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.

Abstract-This paper describes how an underwater vehicle can control its motion by sensing the surrounding flowfield and using the sensor measurements in a dynamic feedback controller. Limitations in existing sensing modalities for flowfield estimation are mitigated by using a fish-inspired distributed sensor array and a nonlinear observer. Estimation performance is further increased by optimizing sensor placement on the vehicle body. We optimize sensor placement along a streamlined body using measures of flowfield observability, namely the empirical observability gramian. Velocity potentials model the flow around the vehicle and a recursive Bayesian filter estimates the flow from noisy velocity measurements. To orient the body into the oncoming flow (a fish-inspired behavior known as rheotaxis) we implement a dynamic, linear controller that uses the estimated angle of attack. Numerical simulations illustrate the theoretical results.

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Autonomous underwater vehicles (AUVs) have shown great promise in fulfilling surveillance, scavenging, and monitoring tasks, but can be hindered in expansive, cluttered or obstacle ridden environments. Traditional gliders and streamlined AUVs are designed for long term operational efficiency in expansive environments, but are hindered in cluttered spaces due to their shape and control authority; agile AUVs can penetrate cluttered or sensitive environments but are limited in operational endurance at large spatial scales. This paper presents the prototype testbed design, modeling, and experimental hydrodynamic drag characterization of a novel self-propelled underwater vehicle capable of actuating its shape morphology. The vehicle prototype incorporates flexible, buckled fiberglass ribs to ensure a rigid shape that can be actuated by modulating the length of the semi-major axis. Tools from generative modeling are used to represent the vehicle shape by using a single control input actuating the vehicles length-to-diameter ratio. By actuating the length and width characteristics of the vehicle’s shape to produce a desired drag profile, we derive the feasible speeds achievable by shape actuation control. Tow-tank experiments with an experimental proto-type suggest shape actuation can be used to manipulate the drag by a factor between 2.15 and 5.8 depending on the vehicle’s operating speed.

A major obstacle to path-planning and formation-control algorithms in multi-vehicle systems are strong flows in which the flow speed is greater than the vehicle speed relative to the flow. This challenge is especially pertinent in the application of unmanned aircraft used for collecting targeted observations in a hurricane. The presence of such a flowfield may inhibit a vehicle from making forward progress relative to a ground-fixed frame, thus limiting the directions in which it can travel. This paper presents results for motion coordination in a strong, known flowfield using a selfpropelled particle model of vehicle motion in which each particle moves at constant speed relative to the flow. A new formulation of the particle model with respect to a rotating reference frame is provided along with a set of conditions on the flowfield that ensures trajectory feasibility. Results from the Lyapunov-based design of decentralized control algorithms are presented for circular, folium, and spirograph trajectories, which are selected for their potential use as hurricane sampling trajectories. The theoretical results are illustrated using numerical simulations in an idealized hurricane model. Nomenclature N Number of particles in the system k Particle index k = 1,. .. , N r k/O Position of particle k with respect to O