Embedded Vision-Guided 3-D Tracking Control for Robotic Fish

Yu, Junzhi; Sun, Feihu; Xu, De; Tan, Min

doi:10.1109/tie.2015.2466555

Cited by 59 publications

(19 citation statements)

References 30 publications

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“…Their attractive features have aroused great interest in the research on bio-inspired robotic fish in the last decades, including propulsive mechanisms, control algorithms [1]- [3], underwater localization [4], [5], etc. In particular, dynamic modeling [6]- [12] is a valuable research topic, since dynamic models can offer guidance for almost all other research or applications related to robotic fish.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Dynamic Modeling for a Swimming Robotic Fish

Yuan

et al. 2016

IEEE Trans. Ind. Electron.

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This paper proposes a data-driven dynamic modeling method for multi-joint robotic fish with irregular geometric profiles and numerous heterogeneous hydrodynamic parameters. The method is composed of two main components: dynamic modeling and hydrodynamic parameter identification. In dynamic modeling, fluid forces exerted on the robotic fish are analyzed by the Morrison equation and the strip method. A dynamic model with an explicit formulation is derived, in which all terms involved in the dynamic analysis are converted to the coordinate system attached to the head. Further, the parameter identification technique is integrated into dynamic modeling, which reshapes it with data-driven feature and thereby makes it be competent to model swimming robots with complex geometric profiles and numerous heterogeneous hydrodynamic parameters. Experimental data of the swimming robotic fish are collected to identify the parameters directly. The obtained dynamic model is validated by data captured under extensive motion modes like forward swimming, varying velocity, and turning. Comparisons of simulated and experimental results demonstrate the effectiveness of the method.Index Terms-Modeling and control, hydrodynamic analysis, parameter identification, robotic fish. Junzhi Yu (SM'14) received the B.E. degree in safety engineering and the M.E. degree in precision instruments and mechanology from

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Section: Introductionmentioning

confidence: 99%

Data-Driven Dynamic Modeling for a Swimming Robotic Fish

Yuan

et al. 2016

IEEE Trans. Ind. Electron.

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“…The platform has been successfully applied to several typical applications, e.g., object detection and recognition. Besides, multi-tier sensor networks [4] seek to provide a lowlatency yet energy-efficient camera sensing solution. SensEye is a notable example which consists of low-power and low-resolution cameras at the bottom tier that trigger higher resolution cameras at the upper tier in an on-demand manner.…”

Section: Ivliterature Surveymentioning

confidence: 99%

Aquatic Debris Monitoring & Detection using Raspberry Pi based ‘AQUABOT’

Ghorpade¹,

R.²

2017

International Journal of Innovative Research in Electrical, Ele

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Monitoring aquatic debris is of great interest to the ecosystems, marine life, human health, and water transport. This paper presents the design and implementation of AQUABOTa vision-based surveillance robot system that integrates raspberry pi, robotic fish model along with camera and other sensors for debris monitoring in relatively calm waters. AQUABOT features real-time debris detection and coverage-based rotation scheduling algorithms. The image processing algorithms for debris detection are specifically designed to address the unique challenges in aquatic environments. The rotation scheduling algorithm provides effective coverage for sporadic debris arrivals despite camera"s limited angular view. In this paper, we focus on the design of debris detection and mobility scheduling algorithms running on a single RASP node. The sensing results of multiple nodes can be sent back to a central server via the long-range communication interface for fusion and human inspection.

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“…Zheng et al 19 presented a vision-based biomimetic fish robot, with the on-board camera to gather visual information of surrounding to play water polo-like games. The authors of literature 20 developed a visualized fish robot to navigate in the underwater environment with the aid of several visual sensing algorithms.…”

Section: Introductionmentioning

confidence: 99%

Design and development of autonomous robotic fish for object detection and tracking

Rehman

Ajwad

et al. 2020

International Journal of Advanced Robotic Systems

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In this article, an autonomous robotic fish is designed for underwater operations like object detection and tracking along with collision avoidance. The computer-aided design model for prototype robotic fish is designed using the Solid Works® software to export an stereolithography (STL) file to MakerBot, a 3D printer, to manufacture the parts of robotic fish using polylactic acid thermoplastic polymer. The precise maneuverability of the robotic fish is achieved by the propulsion of a caudal fin. The oscillation of the caudal fin is controlled by a servomotor. A combination of visual and ultrasonic sensors is used to track the position and distance of the desired object with respect to the fish and also to avoid the obstacles. The robotic fish has the ability to detect an object up to a distance of 90 cm at normal exposure conditions. A computational fluid dynamics analysis is conducted to analyze the fluid hydrodynamics (flow rate of water and pressure) around the hull of a robotic fish and the drag force acting on it. A series of experimental results have shown the effectiveness of the designed underwater robotic fish.

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Embedded Vision-Guided 3-D Tracking Control for Robotic Fish

Cited by 59 publications

References 30 publications

Data-Driven Dynamic Modeling for a Swimming Robotic Fish

Data-Driven Dynamic Modeling for a Swimming Robotic Fish

Aquatic Debris Monitoring & Detection using Raspberry Pi based ‘AQUABOT’

Design and development of autonomous robotic fish for object detection and tracking

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