Levi DeVries

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

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.

Multi-vehicle Control in a Strong Floweld with Application to Hurricane Sampling

cially pertinent in the application of unmanned aircraft used for collecting targeted observations in a hurricane. The presence of such a oweld may inhibit a vehicle from making forward progress relative to a ground-xed frame, thus limiting the directions in which it can travel. Using a selfpropelled particle model in which each particle moves at constant speed relative to the ow, this paper presents results for motion coordination in a strong, known oweld. We present the particle model with respect to inertial and rotating reference frames and provide for each case a set of conditions on the oweld that ensure 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.

Observability-based optimization for flow sensing and control of an underwater vehicle in a uniform flowfield

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.

Observability-based Optimization of Coordinated Sampling Trajectories for Recursive Estimation of a Strong, Spatially Varying Flowfield

Autonomous vehicles are effective environmental sampling platforms whose sampling performance can be optimized by path-planning algorithms that drive vehicles to specific regions of the operational domain containing the most informative data. In this paper, we apply tools from nonlinear observability, nonlinear control, and Bayesian estimation to derive a multi-vehicle control algorithm that steers vehicles to an optimal sampling formation in an estimated flowfield. Sampling trajectories are optimized using the empirical observability gramian, which quantifies the sensitivity of output measurements to variations of the flowfield parameters. We reconstruct the parameters of the flowfield from noisy flow measurements collected along the sampling trajectories using a recursive Bayesian filter.

Wake Estimation and Optimal Control for Autonomous Aircraft in Formation Flight

The continued development of sophisticated aircraft with high fidelity control systems will enable autonomous execution of challenging tasks such as aerial refueling and close formation flight. In order to achieve such tasks in autonomous flight, an aircraft must sense other aircraft in close proximity and position itself relative to them. For example, aerial refueling requires the follower aircraft to intercept the filling nozzle attached to the leader aircraft; also, formation-flying aircraft must position themselves strategically to realize benefits of aerodynamic efficiency. This paper uses lifting-line theory to represent a two-aircraft formation and presents a grid-based, recursive Bayesian filter for estimating the wake parameters of the leader aircraft using noisy pressure measurements distributed along the trailing aircraft’s wing; the estimator also requires a binary, relative-altitude measurement to break the vertical symmetry. Optimal control strategies are presented to steer the follower aircraft to a desired position relative to the leader while simultaneously optimizing the observability of the leader’s relative position. The control algorithms guide the follower aircraft along trajectories that maintain adequate observability, thereby guaranteeing estimator performance. Theoretical results are illustrated using numerical examples of two-aircraft formations.

Wake Sensing and Estimation for Control of Autonomous Aircraft in Formation Flight

The continued development of sophisticated aircraft with high-fidelity control systems will enable autonomous execution of challenging tasks such as aerial refueling and close-formation flight. To achieve such tasks autonomously, an aircraft must sense other aircraft in close proximity and position itself relative to them. For example, formation-flying aircraft must position themselves strategically to realize benefits of aerodynamic efficiency; aerial refueling requires the follower aircraft to intercept the filling nozzle attached to the leader aircraft. This paper uses lifting-line theory to represent a two-aircraft formation and presents a grid-based, recursive Bayesian filter for estimating the wake parameters of the lead aircraft using noisy pressure measurements distributed along the trailing aircraft’s wing; the estimator also uses a binary, relative-altitude measurement to break the vertical symmetry. The paper employs measures of observability to quantify spatial regions prone to degraded estima...

Randomized path optimization for the mitigated counter-detection of UAVs

UAVs provide exceptional capabilities and a myriad of potential mission sets, but the ability to disguise where the aircraft takes off and lands would expansively advance the abilities of UAVs. This paper describes the development of a nonlinear estimation algorithm to predict the terminal location of an aircraft and a trajectory optimization strategy to mitigate the algorithms success. A recursive Bayesian filtering scheme is used to assimilate noisy measurements of the UAVs position to predict its terminal location. We use a blackbody radiation- based likelihood function tuned to the UAVs known endurance limitations to assimilate the position measurements. A quadratic trajectory generation method with waypoint and time variation is used to produce a parameterized family of potential aircraft trajectories. The estimation algorithm is then used to assess parameterized UAV trajectories that minimize certainty of the true terminal location. The KL divergence is used to compare the probability density of aircraft termination to a normal distribution around the true terminal location. Results show that the greatest obfuscation of path directly correlates to variations in time of flight with respect to the vehicles maximum possible flight time.

State observation and parameter estimation in cyclic pursuit systems

This paper addresses estimation of states and control parameters in cyclic pursuit systems. Classical observability tests are employed to derive conditions under which system states (i.e. agent positions) are observable in several types of cyclic pursuit schemes, including constant bearing and beacon-referenced pursuit systems. Under straightforward conditions on the system control parameters, it is demonstrated that the relative positions and control parameters of the other agents (as well as the beacon, when applicable) can be accurately estimated by an observer agent based only on direct sensing of one neighbor. Since the observer node can be viewed as an infiltrator agent, the results suggest applications in characterizing unknown members of a collective.

Kernel design for coordination of autonomous, time-varying multi-agent configurations

The coordination of agents in an autonomous system can greatly increase its ability to perform missions in a wide array of applications including distributed computing, coordination of mobile autonomous agents, and cooperative sensing. To expand the functionality of these systems to a wider array of applications, a need exists for coordinated control algorithms driving the system of nodes or agents to any prescribed state configuration in both time and space using only information passed between communicating agents. Using tools from graph theory, this paper derives a graph transformation method that maps the kernel of a graphs Laplacian matrix to any desired state configuration vector while retaining inter-agent communication characteristics of the graph. Using the transformation, this paper derives a theoretically-justified, decentralized control algorithm driving kinematic agents to any relative time-varying state configuration. Theoretical results are illustrated with numerical examples including load distribution in a computing network and surveillance of a moving target with kinematic agents.

OBSERVABILITY-BASED SAMPLING AND ESTIMATION OF FLOWFIELDS USING MULTI-SENSOR SYSTEMS

Title of dissertation: OBSERVABILITY-BASED SAMPLING AND ESTIMATION OF FLOWFIELDS USING MULTI-SENSOR SYSTEMS Levi D. DeVries, Doctor of Philosophy, 2014 Dissertation directed by: Professor Derek A. Paley Department of Aerospace Engineering The long-term goal of this research is to optimize estimation of an unknown flowfield using an autonomous multi-vehicle or multi-sensor system. The specific research objective is to provide theoretically justified, nonlinear control, estimation, and optimization techniques enabling a group of sensors to coordinate their motion to target measurements that improve observability of the surrounding environment, even when the environment is unknown. Measures of observability provide an optimization metric for multi-agent control algorithms that avoid spatial regions of the domain prone to degraded or ill-conditioned estimation performance, thereby improving closed-loop control performance when estimated quantities are used in feedback control. The control, estimation, and optimization framework is applied to three applications of multi-agent flowfield sensing including (1) environmental sampling of strong flowfields using multiple autonomous unmanned vehicles, (2) wake sensing and observability-based optimal control for two-aircraft formation flight, and (3) bio-inspired flow sensing and control of an autonomous unmanned underwater vehicle. For environmental sampling, this dissertation presents an adaptive sampling algorithm steering a multi-vehicle system to sampling formations that improve flowfield observability while simultaneously estimating the flow for use in feedback control, even in strong flows where vehicle motion is hindered. The resulting closed-loop trajectories provide more informative measurements, improving estimation performance. For formation flight, this dissertation uses lifting-line theory to represent a two-aircraft formation and derives optimal control strategies steering the follower aircraft to a desired position relative to the leader while simultaneously optimizing the observability of the leader’s relative position. The control algorithms guide the follower aircraft to a desired final position along trajectories that maintain adequate observability and avoid areas prone to estimator divergence. Toward bio-inspired flow sensing, this dissertation presents an observability-based sensor placement strategy optimizing measures of flowfield observability and derives dynamic output-feedback control algorithms autonomously steering an underwater vehicle to bio-inspired behavior using a multi-modal artificial lateral line. Beyond these applications, the broader impact of this research is a general framework for using observability to assess and optimize experimental design and nonlinear control and estimation performance. OBSERVABILITY-BASED SAMPLING AND ESTIMATION OF FLOWFIELDS USING MULTI-SENSOR SYSTEMS