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Andy Stewart

Senior Engineer and Associate Director, NNMREC

Affiliate Assistant Professor

Email

andy@apl.washington.edu

Phone

206-221-8015

Biosketch

Andrew Stewart's research supports the development of next-generation ocean science technology and the creation of new tools to advance capabilities and maintain strategic advantage for the U.S. Navy. His interests include vehicles, marine renewable energy technologies, remote sensing, and robotics. Through employing design methodologies rooted in fundamental principles, Stewart contributes to all phases of project development from conceptual design to fabrication, testing, and deployment. In addition to conducting federally-funded research, Dr. Stewart is actively commercializing technology developed within the Laboratory and frequently collaborates with industry to aid transition. In 2014 Dr. Stewart joined the executive committee of the Northwest National Marine Renewable Energy Center as Associate Director.

Department Affiliation

Ocean Engineering

Education

B.S. Mechanical Engineering, University of California San Diego, 2006

M.A. Dynamics & Control Theory, Princeton University, 2008

Ph.D. Mechanical & Aerospace Engineering, Princeton University, 2012

Videos

BluHaptics: Intuitive Control for Marine Technology Applications

Haptic technologies — providing tactile feedback to the operator — developed to guide robotic surgeries are now being applied to underwater robotic platforms. The goal is safer and more efficient ROV operations.

24 Feb 2014

Unmanned Air System — UAS

The APL-UW unmanned air system (UAS) testbed is a collaborative project involving researchers specializing in autonomy, remote sensing, and ocean science instrumentation. This effort represents the beginning of a research program to extend our expertise into the aerial domain.

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29 Jan 2014

The overarching goal of this project is to develop new opportunities for basic and applied research. The work focuses not only on UAS technology (marine applications in particular) but also on the development of tools that will transform how research is conducted at sea. Recent tests with the prototype system have verified stability and key performance characteristics, with flight duration exceeding 20 mins and a top speed exceeding 30 knots.

Propagating Undersea Vehicle Expertise

APL-UW scientists and engineers mentor the UW ROV Team. The underwater robotics program at the University of Washington provides a dynamic learning environment for oceanography and engineering students to design, build, and operate an underwater remotely operated vehicle (ROV) from scratch.

21 Mar 2013

Publications

2000-present and while at APL-UW

Advanced telerobotic underwater manipulation using virtual fixtures and haptic rendering

Ryden, F., A. Stewart, and H.J. Chizeck, "Advanced telerobotic underwater manipulation using virtual fixtures and haptic rendering," Proc., Oceans, 23-27 September, San Diego, 8 pp (IEEE, 2013).

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23 Sep 2013

As the variety and complexity of tasks carried about by telerobotic systems underwater increases, so does the demand for technology that increases efficiency and safety of operations. We present a novel approach to subsea manipulation that leverages the natural perceptive capability of human pilots. We present a method and framework for generation and rendering of six degree-of-freedom haptic virtual fixtures based on streaming point cloud data captured by an RGB-D sensor. Sensor data is captured at 30 Hz and virtual fixtures are constructed in real-time in parallel on a GPU. The method is applied to two typical underwater telerobotic valve operations by implementing a forbidden-region virtual fixture and a guidance virtual fixture. During operation, real-time haptic feedback is sent to the operator at 1000 Hz. This haptic feedback allows pilots of remotely-operated vehicles to receive sense of touch feedback through the use of non-contact sensors.

Development of an adaptable monitoring package for marine renewable energy

Joslin, J., E. Celkis, C. Roper, A. Stewart, and B. Polagye, "Development of an adaptable monitoring package for marine renewable energy," Proc., Oceans, 23-27 September, San Diego, 8 pp (IEEE, 2013).

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23 Sep 2013

The Adaptable Monitoring Package (AMP) along with a Remotely Operated Vehicle (ROV) and custom tool skid, is being developed to support near-field (󖼚 meters) and long-range monitoring of hydrokinetic energy converters. The goal for the AMP is to develop a system capable of supporting a wide range of environmental monitoring in harsh oceanographic conditions, at a cost in line with other aspects of technology demonstrations. This paper presents a system description of all related infrastructure for the AMP, including supported instrumentation, deployment ROV and tool skid, launch platform, and docking station. Design requirements are driven by the monitoring instrumentation and the strong waves and currents that typify marine renewable energy sites. Hydrodynamic conditions from the Pacific Marine Energy Centers wave test sites and Admiralty Inlet, Puget Sound, Washington are considered in the design as early adoption case studies. A methodology is presented to increase the capabilities to deploy and operate the AMP in strong currents by augmenting thrust and optimizing the system drag profile through computational fluid dynamic modeling. Preliminary results suggest that the AMP should be deployable in turbulent environments with mean flow velocities up to 1 m/s.

Towards human–robot teams: Model-based analysis of human decision making in two-alternative choice tasks with social feedback

Stewart, A., M. Cao, A. Nedic, D. Tomlin, and N. Leonard, "Towards human–robot teams: Model-based analysis of human decision making in two-alternative choice tasks with social feedback," Proc. IEEE, 100, 751-775, doi:10.1109/JPROC.2011.2173815, 2012.

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1 Mar 2012

With a principled methodology for systematic design of human–robot decision-making teams as a motivating goal, we seek an analytic, model-based description of the influence of team and network design parameters on decision-making performance. Given that there are few reliably predictive models of human decision making, we consider the relatively well-understood two-alternative choice tasks from cognitive psychology, where individuals make sequential decisions with limited information, and we study a stochastic decision-making model, which has been successfully fitted to human behavioral and neural data for a range of such tasks. We use an extension of the model, fitted to experimental data from groups of humans performing the same task simultaneously and receiving feedback on the choices of others in the group. First, we show how the task and model can be regarded as a Markov process. Then, we derive analytically the steady-state probability distributions for decisions and performance as a function of model and design parameters such as the strength and path of the social feedback. Finally, we discuss application to human–robot team and network design and next steps with a multirobot testbed.

More Publications

The role of social feedback in steady-state performance of human decision making for two-alternative choice tasks

Stewart, A., and N.E. Leonard, "The role of social feedback in steady-state performance of human decision making for two-alternative choice tasks," in Proc., 49th IEEE Conf. Decision and Control, 15-17 December, Atlanta, GA, 3796-3801, doi:10.1109/CDC.2010.5717531 (IEEE, 2010).

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15 Dec 2010

With an eye towards design of human-in-the-loop systems, we investigate human decision making in a social context for tasks that require the human to make repeated choices among finite alternatives. We consider a human decision maker who receives feedback on his/her own performance as well as on the choices of others performing the same task. We use a drift-diffusion, decision-making model that has been fitted to human neural and behavioral data in sequential, two-alternative, forced-choice tasks and recently extended to the social context with an empirically derived feedback term that depends on choices of other decision makers.

We show conditions for this model to be a Markov process, and we derive the steady-state probability distribution for choice sequences and individual performance as a function of the strength of the social feedback. It has recently been shown in behavioral experiments that human decision-making performance for a relatively easy task is decreased with this social feedback; we show that our analytic predictions agree with this finding.

Steady-state distributions for human decisions in two-alternative choice tasks

Stewart, A., M. Cao, and N.E. Leonard, "Steady-state distributions for human decisions in two-alternative choice tasks," in Proc., American Control Conference, 30 June - 2 July, Baltimore, MD, 2378-2383 (IEEE, 2010).

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30 Jun 2010

In human-in-the-loop systems, humans are often faced with making repeated choices among finite alternatives in response to observations of the evolving system performance. In order to design humans into such systems, it is important to develop a systematic description of human decision making in this context. We examine a commonly used, drift-diffusion, decision-making model that has been fit to human neural and behavioral data in sequential, two-alternative, forced-choice tasks.

We show how this model and type of task together can be regarded as a Markov process, and we derive the steady-state probability distribution for choice sequences. Using the analytic expression for this distribution, we prove matching behavior for tasks that exhibit a matching point and we compute the sensitivity of steady-state choices to a model parameter that measures the decision maker's "exploratory" tendency.

Convergence in human decision-making dynamics

Cao, M., A. Stewart, and N.E. Leonard, "Convergence in human decision-making dynamics," Syst. Control Lett., 59, 87-97, doi:10.1016/j.sysconle.2009.12.00, 2010.

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1 Feb 2010

A class of binary decision-making tasks called the two-alternative forced-choice task has been used extensively in psychology and behavioral economics experiments to investigate human decision making. The human subject makes a choice between two options at regular time intervals and receives a reward after each choice; for a variety of reward structures, these experiments show convergence of the aggregate behavior to rewards that are often suboptimal.

In this paper we present two models of human decision making: one is the Win-Stay, Lose-Switch (WSLS) model and the other is a deterministic limit of the popular Drift Diffusion (DD) model. With these models we prove the convergence of human behavior to the observed aggregate decision making for reward structures with matching points. The analysis is motivated by human-in-the-loop systems, where humans are often required to make repeated choices among finite alternatives in response to evolving system performance measures. We discuss application of the convergence result to the design of human-in-the-loop systems using a map from the human subject to a human supervisor.

Integrating human and robot decision-making dynamics with feedback: Models and convergence analysis

Cao, M., A. Stewart, and N.E. Leonard, "Integrating human and robot decision-making dynamics with feedback: Models and convergence analysis," in Proc., 47th Conf. Decision and Control, 9-11 December, Cancun, Mexico, 1127-1132, doi:10.1109/CDC.2008.4739103 (IEEE, 2008).

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9 Dec 2008

Leveraging research by psychologists on human decision-making, we present a human-robot decision-making problem associated with a complex task and study the corresponding joint decision-making dynamics. The collaborative task is designed so that the human makes decisions just as human subjects make decisions in the two-alternative, forced-choice task, a well-studied decision-making task in behavioral experiments. The human subject chooses between two options at regular time intervals and receives a reward after each choice; for a variety of reward structures, the behavioral experiments show convergence to suboptimal choices.

We propose a human-supervised robot foraging problem in which the human supervisor makes a sequence of binary decisions to assign the role of each robot in a group in response to a report from the robots on their resource return. We discuss conditions under which the decision dynamics of this human-robot task is reasonably well approximated by the kinds of reward structures studied in the psychology experiments. Using the win-stay, lose-switch human decision-making model, we prove convergence to the experimentally observed aggregate human decision-making behavior for reward structures with matching points. Finally, we propose an adaptive law for robot reward feedback designed to help the human make optimal decisions.

Inventions

Virtual Haptic Fixture Tools

Record of Invention Number: 46853

Howard Chizeck, Kevin Huang, Fredrick Ryden, Andy Stewart

Disclosure

21 Feb 2014

Methods for Underwater Haptic Rendering Using Non-contact Sensors

Record of Invention Number: 46396

Wei-Chih Wang, Fredrik Ryden, Payman Arabshahi, Andy Stewart, Howard Chizeck

Disclosure

7 Feb 2013

Virtual Fixtures for Subsea Technology

Record of Invention Number: 46397

Andy Stewart, Fredrik Ryden, Howard Chizeck

Disclosure

7 Feb 2013

Acoustics Air-Sea Interaction & Remote Sensing Center for Environmental & Information Systems Center for Industrial & Medical Ultrasound Electronic & Photonic Systems Ocean Engineering Ocean Physics Polar Science Center
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