MrDMD-Based Sensor Placement in Distributed Flow Estimation for the Design of the Artificial Lateral Line of an Underwater Robot

An artificial lateral line (ALL) is a sensing system that imitates the distributed perception organs of fish and plays a major role in enhancing the flow estimation capability of underwater robots. Whereas various ALLs have been designed and developed, it is still an open question how to better place ALL sensors on underwater robots, especially for those with complex shapes and working in dynamic flow and robot operating conditions. Aiming to answer this question, this paper presents a novel data-driven sensor placement method for ALLs of underwater robots. This method adopts distributed pressure sensors to measure the flow field along the profile or the outermost boundary of an underwater robot, and quantifies the dynamic information embedded within these measurements using multi-resolution dynamic mode decomposition (mrDMD). The sensors are then positioned by optimizing the dynamic flow information to enhance the perception. Compared with existing sensor placement methods, such as observability maximization and exhaustive experimental search, the proposed method focuses on the modes of dynamics variability at various spatio-temporal scales, thus leading to improved sensing ability especially in complex and dynamic flows. In addition, comprehensively considering the sensor placement under different flow and robot operating conditions, the proposed method is expected to provide an optimal solution for the overall sensing performance of the ALL system. To demonstrate the effectiveness of the proposed method, a case study of background flow speed estimation of oscillating underwater robots of different shapes in a uniform flow is presented.