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

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

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

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

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

Functional Modeling Identifies Paralogous Solanesyl-diphosphate Synthases That Assemble the Side Chain of Plastoquinone-9 in Plastids

Background: Plastid isoforms of solanesyl-diphosphate synthase catalyze the elongation of the prenyl side chain of plastoquinone-9. Results: Corresponding mutants display lower levels of plastoquinone-9 and plastochromanol-8 and display intact levels of vitamin E. Conclusion: Plastochromanol-8 originates from a subfraction of non-photoactive plastoquinol-9 and is not essential for seed longevity. Significance: Viable plastoquinone-9 mutants are invaluable tools for understanding plastid metabolism

Automated Battery Swap and Recharge to Enable Persistent UAV Missions

This paper introduces a hardware platform for automated battery changing and charging for multiple UAV agents. The automated station holds a bu er of 8 batteries in a novel dual-drum structure that enables a "hot" battery swap, thus allowing the vehicle to remain powered on throughout the battery changing process. Each drum consists of four battery bays, each of which is connected to a smart-charger for proper battery maintenance and charging. The hot-swap capability in combination with local recharging and a large 8-battery capacity allow this platform to refuel multiple UAVs for long-duration and persistent missions with minimal delays and no vehicle shutdowns. Experimental results from the RAVEN indoor flight test facility are presented that demonstrate the capability and robustness of the battery change/charge station in the context of a multi-agent, persistent mission where surveillance is continuously required over a speci ed region.Boeing Scientific Research LaboratoriesUnited States. Air Force Office of Scientific Research (FA9550-09-1-0522

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

Functional Modeling Identifies Paralogous Solanesyl-diphosphate Synthases That Assemble the Side Chain of Plastoquinone-9 in Plastids

Background: Plastid isoforms of solanesyl-diphosphate synthase catalyze the elongation of the prenyl side chain of plastoquinone-9. Results: Corresponding mutants display lower levels of plastoquinone-9 and plastochromanol-8 and display intact levels of vitamin E. Conclusion: Plastochromanol-8 originates from a subfraction of non-photoactive plastoquinol-9 and is not essential for seed longevity. Significance: Viable plastoquinone-9 mutants are invaluable tools for understanding plastid metabolism