State Key Laboratory for Turbulence and Complex Systems, Intelligent Biomimetic Design Lab, College of Engineering, Peking University, Beijing, 100871, People's Republic of China.
Bioinspir Biomim. 2020 May 13;15(4):046003. doi: 10.1088/1748-3190/ab810a.
In nature, the lateral line system (LLS) is a critical sensor organ of fish for rheotaxis in complex environments. Inspired by the LLS, numbers of artificial lateral line systems (ALLSs) have been designed to the fish-like robots for flow field perception, assisting the robots to be stable in the face of flow disturbances. However, almost all pressure sensor based ALLSs face the challenge of the low signal to noise ratio (SNR), resulting in inaccurate perception information. To solve this problem, this paper describes a dual-sensor fusion method by integrating the ALLSs with the inertial measurement unit (IMU), and shows the excellent performance by a higher precision and lower latency attitude holding of robotic fish. First, low-pass filtering is performed on ALLS data with low-SNR. Second, the ALLS data is mapped to the angle of attack based on an artificial neural network. Finally, a fusion perception method is established based on the time correlation between ALLS and IMU. To demonstrate the efficacy of our proposed method, we compare the result of attitude holding by three methods (dual-sensor fusion method, IMU based method, and ALLS based method). Furthermore, dual-sensor fusion method is tested at varied flow velocities and varied desired angles of attack, indicating that the algorithm can enable the robotic fish to perform dynamic movements in the incoming flow. This work provides a method for the attitude control of autonomous underwater vehicles (AUVs) by fusing the sensory data of ALLS and IMU, which is also applicable to other flow sensors and IMU.
在自然界中，侧线系统（LLS）是鱼类用于在复杂环境中进行洄游的关键传感器器官。受 LLS 的启发，已经设计出许多人工侧线系统（ALLSs）来帮助鱼类机器人进行流场感知，使机器人能够在面对流场干扰时保持稳定。然而，几乎所有基于压力传感器的 ALLSs 都面临着信噪比（SNR）低的挑战，导致感知信息不准确。为了解决这个问题，本文提出了一种通过将 ALLS 与惯性测量单元（IMU）集成的双传感器融合方法，并通过提高机器人鱼的高精度和低延迟姿态保持性能来展示其优异性能。首先，对具有低 SNR 的 ALLS 数据进行低通滤波。其次，根据人工神经网络将 ALLS 数据映射到迎角。最后，基于 ALLS 和 IMU 之间的时间相关性建立融合感知方法。为了验证我们提出的方法的有效性，我们比较了三种方法（双传感器融合方法、基于 IMU 的方法和基于 ALLS 的方法）的姿态保持结果。此外，还在不同流速和不同期望迎角下测试了双传感器融合方法，表明该算法可以使机器人鱼在来流中进行动态运动。这项工作通过融合 ALLS 和 IMU 的传感器数据为自主水下机器人（AUV）的姿态控制提供了一种方法，该方法也适用于其他流量传感器和 IMU。
