State Key Laboratory for Turbulence and Complex Systems, Department of Mechanics and Engineering Science, BIC-ESAT, College of Engineering, Peking University, Beijing 100871, China.
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Sensors (Basel). 2020 Jul 21;20(14):4060. doi: 10.3390/s20144060.
Autonomous underwater missions require the construction of a stable visual sensing system. However, acquiring continuous steady image sequences is a very challenging task for bionic robotic fish due to their tight internal space and the inherent periodic disturbance caused by the tail beating. To solve this problem, this paper proposes a modified stabilization strategy that combines mechanical devices and digital image techniques to enhance the visual sensor stability and resist periodic disturbance. More specifically, an improved window function-based linear active disturbance rejection control (LADRC) was utilized for mechanical stabilization. Furthermore, a rapid algorithm with inertial measurement units (IMUs) was implemented for digital stabilization. The experiments regarding mechanical stabilization, digital stabilization, and target recognition on the experimental platform for simulating fishlike oscillations demonstrated the effectiveness of the proposed methods. The success of these experiments provides valuable insight into the construction of underwater visual sensing systems and also establishes a solid foundation for the visual applications for robotic fish in dynamic aquatic environments.
自主水下任务需要构建一个稳定的视觉感知系统。然而，由于仿生机器鱼内部空间紧凑，以及由尾部拍打产生的固有周期性干扰，获取连续稳定的图像序列是一项极具挑战性的任务。为了解决这个问题，本文提出了一种改进的稳定策略，将机械装置和数字图像技术相结合，以增强视觉传感器的稳定性并抵抗周期性干扰。具体来说，采用了一种基于改进窗口函数的线性主动干扰抑制控制（LADRC）进行机械稳定化。此外，还实现了一种基于惯性测量单元（IMU）的快速算法用于数字稳定化。在模拟鱼类摆动的实验平台上进行的机械稳定化、数字稳定化和目标识别实验验证了所提出方法的有效性。这些实验的成功为水下视觉感知系统的构建提供了有价值的见解，也为机器鱼在动态水生环境中的视觉应用奠定了坚实的基础。
