Distributed flow estimation and closed-loop control of an underwater vehicle with a multi-modal artificial lateral line

Abstract

Bio-inspired sensing modalities enhance the ability of autonomous vehicles to characterize and respond to their environment. This paper concerns the lateral line of cartilaginous and bony fish, which is sensitive to fluid motion and allows fish to sense oncoming flow and the presence of walls or obstacles. The lateral line consists of two types of sensing modalities: canal neuromasts measure approximate pressure gradients, whereas superficial neuromasts measure local flow velocities. By employing an artificial lateral line, the performance of underwater sensing and navigation strategies is improved in dark, cluttered, or murky environments where traditional sensing modalities may be hindered. This paper presents estimation and control strategies enabling an airfoil-shaped unmanned underwater vehicle to assimilate measurements from a bio-inspired, multi-modal artificial lateral line and estimate flow properties for feedback control. We utilize potential flow theory to model the fluid flow past a foil in a uniform flow and in the presence of an upstream obstacle. We derive theoretically justified nonlinear estimation strategies to estimate the free stream flowspeed, angle of attack, and the relative position of an upstream obstacle. The feedback control strategy uses the estimated flow properties to execute bio-inspired behaviors including rheotaxis (the tendency of fish to orient upstream) and station-holding (the tendency of fish to position behind an upstream obstacle). A robotic prototype outfitted with a multi-modal artificial lateral line composed of ionic polymer metal composite and embedded pressure sensors experimentally demonstrates the distributed flow sensing and closed-loop control strategies.

A novel biomimetic sensor system for vibration source perception of autonomous underwater vehicles based on artificial lateral lines

“…Each neuron contains several cilia bundles, sealed in a gelatinous shell. Fluids may trigger the neurons to enable fish to identify near-field objects and water currents [15,16]. Coombs and Conley [17][18][19] found that the mottled sculpin can always locate vibrating objects.…”

Flow sensing, estimation and control for rotorcraft in ground effect

“…It is assumed henceforth that the rotorcraft has landed at the boundary condition of h/R =0.5, which is reasonable since the rotor distance above the landing gear is generally greater than 0.5R. Thrust IGE is substituted into the dynamics in (14) to obtain…”

“…The Bayesian filter [14] [19] is a probabilistic approach for estimation that assimilates noisy measurements into a probability density function (PDF) and also allows the incorporation of nonlinear system dynamics and nonlinear observation operators. (The optimal Bayesian filter for linear systems with linear measurements and Gaussian noise is the Kalman filter [20], while a common Bayesian filter for nonlinear systems with nonlinear observation and noise model is the particle filter [21].)…”

“…In the estimation step, the Bayesian filter in the form of [14] is used to estimate the vehicle height based on the flow velocity measurements from an array of differential pressure airspeed sensors. Grid-based Bayesian estimation is performed recursively, in which the finite parameter space over height h is discretized and the PDFs are evaluated on this grid for each new measurement.…”

“…The use of an aerodynamic model permits comparison to measurements from sensors such as multi-component differential pressure airspeed sensors [12]. Lagor et al [13] and DeVries et al [14] have previously shown that a reducedorder flow model can be rapidly evaluated within a Bayesian filter to perform similar estimation and control tasks in an uncertain flow environment. This paper develops a dynamic controller for hover and landing IGE based on a flow model for vehicle height estimation.…”