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学者姓名：闫帅铮
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Abstract ：
This article proposes a tightly coupled visual-acoustic sensor fusion method for self-localization of a biomimetic robotic shark. To address the decreased localization accuracy of visual-based simultaneous localization and mapping systems employed on a robotic fish in underwater environments, we integrate velocity measurements from the acoustic sensor Doppler velocity log (DVL) into a visual odometry. To fully exploit the local position change information contained in velocity measurements, DVL measurements are fused in two stages of visual tracking. Specifically, we first employ the velocity measurements to improve the initial camera pose estimation during visual tracking, aiming to provide a better initial value for subsequent pose optimization. Thereafter, these velocity measurements are directly employed to constrain the camera position change between two adjacent frames by constructing a DVL residual term, which is optimized jointly with the visual residual to obtain a more accurate camera pose. Extensive experiments are conducted on both self-collected simulated datasets and real-world underwater datasets. Experimental results demonstrate that the proposed visual-acoustic fusion method can effectively improve the localization accuracy for the robotic shark by more than 50% compared to a pure visual system, providing valuable guidance for improving the autonomous localization capability of underwater biomimetic robots.
Keyword ：
Robotic fish Robotic fish sensor fusion sensor fusion simultaneous localization and mapping (SLAM) simultaneous localization and mapping (SLAM) underwater self-localization underwater self-localization
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| GB/T 7714 | Huang, Yupei , Li, Peng , Yan, Shuaizheng et al. Self-Localization of a Biomimetic Robotic Shark Using Tightly Coupled Visual-Acoustic Fusion [J]. | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2024 . |
| MLA | Huang, Yupei et al. "Self-Localization of a Biomimetic Robotic Shark Using Tightly Coupled Visual-Acoustic Fusion" . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2024) . |
| APA | Huang, Yupei , Li, Peng , Yan, Shuaizheng , Tan, Min , Yu, Junzhi , Wu, Zhengxing . Self-Localization of a Biomimetic Robotic Shark Using Tightly Coupled Visual-Acoustic Fusion . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2024 . |
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Abstract ：
In nature, the hammerhead shark possesses a special rolled swimming motion to combat the effects of inborn negative buoyancy. Inspired by this natural mechanism, we developed a novel biomimetic robotic hammerhead shark to explore the distinctive rolled swimming motion mode. First, a scaled-down robotic prototype is constructed based on the morphological characteristics of the hammerhead shark. Second, the kinematics and dynamics of fish-like swimming are built, thereafter, model identification and validation are performed to improve the accuracy of the robotic model. Furthermore, the physical effects of the long dorsal fin and the rolling state on swimming performance are investigated indepth by numerically simulating the lift and drag forces over different fin surfaces and the dynamic torque of the body. Finally, extensive aquatic experiments demonstrate the remarkable improvements on locomotion performance and propulsive efficiency of the robotic hammerhead shark by the proposed rolled motion. The obtained results provide a new solution for the long voyage of high-load robotic fish system.
Keyword ：
Biomimetic locomotion Biomimetic locomotion hammerhead shark hammerhead shark robotic dynamics robotic dynamics robotic fish robotic fish
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| GB/T 7714 | Yan, Shuaizheng , Wu, Zhengxing , Wang, Jian et al. Towards Unusual Rolled Swimming Motion of a Bioinspired Robotic Hammerhead Shark Under Negative Buoyancy [J]. | IEEE-ASME TRANSACTIONS ON MECHATRONICS , 2023 , 29 (3) : 2253-2265 . |
| MLA | Yan, Shuaizheng et al. "Towards Unusual Rolled Swimming Motion of a Bioinspired Robotic Hammerhead Shark Under Negative Buoyancy" . | IEEE-ASME TRANSACTIONS ON MECHATRONICS 29 . 3 (2023) : 2253-2265 . |
| APA | Yan, Shuaizheng , Wu, Zhengxing , Wang, Jian , Li, Sijie , Tan, Min , Yu, Junzhi . Towards Unusual Rolled Swimming Motion of a Bioinspired Robotic Hammerhead Shark Under Negative Buoyancy . | IEEE-ASME TRANSACTIONS ON MECHATRONICS , 2023 , 29 (3) , 2253-2265 . |
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Robust vision restoration of underwater images remains a challenge. Owing to the lack of well-matched underwater and in-air images, unsupervised methods based on the cyclic generative adversarial framework have been widely investigated in recent years. However, when using an end-to-end unsupervised approach with only unpaired image data, mode collapse could occur, and the color correction of the restored images is usually poor. In this paper, we propose a data- and physics-driven unsupervised architecture to perform underwater image restoration from unpaired underwater and in-air images. For effective color correction and quality enhancement, an underwater image degeneration model must be explicitly constructed based on the optically unambiguous physics law. Thus, we employ the Jaffe-McGlamery degeneration theory to design a generator and use neural networks to model the process of underwater visual degeneration. Furthermore, we impose physical constraints on the scene depth and degeneration factors for backscattering estimation to avoid the vanishing gradient problem during the training of the hybrid physical-neural model. Experimental results show that the proposed method can be used to perform high-quality restoration of unconstrained underwater images without supervision. On multiple benchmarks, the proposed method outperforms several state-of-the-art supervised and unsupervised approaches. We demonstrate that our method yields encouraging results in real-world applications.
Keyword ：
style transfer style transfer Underwater image restoration Underwater image restoration unsupervised learning unsupervised learning
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| GB/T 7714 | Yan, Shuaizheng , Chen, Xingyu , Wu, Zhengxing et al. HybrUR: A Hybrid Physical-Neural Solution for Unsupervised Underwater Image Restoration [J]. | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2023 , 32 : 5004-5016 . |
| MLA | Yan, Shuaizheng et al. "HybrUR: A Hybrid Physical-Neural Solution for Unsupervised Underwater Image Restoration" . | IEEE TRANSACTIONS ON IMAGE PROCESSING 32 (2023) : 5004-5016 . |
| APA | Yan, Shuaizheng , Chen, Xingyu , Wu, Zhengxing , Tan, Min , Yu, Junzhi . HybrUR: A Hybrid Physical-Neural Solution for Unsupervised Underwater Image Restoration . | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2023 , 32 , 5004-5016 . |
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