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RISS 인기검색어
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- 저자 : 하언태(Eun-Tae Ha) (검색결과 4 건)
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부품조립 및 핸들링을 위한 말단효과장치의 정밀 그리핑 제어에 관한 연구
하언태(Un-Tae Ha),성기원(Ki-Won Sung),강언욱(Eun-Wook Kang) 한국산업융합학회 2015 한국산업융합학회 논문집 Vol.18 No.3
In this paper, we propose a new precise control technology of robotic gripper for assembling and handling of part. When a robot manipulator interacts mechanically with its environment to perform tasks such as assembly or edge-finishing, the end-effector is thereby constrained by the environment. Therefore grasping force control is very important, since it increases safety due to monitoring of contact force. A comparison of various force control architecture is reported. Different force control methods can often be configured to achieve similar results for a given task, and the choice of control algorithm depends strongly on the application or on the characteristics of a particular robot. In the research, the adjustable gripping force can be controlled and improved the accuracy using the artificial intelligence techniques.
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A Study on Precise Control of Autonomous Travelling Robot Based on RVR
심병균,김종수,하언태,Shim, Byoung-Kyun,Cong, Nguyen Huu,Kim, Jong-Soo,Ha, Eun-Tae The Korean Society of Industry Convergence 2014 한국산업융합학회 논문집 Vol.17 No.2
Robust voice recognition (RVR) is essential for a robot to communicate with people. One of the main problems with RVR for robots is that robots inevitably real environment noises. The noise is captured with strong power by the microphones, because the noise sources are closed to the microphones. The signal-to-noise ratio of input voice becomes quite low. However, it is possible to estimate the noise by using information on the robot's own motions and postures, because a type of motion/gesture produces almost the same pattern of noise every time it is performed. In this paper, we propose an RVR system which can robustly recognize voice by adults and children in noisy environments. We evaluate the RVR system in a communication robot placed in a real noisy environment. Voice is captured using a wireless microphone. Navigation Strategy is shown Obstacle detection and local map, Design of Goal-seeking Behavior and Avoidance Behavior, Fuzzy Decision Maker and Lower level controller. The final hypothesis is selected based on posterior probability. We then select the task in the motion task library. In the motion control, we also integrate the obstacle avoidance control using ultrasonic sensors. Those are powerful for detecting obstacle with simple algorithm.
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이우송(Woo-Song Lee),심현석(Hyun-Seok Shim),하언태(Eun-Tae Ha),김종수(Jong-Soo Kim) 한국산업융합학회 2015 한국산업융합학회 논문집 Vol.18 No.1
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.
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A Study on Precise Control of Autonomous Travelling Robot Based on RVR
Byoung-Kyun Shim(심병균),Nguyen Huu Cong,Jong-Soo Kim(김종수),Eun-Tae Ha(하언태) 한국산업융합학회 2014 한국산업융합학회 논문집 Vol.17 No.2
Robust voice recognition (RVR) is essential for a robot to communicate with people. One of the main problems with RVR for robots is that robots inevitably real environment noises. The noise is captured with strong power by the microphones, because the noise sources are closed to the microphones. The signal-to-noise ratio of input voice becomes quite low. However, it is possible to estimate the noise by using information on the robot’s own motions and postures, because a type of motion/gesture produces almost the same pattern of noise every time it is performed. In this paper, we propose an RVR system which can robustly recognize voice by adults and children in noisy environments. We evaluate the RVR system in a communication robot placed in a real noisy environment. Voice is captured using a wireless microphone. Navigation Strategy is shown Obstacle detection and local map, Design of Goal-seeking Behavior and Avoidance Behavior, Fuzzy Decision Maker and Lower level controller. The final hypothesis is selected based on posterior probability. We then select the task in the motion task library. In the motion control, we also integrate the obstacle avoidance control using ultrasonic sensors. Those are powerful for detecting obstacle with simple algorithm.
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