A lateral line system, consisting of arrays of flow-sensing neuromasts, allows fish and amphibians to probe their ambient environment and plays a vital role in their behaviors spanning predator/prey detection, schooling, rheotaxis, courtship and communication. The feats of biological lateral lines have inspired an increasing interest in engineering a similar sensing module for underwater robots and vehicles. Often known as artificial lateral lines, these sensors could potentially enable an underwater robot to detect and identify moving or stationary objects, and exploit ambient flow energy for efficient locomotion. Despite the advances made in this area, realizing a practical artificial lateral line still faces significant challenges in both signal processing and flow sensor fabrication.In this dissertation we describe our effort in developing signal processing methods for hydrodynamic object localization and tracking using an artificial lateral line (ALL). We consider two types of objects, a vibrating sphere and a non-vibrating cylinder, both of which are of interest in underwater applications. A vibrating sphere, known as a dipole source, is widely used in the study of biological lateral lines and it emulates the rhythmic body or fin movement. A non-vibrating cylinder (with unknown cross-section shape), on the other hand, represents a general moving or stationary object underwater with a 2D flow profile. First, a novel bio-inspired artificial lateral line system is proposed for underwater robots and vehicles by exploiting the inherent sensing capability of ionic polymer-metal composites (IPMCs). Analogous to its biological counterpart, the IPMC-based lateral line processes the sensor signals through a neural network, and we demonstrate the performance of this approach in the localization of a dipole source. Second, with an assumption of potential flows, we formulate nonlinear estimation problems for the localization and tracking of a dipole source based on analytical flow models, and propose and compare several algorithms for solving the problem. For the case of a moving cylinder, we use conformal mapping to represent a general cross-section profile, and explore the use of Kalman-filtering-type techniques in the tracking and size/shape estimation of the object.We have conducted extensive experiments to validate the developed algorithms with an artificial lateral line prototype made of millimeter-scale IPMC sensors, with sensor-to-sensor separation of 2 cm, which is determined through an optimization process based on the Cramer-Rao bound (CRB) analysis. Finally, we experimentally explore the use of IPMC sensors for estimating the hydrodynamic parameters involved in a Karman vortex street that is created by a stationary cylinder in a flow. We validate that the vortex shedding frequency, which can be extracted from the sensor signal, shows clear correlation with the flow speed and the obstacle size.
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