Design of aquarium robot world using detecting fish robot position method

Shin, Kyoojae; Musunuri, Yogendra Rao

doi:10.1109/elinfocom.2016.7562987

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…To accomplish this, the background should be white to avoid noise and achieve the optimal extraction of the target object. (13) (b) Extraction of the region of interest (ROI): The following classical method is used to extract the target object or frame from video after capturing. The relevant frames are converted to grayscale and the Gaussian blur process is applied to delete noise.…”

Section: Hand Segmentationmentioning

confidence: 99%

Human and Robotic Fish Interaction Controlled Using Hand Gesture Image Processing

Angani¹,

Lee²,

Talluri³

et al. 2020

Sensors and Materials

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This paper is about the control of robotic fish movement in an aquarium via human hand gestures detected by image sensors attached in the aquarium. In this study, sensors actively interact with humans and robotic fish. Image and radio frequency sensors are used to identify the position and color of robotic fish. Recently, we have studied human interactive control based on hand gesture recognition. Image sensors send the input signals of hand gestures obtained from real-time video images processed using tracking control algorithms, such as color mark, stop zone, and lead-lag tracking algorithms, to robotic fish. The movement of robotic fish is controlled via the movement of the two hands, where the left hand is for the fish to be controlled and the right hand is for controlling the movement of the robotic fish. Hand gesture recognition consists of hand feature segmentation and gesture recognition from the hand features. Our results show that interactive human control using hand gestures successfully controls the movement of robotic fish.

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Section: Hand Segmentationmentioning

confidence: 99%

Human and Robotic Fish Interaction Controlled Using Hand Gesture Image Processing

Angani¹,

Lee²,

Talluri³

et al. 2020

Sensors and Materials

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show abstract

“…(4), u and v are respectively the horizontal and vertical directions of the optical flow, which are also known as motion vectors. (14)(15)(16)(17) To compute the motion vectors, the optical flow limitation equation [Eq. (5)] is used with the Lucas-Kanade method.…”

Section: Optical Flow Algorithmmentioning

confidence: 99%

Lead-lag Swimming Control of Robot Fish Using Color Detection Algorithm

Angani¹,

Kang²,

Talluri³

et al. 2020

Sensors and Materials

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This paper is about lead-lag robot fish swimming control by image processing. In robot fish aquariums, fish can generally move randomly in any path. This irregular movement may cause collisions between the fish, damaging them. Hence, an effective swimming control method is necessary. We thus proposed a lead-lag swimming control system for robot fish. Here, we simply study the detection of moving objects in an aquarium because we need to find the positions of moving robot fish. The locations of the robot fish are recognized using an image processing technique employing image and position sensors. This method is used to obtain the velocity for each pixel in an image and assumes a constant velocity in each video frame to obtain the positions of robot fish by comparing consecutive video frames. By using the position data, we compute the distance between robot fish and determine which robot fish is the lead fish and which fish is the lagging fish. The lead fish then waits for the lagging fish to catch up. The results of this proposed system are satisfactory in preventing collisions between robot fish. This system is exhibited in Busan Science Museum in South Korea.

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Realization of aquarium robot holographic world using 3 axes tracking optical flow detecting method

Shin

Musunuri

2016

2016 Future Technologies Conference (FTC)

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Design of aquarium robot world using detecting fish robot position method

Cited by 3 publications

References 4 publications

Human and Robotic Fish Interaction Controlled Using Hand Gesture Image Processing

Human and Robotic Fish Interaction Controlled Using Hand Gesture Image Processing

Lead-lag Swimming Control of Robot Fish Using Color Detection Algorithm

Realization of aquarium robot holographic world using 3 axes tracking optical flow detecting method

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