• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1115-1120
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• 2003

The present paper shows the possible development of particle swarm optimization (PSO) based fuzzy-neural networks (FNN) which can be employed as an important building block in real life robot systems, controlled by voice-based commands. The PSO is employed to train the FNNs which can accurately output the crisp control signals for the robot systems, based on fuzzy linguistic spoken language commands, issued by an user. The FNN is also trained to capture the user spoken directive in the context of the present performance of the robot system. Hidden Markov Model (HMM) based automatic speech recognizers are developed, as part of the entire system, so that the system can identify important user directives from the running utterances. The system is successfully employed in a real life situation for motion control of a redundant manipulator.

• 한국산업융합학회 논문집
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• 제16권4호
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• pp.127-132
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• 2013

We describe a research about remote control of mobile robot based on voice command in this paper. Through real-time remote control and wireless network capabilities of an unmanned remote-control experiments and Home Security / exercise with an unmanned robot, remote control and voice recognition and voice transmission are possible to transmit on a PC using a microphone to control a robot to pinpoint of the source. Speech recognition can be controlled robot by using a remote control. In this research, speech recognition speed and direction of self-driving robot were controlled by a wireless remote control in order to verify the performance of mobile robot with two drives.

• 제어로봇시스템학회논문지
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• 제16권4호
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• pp.319-328
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• 2010

This paper proposes a robust adaptive fuzzy tracking control scheme for a nonholonomic mobile robot with external disturbances as well as parameter uncertainties in the robot kinematics, the robot dynamics, and the actuator dynamics. In modeling a mobile robot, the actuator dynamics is integrated with the robot kinematics and dynamics so that the actuator input voltages are the control inputs. The presented controller is designed based on a FBFN (Fuzzy Basis Function Network) to approximate an unknown nonlinear dynamic function with the uncertainties, and a robust adaptive input to overcome the uncertainties. When the controller is designed, the different parameters for two actuator models in the actuator dynamics are taken into account. The proposed control scheme does not require the kinematic and dynamic parameters of the robot and actuators accurately. It can also alleviate the input chattering and overcome the unknown friction force. The stability of the closed-loop control system including the kinematic control system is guaranteed by using the Lyapunov stability theory and the presented adaptive laws. The validity and robustness of the proposed control scheme are shown through a computer simulation.

• 로봇학회논문지
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• 제16권4호
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• pp.327-335
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• 2021

In this paper, we propose an embedded robot system based on "ROS2 on Yocto" that can support various robots. We developed a lightweight OS based on the Yocto Project as a next-generation robot platform targeting cloud robotics. Yocto Project was adopted for portability and scalability in both software and hardware, and ROS2 was adopted and optimized considering a low specification embedded hardware system. We developed SLAM, navigation, path planning, and motion for the proposed robot system validation. For verification of software packages, we applied it to home cleaning robot and indoor delivery robot that were already commercialized by LG Electronics and verified they can do autonomous driving, obstacle recognition, and avoidance driving. Memory usage and network I/O have been improved by applying the binary launch method based on shell and mmap application as opposed to the conventional Python method. Finally, we verified the possibility of mass production and commercialization of the proposed system through performance evaluation from CPU and memory perspective.

• 로봇학회논문지
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• 제5권1호
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• pp.23-31
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• 2010

This paper suggests a multiple robot simulator which considers the uncertainties in robot motion and sensing. A mobile robot moves with errors due to some kinds of uncertainties from actuators, wheels, electrical components, environments. In addition, sensors attached to a mobile robot can't make accurate output information because of uncertainties of the sensor itself and environment. Uncertainties in robot motion and sensing leads researchers find difficulty in building mobile robot navigation algorithms. Generally, a robot algorithm without considering unexpected uncertainties fails to control its action in a real working environment and it leads to some troubles and damages. Thus, the authors propose a simulator model which includes robot motion and sensing uncertainties to help making robust algorithms. Sensor uncertainties are applied in range sensors which are widely used in mobile robot localization, obstacle detection, and map building. The paper shows performances of the proposed simulator by comparing it with a simulator without any uncertainty.

• 한국지능시스템학회논문지
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• 제15권4호
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• pp.412-417
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• 2005

오늘날 인터넷을 기반으로 하는 원격 제어 기술이 급속히 발달하고 있다. 그러나 이러한 원거리 네트워크 기반 제어는 데이터를 전송함에 있어서 지연이 불가피하며, 또한 이 지연이 일정하지 않은 문제점을 지니고 있다. 이러한 네트워크 지연은 시스템의 안정성이나 정확도에 영향을 미친다. 본 논문에서는 네트워크상의 데이터 전송 지연을 고려한 이동 로봇의 원격 운용을 위해 TSK (Takagi-Sugeno-Kang) 퍼지 시스템을 이용하여 전송 지연의 확률 분포 함수와 네트워크 모델을 구하고 이를 전송 지연 예측 알고리즘에 적용하였다. 그리고 컴퓨터 시뮬레이션으로부터 제안된 알고리즘의 실효성을 검증하고, 기존의 예측 알고리즘과의 비교분석을 통하여 그 성능을 평가하였다.

• 제어로봇시스템학회논문지
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• 제21권6호
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• pp.518-523
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• 2015

This paper proposes a gait phase classifier using a Recurrent Neural Network (RNN). Walking is a type of dynamic system, and as such it seems that the classifier made by using a general feed forward neural network structure is not appropriate. It is known that an RNN is suitable to model a dynamic system. Because the proposed RNN is simple, we use a back propagation algorithm to train the weights of the network. The input data of the RNN is the lower body's joint angles and angular velocities which are acquired by using the lower limb exoskeleton robot, ROBIN-H1. The classifier categorizes a gait cycle as two phases, swing and stance. In the experiment for performance verification, we compared the proposed method and general feed forward neural network based method and showed that the proposed method is superior.

• 대한전기학회논문지
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• 제39권12호
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• pp.1306-1316
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• 1990

A controller using a multilayer neural network is proposed to the dynamic control of a PUMA 560 robot arm. This controller is developed based on an error back-propagation (BP) neural network. Since the neural network can model an arbitrary nonlinear mapping, it is used as a commanded feedforward torque generator. A Proportional Derivative (PD) feedback controller is used in parallel with the feedforward neural network to train the system. The neural network was trained by the current state of the manipulator as well as the PD feedback error torque. No a priori knowledge on system dynamics is needed and this information is rather implicitly stored in the interconnection weights of the neural network. In another experiment, the neural network was trained with the current, past and future positions only without any use of velocity sensors. Form this thim window of position values, BP network implicitly filters out the velocity and acceleration components for each joint. Computer simulation demonstrates such powerful characteristics of the neurocontroller as adaptation to changing environments, robustness to sensor noise, and continuous performance improvement with self-learning.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1936-1939
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• 2004

This paper proposes streaming layer for personal robot's middleware. Under assumption that robot has open architecture, i.e. consists of modules created by different vendors and intercommunication between these modules is necessary, we have to consider that there are many different network interfaces. To make communication between modules possible it is necessary to develop new type of middleware. Such middleware has to support different platforms, i.e. OS, network interface, hardware, etc. In addition, it is necessary to implement effective interface between network and application in order to manage inter application communications and use network resources more effectively. Streaming layer is such interface that implements necessary functionality together with simplicity and portability. Streaming layer provides high level of abstraction and makes communication between distributed applications transparent as if are located in same module. With possibility of extension by user defined application interfaces it is suitable for distributed environments, i.e. module based architecture including small-embedded systems like as DSP board. To verify the proposed streaming layer structure it is implemented using C and tested.

• 한국정보통신학회논문지
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• 제17권1호
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• pp.8-14
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• 2013

본 논문에서는 친환경 이동 수단인 이륜 역진자 로봇을 기존의 방법보다 더욱 안정적으로 밸런싱 하기 위한 제어시스템을 구현하였다. 먼저 이륜 역진자 로봇의 제어시스템을 퍼지 제어구조로 선택하고, 적절한 소속함수 요소 값들을 지정된 3종류의 무게에 따라 시행착오적으로 구하였다. 임의의 무게에 따른 퍼지 소속 함수 요소 값을 구하기 위해 3종류의 무게에 따른 퍼지 소속함수 요소 값을 신경회로망으로 튜닝한 뒤 퍼지 제어시스템에 적용하여 보다 안정적인 제어가 가능 하도록 제어시스템을 구현하였다. 구현된 제어시스템을 실제 로봇에 적용시켜 본 결과, 기존의 퍼지 제어시스템에 비해서 본 논문에서 제안한 신경회로망으로 튜닝한 퍼지 제어시스템이 보다 우수함을 확인할 수 있었다.