https://ntrs.nasa.gov/search.jsp?R=20190002512 2019-08-30T21:36:06+00:00Z Intelligent Systems Division Overview

Autonomous Systems And Robotics Intelligent Systems Division

Division Chief David Alfano

Deputy Chief Scott Poll

Assoc. (Aero. Syst.) Assoc. (Expl. Syst.) Joseph Totah Dr. Michael Lowry Asst. Chief, Operations Sonie Lau

Assoc. (Mission Syst.) Assoc. (Science) Dawn McIntosh Dr. Joseph Coughlan Software Management Office Robert Duffy

Tech Areas:

Autonomous Systems Collaborative and Discovery and Robust Software & Robotics Assistant Systems Systems Health Engineering Lead: Diana Acosta Lead: Richard Papasin Lead: Dr. Kai Goebel Lead: Dr. Guillaume Brat Deputy: Diana Acosta Deputy: Khai (Peter) Tran Deputy: Dr. Anupa Bajwa Groups: Advanced Control and Data Repository Systems Data Sciences Core Avionics and Evolvable Systems Software Technologies Decision Support Systems Diagnostics & Prognostics Deployable Automation System Thinking, Architecture ISHM Technology Technologies Enterprise Information Management and Collaboration Insertion Projects Intelligent Robotics Ground & Flight Data Systems Models and Algorithms for Applied Physics & Reliable Software Planning and Scheduling Information Integration Quantum Computing Assured Autonomy Intelligent Systems Division

Autonomous Systems and Robotics (ASR)

Charter/Goals/Objectives: NASA Vision calls for closer Customers/Projects/Partners: ASR Executes over 30 projects cooperation between humans and systems than ever for various mission directorates and external partners. before. Creating automated and autonomous behaviors via robotic assistants, robust avionics, intelligent planning and ARMD Projects -- Performance Adaptive Aeroelastic Wing; scheduling, and advanced control technologies are the focus of Reduced Crew Operations; small UAS Autonomy; Autonomous Aircraft Operations; the ASR technical area. This endeavor requires building SMD Projects -- Biosentinel Avionics FSW; EuCropis FSWSporesat systems that can adapt their behaviors to environments that are II FSW; Visual Science-Ops for MSL & MRO; Drilling and Sampling complex, rapidly changing, and not well-understood. ASR has Automation; Planetary Lake Lander; DragonEye UAVs for Airborne unique capabilities and agency leadership in applying Science; autonomy and robotics expertise to NASA missions, and STMD Projects -- EDSN/NODES FSW; SPHERES/Astrobee; terrestrial demonstrations. Autonomous Systems Planning and Diagnostics; Tensegrity Robots; Resource Rover; Products/Deliverables: HEOMD Projects --Autonomous Systems and Operations; AES § Avionics software Modular Power Systems (AMPS); GeoCam Space; § Adaptive and optimal control and estimation External -- Crisis Mapping (CM); Disaster Response; § Advanced flight management systems § Automated planning and scheduling Express FSW; Nissan Self-Driving Car § Control agent architectures § Computer vision & digital mapmaking Resources: Greater than 20 Million Dollars § Distributed and multi-agent systems Workforce (FTE/WYE): 32/50 § Drilling automation Facilities/Labs: § Embedded software systems 1. Advanced Control Technology Labs § Evolvable systems 2. Flight Software and Avionics Lab § Geospatial data systems § Model Based Flight Software Development 3. NASA UAS Research Complex § Space robotics (planetary rovers & free-flyers) 4. Robotics labs and Roverscape Intelligent Systems Division

Discovery and Systems Health (DaSH)

Charter/Goals/Objectives Customers/Projects/Partners • Develop methods, models, and tools to understand complex ARMD: AAVP/ACP; AOSP/ATD, SASO, SMART-NAS; engineered systems and large-scale data problems TACP/Seedling, VIPR3; • Physics-based, data-driven and hybrid modeling for HEOMD: AES/AMPS, APL, ASO, RESOLVE, RPM; Integrated Systems Health Management (ISHM) ESD/AGSM, SLS; SLPS/SporeSat2; • Machine learning, data mining, text mining, statistical STMD: GCD/ACAWS, ACLO, RVSM, SAFEM, MGI; pattern recognition, and exploratory data analysis DoD: AFLR; DARPA, IARPA; • Quantum computing NESC; NSF; USGS; • Dramatically improve ability to solve difficult optimization problems for NASA’s missions

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-0.4 -1.5 -1 -0.5 0 0.5 1 1.5 x Products/Deliverables Resources • Analysis of complex physical phenomena Workforce: 26 FTE / 41WYE • Mathematical models representing physical Facilities/Labs: • systems SHARP Lab • Custom aging facilities for mechanical devices • Novel algorithms for health management, (valves, electronics, electrical systems, batteries, analysis of large data, quantum computing wires, electro-mechanical actuators, etc.) • Experimental Data (released on data • Hardware-in-the-loop experiments for algorithm repositories) benchmarking • Improved engineering and scientific processes • Quantum Computer (shared resource, in Code TN) • Integrated software products for flight and ground mission support Intelligent Systems Division

Robust Software Engineering (RSE)

Charter/Goals/Objectives Customers/Projects/Partners • • Our goal is to dramatically increase the reliability and ARMD • AOSP program: SMART-NAS and SASO projects robustness of software in NASA missions and in the aviation • industry, and the productivity of its software engineering, TAP program, CAS project: Cloud in the Clouds study • through the research, development, application, and transfer of STMD • automated software engineering technology that scales to meet SSTP program: Propulsion Path Finder • NASA's software challenges. and Catalyst • • Lead research in formal analysis of cyber-physical systems Other collaborations: • • Lead research in multi-agent systems Lockheed Martin, Boeing, GE, Nissan • Air Force Research Lab, • Infuse in NASA missions and aviation industry • ONERA, Toulouse U. (France), Middlesex U. (UK)

Products/Deliverables Resources

• Research in formal software analysis, from requirement • Workforce (FTE/WYE) analysis to automated testing. • 11 FTEs • Research in system engineering, including software/hardware • 21 WYEs interactions, design for V&V, human-system interaction • Facilities/Lab analysis, and argument-based safety assurance methods. • RSE lab (N269, rm 131) • Mission software, including common plug-and-play software • Advanced Systems Technology Lab (N269, rm 271) bus and advanced V&V tools and processes. • Research in assures autonomy, including tensegrity, multi- agent systems, UAV swarms, and collaborative autonomous systems Intelligent Systems Division

Collaborative Assistant Systems (CAS)

Charter/Goals/Objectives CAS Customers/Projects/Partners • Aeronautics (Airspace Systems) Develops information technologies and collaboration tools that facilitate and enable the specialized work of distributed teams in NASA mission • HQ/Agency-wide (OCIO) settings. • Human Exploration and Operations • HRP – IT Arch, OnePortal, ExMC Lab Analysis & Middleware Mission Statement: The goal of the collaborative and assistant systems • AMO development support technical area is to design and develop information management technologies • Science mission support and collaboration tools that facilitate the process by which NASA engineers, • Airborne Science scientists, and mission personnel collaborate in their unique work settings. The research and development activities in this area focus on applying information • ISOC MMOC: PGS BioScience, Rodent Research management, artificial intelligence, enterprise architecture, and computer- • SOFIA supported cooperative systems that are more usable, augment human cognition, • Space mission support and facilitate the specialized work of distributed teams in NASA mission settings. • NOAA, NRL, ONR, DOE

Products/Deliverables Resources: • ATM Data Integration • Learning Automation/Airspace 23 CS/25+ WYE • Agency Web Services Architecture • Mission Control Technologies • ARC Life Sciences Data Archive (ALSDA) • Mission Tool Suite (MTS) • Autonomous Mission Operations Support • NASA’s Technology Transfer Facilities/Labs: • Cog E for AMMOS System (NTTS) Mission Control Technologies Laboratory • Flight Opportunities Reporting System • OCIO Open Innovation Mission Tool Suite Laboratory • GeneLAB Data Architecture • Open MOS • HRP ExMC EMSD Development • Planetary Data System (PDS) HRP IT Arch & ExMC Development Lab • HRP IT Architecture Tools NTTS & PMT Workspace and Helpdesk • HRP OnePortal • Program/Project Management Tool SOFIA MCCS/PIS Development Lab • IMS (Inductive Monitoring System) Support (PMT) • IRIS Flight Control and GDS • RPM MOS • ISS Operations Center (ISOC) • RPM GDS • IT Labs • SOFIA MCCS/PIS • Kepler SOC • Transiting Exoplanet Survey • TESS SPOC Satellite (TESS) SOC v Blue indicate CAS PI/PM led Intelligent Systems Division Science and Human Exploration Highlights

Kepler (K2)/ TESS IRIS TI leads SOC, responsible Ground Data System and for processing instrument Mission Operations data to allow identification Support of exoplanets.

Rodent Research Project Payload Ground System LADEE (PGS) software for Flight software, operational ISS facility ground data systems and mission ops Human Exploration for successful mission to map the lunar Technologies atmosphere and dust environment First successful demonstration of astronaut orbital control Smart SPHERES of a planetary rover Power, propulsion, computing, and navigation for free- Mission Tools Suite flying robots to Web-based software tools supporting augment and airborne science support ISS crew observations activities Intelligent Systems Division Aeronautics Highlights

NASA, Boeing, University of Washington

Physics Based Methods Group Successfully Demonstrates On-Line Wire Chafing Fault-Detection Variable Camber Continuous Trailing Edge Flap Tests at the Technology on C-17 Jet Engine (AvSafe/VSST). – University of Washington Aeronautical Lab Wind Tunnel. POC: S. Schuet (FAP/FW). – POC: N.Nguyen

Programmable Structures Could Unlock Potential of Morphing Wings Loss of Control Prediction and Cueing: A Pilot in Simulation Experiment on Evaluation PoC: Kenny Cheung the Loop Motion-Based Simulation Test in the Methods and Tools for Reduced Crew Vertical Motion Simulator (AvSafe/VSST). – POC: Operations in the Advance Concepts Flight K. Krishnakumar Simulator (ASP/CTD). – POC’s: M. Feary and J. Kaneshige Intelligent Systems Division

Intelligent Decentralized Survey of Volcanic Plumes Goal: Characterize a volcanic plume to validate 3D models and space based observations (ASTER senor) Case study: for autonomy research to intelligently coordinate UAVs with sensor in-the-loop feedback and distributed control Approach: Coordinate in situ measurements from multiple (3) unmanned aerial vehicles to study emission, transport, composition, of volcanic plumes UAV flight follows simulated/expected plume shape with sensor in the loop feedback to guide multiple UAV coordination STMD project leverages the multiple UAV coordination capabilities and UAV sampling guided by a computer model of expected access to volcanic airspace & plume shape and intensity. NASA Airborne Science ARC mission support & Earth Surface and Interior Focus Area HQ. C.A. Ippolito ,et al. 2016, DOI: 10.2514/6.2016-092 Intelligent Systems Division

Astrobee Perching Arm • Free-flying space robot – International Space Station internal environment – All electric with fan-based propulsion – Three smartphone computers – Expansion port for new payloads – Open-source software Cameras – ~30x30x30 cm, ~8 kg • Uses – Mobile sensor – Remotely operated camera Computers – Zero-G robotic research • Autonomy – Docking & recharge Bumpers – Perching on handrails Signal lights – Vision-based navigation Nozzles Tensegrity SUPERball for Earth Science: Monitoring Hazardous Environments

Lightweight: backpack into remote site

Roll or drop into craters or crevasses

Move to science site after landing

Sample locations unsafe for staff such as volcano vents or avalanches

Drop from drone or airplane

Superball 2.0 ready summer 2017 will be able to fall 10-20 feet safely and have greatly improved locomotion and control capability. Self-Roll to Final Destination Robust Design Allows for Hard Landings and Going Down Craters An unintended slip or fall is no longer fatal to the mission. Intelligent Systems Division Data Sciences Group http://ti.arc.nasa.gov/tech/dash/data-sciences/ Machine Learning/Data Mining Research and Development (R&D) for application to NASA problems (Aeronautics, Earth Science, Space Exploration, Space Science)

Example Problems • Aeronautics: Anomaly Detection, Precursor Identification, text mining (classification, topic identification) for commercial aviation, relating pilot fatigue to aircraft performance, identifying patterns in RNAV waypoint compliance

• Earth Science: Filling in missing measurements (e.g., ground-based pollution sensors) through relationships with other measurements (e.g., satellite remote sensing), anomaly detection, graph mining to find teleconnections and changes in them, learning relationships between vegetation and climate variables through symbolic regression

• Space Science: Kepler and TESS planet candidate identification

• Human Space Exploration: system health management (monitoring ISS using in- house Inductive Monitoring System), vascular structure identification for astronaut health, machine learning within Advanced MultiMission Operations System (AMMOS) • Open-sourced algorithms, papers, project information available on Dashlink (https://c3.nasa.gov/dashlink/) • Collaborators: NASA (multiple centers and divisions), Univ. Minnesota, Univ. Vermont, CSUMB, UCSC, ASU, UC Berkeley, Stanford, SJSU, ASRS (BAH), SWA, easyJet, HP, MITRE