Target tracking of robotic fish based on embedded vision and CPG model

Sun, Feihu; Yu, Junzhi; Xu, De; Wang, Ming

doi:10.1109/icinfa.2013.6720478

Cited by 3 publications

(5 citation statements)

References 17 publications

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“…4) Exclude pseudo-parallelograms. The non-target parallelograms are excluded according to the mean value of Cr, while the mirror effect [21] is eliminated in virtue of position relationship.…”

Section: B Shape-based Recognitionmentioning

confidence: 99%

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Active visual tracking of free-swimming robotic fish based on automatic recognition

Sun

Chen

et al. 2014

Proceeding of the 11th World Congress on Intelligent Control and Automation

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Implementing automatic visual tracking for underwater robots often remains a great challenge. In this paper, we demonstrate an active visual tracking case with a free-swimming robotic fish by use of a combination of TI DM3730-centered embedded hardware and software. Specifically, automatic recognition algorithms mainly involving color-based identification and shape-based identification are proposed. In parallel with the enhanced target recognition, an improved real-time location method based on the weighed histogram representation of the target model that synthesizes the CAMSHIFT algorithm and the Kalman filter, is put forward. After that, a fuzzy logic controller based on the CPG model is employed to generate control signals related to the desired target. Aquatic experiments validate the effectiveness of the proposed algorithms.

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Section: B Shape-based Recognitionmentioning

confidence: 99%

“…The rule base is designed on the basis of experience, while the center-of-gravity defuzzification method is employed to calculate the output. The specific details are described in [21].…”

Section: Motion Controlmentioning

confidence: 99%

Active visual tracking of free-swimming robotic fish based on automatic recognition

Sun

Chen

et al. 2014

Proceeding of the 11th World Congress on Intelligent Control and Automation

Self Cite

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“…The standard trigonometric functions are selected as the membership functions; the ruse base is designed on experience; and the center-of-gravity defuzzification method is utilized. The specific methods are depicted in [20]. We remark that, on one hand, the robotic fish is designed to achieve efficient and agile locomotion.…”

Section: Design Of Multistage Directional Controlmentioning

confidence: 99%

“…Based on our previous work on fishlike propulsion and maneuvering [18]- [20], this paper focuses on design and implementation of 3-D tracking control for a free-swimming robotic fish with onboard embedded vision. An overall 3-D tracking control framework is first proposed.…”

Section: Introductionmentioning

confidence: 99%

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

Sun

et al. 2016

IEEE Trans. Ind. Electron.

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Abstract-Visual tracking of free-swimming robotic fish remains a great challenge by considering imaging qualities in aquatic environments. In this paper, we propose a visual identification and positioning method to obtain accurate position for 3-D tracking control comprising depth control and directional control. Specifically, a depth control method based on fuzzy sliding-mode control is put forward to make the robotic fish swim to the preferred target depth and remain at that depth. A directional control method with multiple stages is proposed, and a series of control strategies is developed to integrate agile locomotion and control accuracy. Experiments on depth control and 3-D tracking control are finally conducted in an indoor pool. The latest results obtained indicate that the proposed algorithms are effective and feasible, which lays a solid foundation for complex underwater task execution.Index Terms-Motion control, robotic fish, threedimensional tracking control, visual identification and positioning.

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“…Many studies focus on this kind of robotic fish [19][20][21] as it is expected that the carangiform robotic fish would have a more powerful motion capability than an anguilliform one. Some research works are relevant to CPG-based swimming control [22][23][24]. An ostraciiform robotic fish swims only using the tail swing without the use of the body swing.…”

Section: Mechatronic Designmentioning

confidence: 99%

CPG-based Sensory Feedback Control for Bio-inspired Multimodal Swimming

Wang

Tan

2014

International Journal of Advanced Robotic Systems

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Sensory feedback plays a very significant role in the generation of diverse and stable movements for animals. In this paper we describe our effort to develop a Central Pattern Generator (CPG)-based sensory feedback control for the creation of multimodal swimming for a multi-articulated robotic fish in the context of neurocomputing. The proposed control strategy is composed of two phases: the upper decision-making and the automatic adjustment. According to the upper control commands and the sensory inputs, different swimming gaits are determined by a finite state machine algorithm. At the same time, the sensory feedback is exploited to shape the CPG coupling forms and control parameters. In the automatic adjustment phase, the CPG model with sensory feedback will adapt the environment autonomously. Simulation and underwater tests are further conducted to verify the presented control scheme. It is found that the CPG-based sensory feedback control method can effectively improve the manoeuvrability and adaptability of the robotic fish in water.

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Target tracking of robotic fish based on embedded vision and CPG model

Cited by 3 publications

References 17 publications

Active visual tracking of free-swimming robotic fish based on automatic recognition

Active visual tracking of free-swimming robotic fish based on automatic recognition

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

CPG-based Sensory Feedback Control for Bio-inspired Multimodal Swimming

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