• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.510-518
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• 2014

In this study, in order to improve and optimize the performance of the turning mechanism for a fish robot in the fluid, we propose the tail joint trajectories using neural networks to mimic the CST(C-shape Sharp Turn) patterns of a real fish which is optimized in the natural environment. In order to mimic the CST patterns of a fish, we convert the sequential recording CST patterns into the coordinate data, and change the numerical coordinate data into a functions. We change the motion functions to the relative joint angles which is adapted to suit robot's shape and data. However, these relative joint trajectories obtained by the sequential recording of the carp have low-precision. It is difficult to apply to the control of a fish robot. Therefore, the relative joint trajectories are interpolated using neural networks with superior generalization ability and applied to the fish robot. we have found that the proposed method using neural networks is superior to ones using high-order polynomial equation through the computer simulations.

• Journal of the Korean Society for Precision Engineering
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• v.33 no.7
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• pp.571-577
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• 2016

In this paper, we propose a novel propulsion method for a Biomimetic underwater robot, which is a bio-inspired approach. The proposed propulsion method mimics the pectoral fins of a real fish. Pectoral fins of real fish are able to propel and change direction. We designed the propulsion mechanism of 1 D.O.F. that has two functions (propel and change direction). We named this propulsion system 'Flipper'. The proposed propulsion method can control forward, pitch and yaw motion using the Flipper. We made an experimental underwater robot system and verified the proposed propulsion method. We measured its maximum speed and turning motion using an experimental underwater robot system. We also analyzed the thrust force from the maximum speed, using the thrust equation. Experimental results showed that our propulsion method enabled the thrust system of the biomimetic robot.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.168.4-168
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• 2001

A great many species of fish congregate in schools, reducing the risk of being eaten by predators and giving one of the considerable survival advantages for fishes. Such a fish school is self-organized only due to individual behaviors for matching the speed and direction with the neighboring fishes. It is interesting to simulate these fish school by small robots, because we can understand how the group structure emerges from the interaction among neighboring individuals. We use a nice simulator for Khepera robot presented by Oliver Michel. It is shown that the Khepera simulator is easily applied to fish school due to the algorithm introduced by I Aoki. The simulator includes sensor noise so appropriately that the simulator can be transferred easily to the real environment. The results of simulation are given as follows: (1) The stability as a group is shown by plotting mean deviations from the center of group ...

• The Journal of Korea Robotics Society
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• v.5 no.3
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• pp.197-208
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• 2010

The ostraciiform swimming mode allows the simplest mechanical design and control for underwater vehicle swimming. Propulsion is achieved via the flapping of caudal fin without the body undulatory motion. In this research, the propulsion of underwater vehicles by ostraciiform swimming mode is explored experimentally using an ostraciiform fish robot and some rigid caudal fins. The effects of caudal fin flapping frequency and amplitude on the cruising performance are studied in particular. A theoretical model of propulsion using rigid caudal fin is proposed and identified with the experimental data. An experimental method to obtain the drag coefficient and the added mass of the fish robot is also proposed.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.8
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• pp.868-874
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• 2014

This paper proposes a novel mapping algorithm in Omni-directional Vision SLAM based on an obstacle's feature extraction using Lucas-Kanade Optical Flow motion detection and images obtained through fish-eye lenses mounted on robots. Omni-directional image sensors have distortion problems because they use a fish-eye lens or mirror, but it is possible in real time image processing for mobile robots because it measured all information around the robot at one time. In previous Omni-Directional Vision SLAM research, feature points in corrected fisheye images were used but the proposed algorithm corrected only the feature point of the obstacle. We obtained faster processing than previous systems through this process. The core of the proposed algorithm may be summarized as follows: First, we capture instantaneous $360^{\circ}$ panoramic images around a robot through fish-eye lenses which are mounted in the bottom direction. Second, we remove the feature points of the floor surface using a histogram filter, and label the candidates of the obstacle extracted. Third, we estimate the location of obstacles based on motion vectors using LKOF. Finally, it estimates the robot position using an Extended Kalman Filter based on the obstacle position obtained by LKOF and creates a map. We will confirm the reliability of the mapping algorithm using motion estimation based on fisheye images through the comparison between maps obtained using the proposed algorithm and real maps.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.1
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• pp.70-77
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• 2014

This paper proposes a novel localization algorithm based on ego-motion which used Lucas-Kanade Optical Flow and warping image obtained through fish-eye lenses mounted on the robots. The omnidirectional image sensor is a desirable sensor for real-time view-based recognition of a robot because the all information around the robot can be obtained simultaneously. The preprocessing (distortion correction, image merge, etc.) of the omnidirectional image which obtained by camera using reflect in mirror or by connection of multiple camera images is essential because it is difficult to obtain information from the original image. The core of the proposed algorithm may be summarized as follows: First, we capture instantaneous $360^{\circ}$ panoramic images around a robot through fish-eye lenses which are mounted in the bottom direction. Second, we extract motion vectors using Lucas-Kanade Optical Flow in preprocessed image. Third, we estimate the robot position and angle using ego-motion method which used direction of vector and vanishing point obtained by RANSAC. We confirmed the reliability of localization algorithm using ego-motion based on fisheye warping image through comparison between results (position and angle) of the experiment obtained using the proposed algorithm and results of the experiment measured from Global Vision Localization System.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.3
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• pp.210-216
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• 2016

This paper presents a novel collision avoidance technique for mobile robots based on omni-directional vision simultaneous localization and mapping (SLAM). This method estimates the avoidance path and speed of a robot from the location of an obstacle, which can be detected using the Lucas-Kanade Optical Flow in images obtained through fish-eye cameras mounted on the robots. The conventional methods suggest avoidance paths by constructing an arbitrary force field around the obstacle found in the complete map obtained through the SLAM. Robots can also avoid obstacles by using the speed command based on the robot modeling and curved movement path of the robot. The recent research has been improved by optimizing the algorithm for the actual robot. However, research related to a robot using omni-directional vision SLAM to acquire around information at once has been comparatively less studied. The robot with the proposed algorithm avoids obstacles according to the estimated avoidance path based on the map obtained through an omni-directional vision SLAM using a fisheye image, and returns to the original path. In particular, it avoids the obstacles with various speed and direction using acceleration components based on motion information obtained by analyzing around the obstacles. The experimental results confirm the reliability of an avoidance algorithm through comparison between position obtained by the proposed algorithm and the real position collected while avoiding the obstacles.

• Journal of computational fluids engineering
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• v.16 no.2
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• pp.24-29
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• 2011

Immersed boundary lattice Boltzmann method has been applied to analyze the characteristics of the self-propelled fish motion swimming robot. The airfoil NACA0012 with caudal fin stroke model was considered to examine the characteristics. The foil in steady forward motion and a combination of steady-state harmonic deformation produces thrust through the formation of a flow downstream from the trailing edge. The harmonic motion of the foil causes unsteady shedding of vorticity from the trailing edge, while forming the vortices at the leading edge as well. The resultant thrust is developed by the pressure difference formed on the upper and lower surface of the airfoil. and the time averaged thrust coefficient increases as Re increase in the region of $Re{\leqq}700$. The suggested numerical method is suitable to develop the fish-motion model to control the swimming robot, however It would need to extend in 3D analysis to examine the higher Re and to determine the more detail mechanism of thrust production.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.316-321
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• 2005

We designed and implemented fish robots for various purposes such as autonomous navigation, maneuverability control, posture balancing and improvement of quick turns in a tank of 120 X 120 X 180cm size. Typically, fish robots have 30-50 X 15-25 X 10-20cm dimensions; length, width and height, respectively. It is essential to have the ability of quick and smooth turning to avoid collision with obstacles or walls of the water pool at a close distance. Infrared distance sensors are used to detect obstacles, magneto-resistive sensors are used to read direction information, and a two-axis accelerometer is mounted to compensate output of direction sensors. Because of the swing action of its head due to the tail fin movement, the outputs of an infrared distance sensor contain a huge amount of noise around true distances. With the information from accelerometers and e-compass, much improved distance data can be obtained by fuzzy logic based estimation. Successful swimming and smooth turns without collision demonstrated the effectiveness of the distance estimation.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.855-858
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• 2003

The trends of recent developed a pet robot which interacts with people are increased gradually. There are a few pet robots that are a robot dog, robot cat, and robot fish. The pet robot is featured that it is possible to sympathize and give pleasure to human. The pet robots express delight, sorrow, surprise, and hunger through the artificial intelligence. Previously, the pet robot has to exchange the battery when it is exhausted. Commercialized robots have a self-recharging function, which express hunger. Robot dog AIBO, SONY in Japan, checks the battery for expressing hunger. They find an energy station for recharge. While operation time of AIBO is 1 hour 30 minutes, recharging time is 2 hours. Recharging time is longer than operation time. During the recharge, they don't operate. We obtain a motivation for eating the battery when find the problem. In this paper, introduce an Autonomous Eating Pet Robot and propose a design for realization. The Autonomous Eating Pet Robot has a function that is the most basic instinct that is finding a food and evacuating.