中国科学院自动化研究所机构知识库

Artificial lateral line (ALL) sensors hold the potential to enhance the perception abilities of robotic fish by capturing surface pressure gradients and identifying near-field object, such as dipole source. However, the robotic fish's free-swimming motion introduces periodic low-frequency noise into the ALL data, while dipole sources with time-varying positions generate pressure signals with complex time-frequency characteristics. This paper proposes a complete solution to these challenges that would enable freely swimming robotic fish to locate dipole source. Firstly, an ALL system consisting of pressure sensors is integrated into the robotic fish, further constructing a real-time data acquisition and processing system. Secondly, to effectively estimate and remove the swimming-induced noise from the ALL data, a noise estimation model is developed based on the bionic motion mode and unsteady Bernoulli equation. Subsequently, short-time Fourier transform is applied to the high-quality data after noise elimination, followed by developing a convolution regression neural network for feature extraction and dipole source localization. Finally, extensive simulations and experiments are conducted to validate the effectiveness of the proposed methods and perform the positive impact of the noise estimation model. Remarkably, within the range of perception, the average accuracy of dipole source location can reach 13.6 mm, providing a promising reference for improving the perception abilities of underwater robots. Note to Practitioners-This paper is motivated by the problem of blind zones in near-field perception of underwater robots. The existing perception methods as visual sensing are limited by the dark and cloudy underwater environment, and are powerless in near-field localization. In addition, the artificial lateral line, as a potential near-field sensor, is challenging to be applied in self-propelled robots due to the swimming noise. This paper proposes an integrated near-field sensory system that includes an ALL sensor, a swimming noise elimination method and a dipole source localization method. Specifically, a fish-inspired ALL sensor is designed by high-accuracy pressure sensors and integrated into the robotic fish. To enhance localization performance, a swimming noise elimination model is constructed based on unsteady Bernoulli equation. Furthermore, a convolution regression network is developed for accurate localization of near-field objects. A series of simulations and experiments demonstrate the effectiveness and superiority of the proposed near-field sensory system. Hopefully, our proposed methods can provide valuable guidance and support for near-field object localization to improve the intelligent operation ability of underwater bionic robots, such as cooperative control, underwater navigation, environment exploration, and so forth.