Model Estimation Within Planning and Learning

Risk and reward are fundamental concepts in the cooperative control of unmanned systems. In this research, we focus on developing a constructive relationship between cooperative planning and learning algorithms to mitigate the learning risk, while boosting system (planner & learner) asymptotic performance and guaranteeing the safety of agent behavior. Our framework is an instance of the intelligent cooperative control architecture (iCCA) where the learner incrementally improves on the output of a baseline planner through interaction and constrained exploration. We extend previous work by extracting the embedded parameterized transition model from within the cooperative planner and making it adaptable and accessible to all iCCA modules. We empirically demonstrate the advantage of using an adaptive model over a static model and pure learning approaches in an example GridWorld problem and a UAV mission planning scenario with 200 million possibilities. Finally we discuss two extensions to our approach to handle cases where the true model can not be captured exactly through the presumed functional form.United States. Air Force Office of Scientific Research (FA9550-09-1-0522)Natural Sciences and Engineering Research Council of CanadaUSAF (FA9550-09-1-0522

Dispersed Fringe Sensing Analysis - DFSA

Dispersed Fringe Sensing (DFS) is a technique for measuring and phasing segmented telescope mirrors using a dispersed broadband light image. DFS is capable of breaking the monochromatic light ambiguity, measuring absolute piston errors between segments of large segmented primary mirrors to tens of nanometers accuracy over a range of 100 micrometers or more. The DFSA software tool analyzes DFS images to extract DFS encoded segment piston errors, which can be used to measure piston distances between primary mirror segments of ground and space telescopes. This information is necessary to control mirror segments to establish a smooth, continuous primary figure needed to achieve high optical quality. The DFSA tool is versatile, allowing precise piston measurements from a variety of different optical configurations. DFSA technology may be used for measuring wavefront pistons from sub-apertures defined by adjacent segments (such as Keck Telescope), or from separated sub-apertures used for testing large optical systems (such as sub-aperture wavefront testing for large primary mirrors using auto-collimating flats). An experimental demonstration of the coarse-phasing technology with verification of DFSA was performed at the Keck Telescope. DFSA includes image processing, wavelength and source spectral calibration, fringe extraction line determination, dispersed fringe analysis, and wavefront piston sign determination. The code is robust against internal optical system aberrations and against spectral variations of the source. In addition to the DFSA tool, the software package contains a simple but sophisticated MATLAB model to generate dispersed fringe images of optical system configurations in order to quickly estimate the coarse phasing performance given the optical and operational design requirements. Combining MATLAB (a high-level language and interactive environment developed by MathWorks), MACOS (JPL s software package for Modeling and Analysis for Controlled Optical Systems), and DFSA provides a unique optical development, modeling and analysis package to study current and future approaches to coarse phasing controlled segmented optical systems

Philanthropy Prizes

This publication provides a brief insight into the wide variety of prizes and awards offered by EFC members and the wider philanthropic sector. The list is not exhaustive but instead offers a selection of prizes that showcase the diverse thematic areas and sectors of  work that prizes can be found recognising, supporting and inspiring

Vision-based Target Localization from a Small, Fixed-wing Unmanned Air Vehicle

Unmanned air vehicles (UAVs) are attracting increased attention as their envelope of suitable tasks expands to include activities such as perimeter tracking, search and rescue assistance, surveillance and reconnaissance. The simplified goal of many of these tasks is to image an object for tracking or information-gathering purposes. The ability to determine the inertial location of a visible, ground-based object without requiring a priori knowledge of its exact location would therefore prove beneficial. This thesis discusses a method of localizing a ground-based object when imaged from a fixed-wing UAV. Using the target\u27s pixel location in an image, with measurements of UAV position, attitude and camera pose angles, the target is localized in world coordinates. This thesis also presents a study of possible error sources and localization sensitivities to each source. From this study, an accuracy within 15.5 m of actual target location is expected. Also, several methods of filtering are presented, which allow for effective noise reduction. Finally, filtered hardware results are presented that verify these expectations by localizing a target from a fixed-wing UAV using on-board vision to within 10.9 meters

Detection of PFOA Using Molecularly Imprinted Polymers

Undergraduate Basi

Approximate multi-agent planning in dynamic and uncertain environments

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, February 2012."December 2011." Cataloged from PDF version of thesis.Includes bibliographical references (p. 120-131).Teams of autonomous mobile robotic agents will play an important role in the future of robotics. Efficient coordination of these agents within large, cooperative teams is an important characteristic of any system utilizing multiple autonomous vehicles. Applications of such a cooperative technology stretch beyond multi-robot systems to include satellite formations, networked systems, traffic flow, and many others. The diversity of capabilities offered by a team, as opposed to an individual, has attracted the attention of both researchers and practitioners in part due to the associated challenges such as the combinatorial nature of joint action selection among interdependent agents. This thesis aims to address the issues of the issues of scalability and adaptability within teams of such inter-dependent agents while planning, coordinating, and learning in a decentralized environment. In doing so, the first focus is the integration of learning and adaptation algorithms into a multi-agent planning architecture to enable online adaptation of planner parameters. A second focus is the development of approximation algorithms to reduce the computational complexity of decentralized multi-agent planning methods. Such a reduction improves problem scalability and ultimately enables much larger robot teams. Finally, we are interested in implementing these algorithms in meaningful, real-world scenarios. As robots and unmanned systems continue to advance technologically, enabling a self-awareness as to their physical state of health will become critical. In this context, the architecture and algorithms developed in this thesis are implemented in both hardware and software flight experiments under a class of cooperative multi-agent systems we call persistent health management scenarios.by Joshua David Redding.Ph.D

Intelligent Cooperative Control Architecture: A Framework for Performance Improvement Using Safe Learning

Planning for multi-agent systems such as task assignment for teams of limited-fuel unmanned aerial vehicles (UAVs) is challenging due to uncertainties in the assumed models and the very large size of the planning space. Researchers have developed fast cooperative planners based on simple models (e.g., linear and deterministic dynamics), yet inaccuracies in assumed models will impact the resulting performance. Learning techniques are capable of adapting the model and providing better policies asymptotically compared to cooperative planners, yet they often violate the safety conditions of the system due to their exploratory nature. Moreover they frequently require an impractically large number of interactions to perform well. This paper introduces the intelligent Cooperative Control Architecture (iCCA) as a framework for combining cooperative planners and reinforcement learning techniques. iCCA improves the policy of the cooperative planner, while reduces the risk and sample complexity of the learner. Empirical results in gridworld and task assignment for fuel-limited UAV domains with problem sizes up to 9 billion state-action pairs verify the advantage of iCCA over pure learning and planning strategies

Using the Pathways Community HUB Care Coordination Model to Address Chronic Illnesses: A Case Study

Background/Objectives: Ohio communities are developing and expanding care coordination initiatives to integrate care for low-income pregnant women. Some of these initiatives are guided by the Pathways Community HUB model, which uses community healthworkers to address health, social, and behavioral risks for at-risk populations. This study documents the development, challenges andmanagement responses, and lessons learned from implementing a Pathways Community HUB care coordination program for anotherpopulation -- low-income adults with chronic disease risks.Methods: The study utilizes data extracted from the Care Coordination Systems (CCS) database used in Lucas County, Ohio between2015 and 2017 and interviews with program managers. Based on CCS data and insights from those interviewed, we describe the development and accomplishments of a Pathways Community HUB program for adults with chronic illnesses and identify challenges and lessons learned.Results: The Toledo/Lucas County program addressed more than half of 3,515 identified health and behavioral risks for 651 low-income adults in the program during its first two years of operation. Key challenges included building community support, establishing capacities to coordinate care, and sustaining the program over time. Establishing community networks to support program services and developing multiple funding sources are key lessons for long-term program sustainability.Conclusions: Documenting challenges and successes of existing programs and extracting lessons to guide implementation of similarpublic health efforts can potentially improve delivery of interventions. The Pathways Community HUB model has demonstrated success in addressing risks among at-risk adults. However, more comprehensive assessments of the model across different populations are warranted

A Test of Spectroscopic Age Estimates of White Dwarfs using Wide WD+WD Binaries

White dwarf stars have been used for decades as precise and accurate age indicators. This work presents a test of the reliability of white dwarf total ages when spectroscopic observations are available. We conduct follow-up spectroscopy of 148 individual white dwarfs in widely separated double-white-dwarf (WD+WD) binaries. We supplement the sample with 264 previously published white dwarf spectra, as well as 1292 high-confidence white dwarf spectral types inferred from their Gaia XP spectra. We find that spectroscopic fits to optical spectra do not provide noticeable improvement to the age agreement among white dwarfs in wide WD+WD binaries. The median age agreement is $\approx$$1.5\sigma$ for both photometrically and spectroscopically determined total ages, for pairs of white dwarfs with each having a total age uncertaintiy $<$ 20\%. For DA white dwarfs, we further find that photometrically determined atmospheric parameters from spectral energy distribution fitting give better total age agreement ($1.0\sigma$, 0.2 Gyr, or 14\% of the binary's average total age) compared to spectroscopically determined parameters from Balmer-line fits (agreement of $1.5\sigma$, 0.3 Gyr, or 28\% of binary's average total age). We find further evidence of a significant merger fraction among wide WD+WD binaries: across multiple spectroscopically identified samples, roughly 20\% are inconsistent with a monotonically increasing initial-final mass relation. We recommend the acquisition of an identification spectrum to ensure the correct atmospheric models are used in photometric fits in order to determine the most accurate total age of a white dwarf star.Comment: 38 pages, 17 figures, submitted to Ap

Wavefront shaping with disorder-engineered metasurfaces

Recently, wavefront shaping with disordered media has demonstrated optical manipulation capabilities beyond those of conventional optics, including extended volume, aberration-free focusing and subwavelength focusing. However, translating these capabilities to useful applications has remained challenging as the input–output characteristics of the disordered media (P variables) need to be exhaustively determined via O(P) measurements. Here, we propose a paradigm shift where the disorder is specifically designed so its exact input–output characteristics are known a priori and can be used with only a few alignment steps. We implement this concept with a disorder-engineered metasurface, which exhibits additional unique features for wavefront shaping such as a large optical memory effect range in combination with a wide angular scattering range, excellent stability, and a tailorable angular scattering profile. Using this designed metasurface with wavefront shaping, we demonstrate high numerical aperture (NA > 0.5) focusing and fluorescence imaging with an estimated ~2.2 × 10^8 addressable points in an ~8 mm field of view