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

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

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

A Thermophilic Ionic Liquid-Tolerant Cellulase Cocktail for the Production of Cellulosic Biofuels

Generation of biofuels from sugars in lignocellulosic biomass is a promising alternative to liquid fossil fuels, but efficient and inexpensive bioprocessing configurations must be developed to make this technology commercially viable. One of the major barriers to commercialization is the recalcitrance of plant cell wall polysaccharides to enzymatic hydrolysis. Biomass pretreatment with ionic liquids (ILs) enables efficient saccharification of biomass, but residual ILs inhibit both saccharification and microbial fuel production, requiring extensive washing after IL pretreatment. Pretreatment itself can also produce biomass-derived inhibitory compounds that reduce microbial fuel production. Therefore, there are multiple points in the process from biomass to biofuel production that must be interrogated and optimized to maximize fuel production. Here, we report the development of an IL-tolerant cellulase cocktail by combining thermophilic bacterial glycoside hydrolases produced by a mixed consortia with recombinant glycoside hydrolases. This enzymatic cocktail saccharifies IL-pretreated biomass at higher temperatures and in the presence of much higher IL concentrations than commercial fungal cocktails. Sugars obtained from saccharification of IL-pretreated switchgrass using this cocktail can be converted into biodiesel (fatty acid ethyl-esters or FAEEs) by a metabolically engineered strain of E. coli. During these studies, we found that this biodiesel-producing E. coli strain was sensitive to ILs and inhibitors released by saccharification. This cocktail will enable the development of novel biomass to biofuel bioprocessing configurations that may overcome some of the barriers to production of inexpensive cellulosic biofuels

The James Webb Space Telescope Mission: Optical Telescope Element Design, Development, and Performance

The James Webb Space Telescope (JWST) is a large, infrared space telescope that has recently started its science program which will enable breakthroughs in astrophysics and planetary science. Notably, JWST will provide the very first observations of the earliest luminous objects in the Universe and start a new era of exoplanet atmospheric characterization. This transformative science is enabled by a 6.6 m telescope that is passively cooled with a 5-layer sunshield. The primary mirror is comprised of 18 controllable, low areal density hexagonal segments, that were aligned and phased relative to each other in orbit using innovative image-based wavefront sensing and control algorithms. This revolutionary telescope took more than two decades to develop with a widely distributed team across engineering disciplines. We present an overview of the telescope requirements, architecture, development, superb on-orbit performance, and lessons learned. JWST successfully demonstrates a segmented aperture space telescope and establishes a path to building even larger space telescopes.Comment: accepted by PASP for JWST Overview Special Issue; 34 pages, 25 figure