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김준홍(Jun-Hong Kim),석진환(Jin-Hwan Suk),심병균(Byoung Kyun Shim),한성현(Sung-Hyun Han) 한국지능시스템학회 2010 한국지능시스템학회 학술발표 논문집 Vol.20 No.1
The equipment of industrial robot in manufacturing and assembly lines has rapidly increased. In order to achieve high productivity and flexibility, it becomes very important to develop the visual feedback control system with Off-Line Programming System(OLPS). We can save much efforts and time in adjusting robots to newly defined workcells by using OLPS. A proposed visual calibration scheme is based on position-based visual feedback. The calibration program firstly generates predicted images of objects in an assumed end-effector position. The process to generate predicted images consists of projection to screen-coordinates, visible range test, and construction of simple silhouette figures. Then, camera images acquired are compared with predicted ones for updating position and orientation data. Computation of error is very simple because the scheme is based on perspective projection, which can be also expanded to experimental results Computation time can be extremely reduced because the proposed method does not require the precise calculation of tree-dimensional object data and image Jacobian.
An Intelligent Control of Mobile Robot Base on Voice Command
김준홍(Jun-Hong Kim),Nguyen Huu Cong,석진환(Jin-Hwan Suk),심병균(Byoung Kyun Shim),한성현(Sung-Hyun Han) 한국지능시스템학회 2010 한국지능시스템학회 학술발표 논문집 Vol.20 No.1
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 describe 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. The final hypothesis is selected based on posterior probability. We then select the 떠sk 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 calculated algorithm.