher than the pre-test. In particular, it significantly affected the improvement of all the sub-components of the learning motivation, and among them, it greatly affected the enhancement of attention, relevance and satisfaction. In conclusion, it can be seen that modular robot programming education has a positive effect on improving students' motivation to learn the informatics curriculum.

모듈라 뱀형 로봇은 고장에 대한 강인성과 환경에 유연한 이동 특성을 가지고 있으나, 제어가 어렵다는 단점이 있다. 진화연산을 로봇에 이용한 많은 연구가 진행되어 왔지만, 어떤 기법의 진화연산이 문제에 더 적합하고, 높은 성능을 얻을 수 있는지에 대한 비교는 거의 이루어지지 않고 있다. 본 논문은 두 가지 대표적인 진화기법인 GA와 GP를 이용하여 모듈라 뱀형 로봇의 이동 제어를 수행하였다. 대상 로봇은 H/W로 구현이 가능한 실제 모듈로 구성되었고, Webots을 사용하여 시뮬레이션 실험을 수행하였으며, GA와 GP 기법에 의한 결과를 비교 분석하였다.

This paper deals with design of a cube-style modular robot. The modular robot can change its own form according to the working environment. Therefore it is suitable to work in the search and rescue area with the shape of snake, legged robot and humanoid robot. Each of modular unit has to install its own controller on the body and driving mechanism in order to give it mobility autonomously. And also they should attach and detach each other with docking mechanism and algorithm. Using this mechanism, they can make union, separation, recombination. The other important point is that some information of each cell should be exchanged to reconfigure their shape and to make some docking of the modular cell. In this paper we suggested a design concept of our modular robot focused on the docking mechanism of the robot.

본 논문에서는 다양한 인공지능을 모듈화하고 모듈들을 서로 결합하여 서비스를 제공할 수 있는 지능형 서비스 로봇에서, 인공지능 모듈들을 라이브러리 간의 의존성을 해소하기 위한 방법 중 하나인 가상 머신의 일종인 도커(Docker)를 활용하여 컨테이너화하여 사용할 때, 인공지능 모듈 내부에서 사용하는 신경망 데이터에 해당하는 지능 모델에 대해 버전 관리를 수행하면서 클라우드 등 외부 서버를 이용하여 증강시킨 지능 모델을 공유하는 기술 개발에 대해 설명한다.

This paper proposes a design methodology of self-reconfigurable kinematics and control engine for modular and reconfigurable robots. A modular manipulator has been proposed to meet the requirement of task adaptation in versatile needs for service and industrial robot area and the function of self-reconfiguration is required to extend the application of modular robots. Kinematic and dynamic contexts are extracted from the module and assembly information and related codes are automatically generated including controller. Thus a user can easily build and use a modular robot without professional knowledge. Simulation results are presented to verify the validity of the proposed method.

Modular snake-like robots are robust for failure and have flexible locomotions for obstacle environment than of walking robot. This requires an adaptation capability which is obtained from a learning approach, but has not been analysed as well. In order to investigate the property of adaptation of locomotion for different terrains, NEAT controllers are trained for a flat terrain and tested for obstacle terrains. The input and output characteristics of the adaptation for the neural network controller are analyzed for different terrains in frequency domain.