Abstract
The measurement accuracy of wind direction and wind speed is very important to the unmanned sailboat control, but the mature mechanical wind sensor and ultrasonic wind sensor both have great defects to be applied to the unmanned sailboat. Inspired by previous works on neural networks, we propose a low-cost, real-time, and robust wind measurement system based on computer vision (CV). This CV-wind-sensor includes an airflow rope and a camera, which can be simply deployed on the sailboat. We implement a prototype system on the FPGA platform and run a series of experiments that demonstrate the promising performance of our system. For example, the absolute measurement loss of the CV sensor in this paper is basically kept below 0.4 m/s, which shows a great advantage of measurement accuracy compared with the mechanical sensor.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bhat, K.S., Twigg, C.D., Hodgins, J.K., Khosla, P.K., Popovic, Z., Seitz, S.M.: Estimating Cloth Simulation Parameters from Video. In D. Breen and M. Lin, editors, Symposium on Computer Animation. The Eurographics Association (2003)
Cardona, J.L., Howland, M.F., Dabiri, J.O.: Seeing the wind: Visual wind speed prediction with a coupled convolutional and recurrent neural network (2019)
Camp, D.W., Turner, R.E., Gilchrist, L.P.: Response tests of cup, vane, and propeller wind sensors. J. Geophys.. Res. 75(27), 5265–5270 (1970)
Cruz, N.A., Alves, J.C.: Autonomous sailboats: An emerging technology for ocean sampling and surveillance. In: OCEANS 2008, 1–6 (2008)
Ebert, P.R., Wood, D.H.: On the dynamics of tail fins and wind vanes. J. Wind Eng. Industrial Aerodyn. 56(2–3), 137–158 (1995)
Guo, Y., Romero, M., Ieng, S., Plumet, F., Benosman, R., Gas, B.: Reactive path planning for autonomous sailboat using an omni-directional camera for obstacle detection. In: 2011 IEEE International Conference on Mechatronics, pages 445–450 (2011)
Harbola, S., Coors, V.: One dimensional convolutional neural network architectures for wind prediction. Energy Conversion Manag. 195(SEP.), 70–75 (2019)
Kang’iri, S., Gradl, C., Byiringiro, J., Ngetha, H.: Design and calibration of a 3d-printed cup-vane wireless sensor node. Designs 2(3), 21 (2018)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, (2014)
Liu, H., Mi, X., Li, Y.: Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, lstm network and elm. Energy Conversion Manag. 159(MAR.), 54–64 (2018)
Li, G., Shi, J.: On comparing three artificial neural networks for wind speed forecasting. Appl. Energy 87(7), 2313–2320 (2010)
Manley, J.E.: Unmanned surface vehicles, 15 years of development. In: OCEANS 2008, 1–4 (2008)
Meka, A., Maximov, M., Zollhoefer, M., Chatterjee, A., Seidel, H. P., Richardt, C., Theobalt, C.: Live intrinsic material estimation, Lime (2018)
Mottaghi, R., Bagherinezhad, H., Rastegari, M., Farhadi, A.: Newtonian image understanding: Unfolding the dynamics of objects in static images. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3521–3529 (2016)
Mohandes, M.A., Rehman, S., Halawani, T.O.: A neural networks approach for wind speed prediction. Renew Energy 13(3), 345–354 (2014)
Nakamura, R.: Observational studies of stable nocturnal boundary layers: intermittent turbulence, sensible heat budgets and observational errors. (2005)
Murphy, R.R., Steimle, E., Griffin, C., Cullins, C., Hall, M., Pratt, K.: Cooperative use of unmanned sea surface and micro aerial vehicles at hurricane wilma. J. Field Robot. 25(3), 164–180 (2008)
Patruno, C., Nitti, M., Stella, E., D’Orazio, T.: Helipad detection for accurate uav pose estimation by means of a visual sensor. Int. J. Adv. Robot. Syst. 14(5), 1729881417731083 (2017)
Pastore, T., Djapic, V.: Improving autonomy and control of autonomous surface vehicles in port protection and mine countermeasure scenarios. J. Field Robot. 27(6), 903–914 (2010)
Petres, C., Romero-Ramirez, M.-A., Plumet, F., Alessandrini, Bertrand: Modeling and reactive navigation of an autonomous sailboat. In: 2011 IEEE/RSJ international conference on intelligent robots and systems, pages 3571–3576. IEEE (2011)
Pindado, S., Cubas, J., Sorribes-Palmer, F..: The cup anemometer, a fundamental meteorological instrument for the wind energy industry. research at the idr/upm institute. Sensors 14(11), 21418–21452 (2014)
Plumet, F., Pêtrès, C., Romero-Ramirez, M., Gas, B., Ieng, S.: Toward an autonomous sailing boat. IEEE J. Oceanic Eng. 40(2), 397–407 (2015)
Quaranta, A.A., Aprilesi, G.C., De Cicco, G., Taroni, A.: A microprocessor based, three axes, ultrasonic anemometer. Journal of Physics E: Scientific Instruments 18(5), 384 (1985)
Runia, T.F.H., Gavrilyuk, K., Snoek, C.G.M., Smeulders, A.W.M.: Go with the flow: perception-refined physics simulation (2019)
Rynne, P.F., von Ellenrieder, K.D.: A wind and solar-powered autonomous surface vehicle for sea surface measurements. In: OCEANS 2008, 1–6 (2008)
Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. Adv. Neural Inform. Process. Syst. 1, 06 (2014)
Shi, K., Liu, M.: Strapdown inertial navigation quaternion fourth-order runge-kutta attitude algorithm. J. Detection Control 041(003), 61–65 (2019)
Spencer, L., Shah, M.: Water video analysis. In: 2004 International Conference on Image Processing, 2004. ICIP’04., volume 4, pages 2705–2708. IEEE (2004)
Steimle, E.T., Hall, M.L.: Unmanned surface vehicles as environmental monitoring and assessment tools. In: OCEANS 2006, 1–5 (2006)
Suzuki, T., Kamano, T., Harada, H.: Study on characteristics of a propeller type anemometer. Wind Engineers. JAWE 1984(22), 5–12 (1984)
Su, F., Shang, D.-z., Wang, J.-b., Liu, X.-m., Zhu, Q.: Sports Center Aquatic. A measurement method of sailing attitude based on mems gyroscope and accelerometer. J Terahertz Sci Electron Inform Technol (2):9 (2014)
Svilainis, L., Dumbrava, V.: Measurement of complex impedance of ultrasonic transducers. Ultragarsas’’ Ultrasound’’ 62(1), 26–29 (2007)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1–9 (2015)
Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., Paluri, M.: A closer look at spatiotemporal convolutions for action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June (2018)
Vodrahalli, K., Bhowmik, A.K.: 3d computer vision based on machine learning with deep neural networks: A review. Journal of the Society for Information Display 25(11), 676–694 (2017)
Wu, J., Lim, J. J., Zhang, H., Tenenbaum, J.B., Freeman, W. T.: Physics 101: Learning physical object properties from unlabeled videos. In: British Machine Vision Conference (2016)
Zhi-qian, L., Ni, W., Sui-ping, Q., Zhi-wei, Z., Jia, S., Dong-ming, W.: Research on correction method of wind measurement based on platform attitude. In: Journal of Physics: Conference Series, volume 1607, page 012073. IOP Publishing (2020)
Zhou, S., Cong, Y., Li, J., Dai, H.: Comparison of algorithms for extracting quaternion from dcm. J. Chinese Inertial Technol. 016(004), 415–418 (2008)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Yang, D., Pan, Z., Cao, Y. et al. Wind measurement by computer vision on unmanned sailboat. Int J Intell Robot Appl 5, 252–263 (2021). https://doi.org/10.1007/s41315-021-00171-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41315-021-00171-6