Autonomous Operations for Small, Low-Cost Missions: SME, EOR (STEDI), DATA, and Pluto Express Introduction Challenges Solutions Technologies Technology Gaps OUTLINE Visions for Future, Low-Cost Missions Future Challenges Envisioned Solutions Tall Poles Summary
Introduction: Heritage from SME (Solar Mesospheric Explorer) SME Mission Provides Heritage in Low-Cost Missions (1981-89) $17M for Total Mission $1.2M to Develop entire Mission Ops & Data System $500K / year Ops Budget 2.5 year development (on schedule) Operations Success Advances in Automation Scheduling Data Processing Fault Monitoring
SME Heritage Advances in Interactive Ops Near Realtime Science Optimization Realtime health & status monitoring and response opportunity Lifecycle Continuity One Mission Ops System for Prelaunch Test and Postlaunch Ops Student Controllers Demonstrated quality of student work force Demonstrated Educational Opportunities of University Ops Center
Current Experiences One Basic Set of Needs for End-to- End Mission Operations System (EEMOS) for all 3 missions (EOR- STEDI, DATA, Pluto Express) EOR (Educational Ozone Researcher) Mission to Measure Total Ozone, including Low Altitudes, to Complement TOMS Runner-Up Mission in STEDI competition Low-Cost ($4.6M for spacecraft, payload, mission ops) Faster (2-year development phase, for 97 launch)
Current Experiences DATA (Distribution and Automation Technology Advancement) Shuttle Hitchhiker Mission to: Measure Solar Irradiance & Dynamics Demonstrate Ops System Technologies in Distributed Operations, Automation, and Autonomy Sponsored by Code X INSTEP Program Low Cost (<$500K) Faster (2.5 year development)
Current Experiences Arrival Time Tics = 1 year Pluto orbit Pluto Express Earth launch Jupiter orbit Saturn orbit Uranus orbit Neptune orbit Pluto Express Two Sciencecraft to fly by Pluto and measure surface and atmosphere Small and Low-Cost for Mission to Outer Planet Strong requirement to reduce ops costs with long (~10 yr) cruise One Basic Set of Needs for End-to-End Mission Operations System ( EEMOS ) for All Three Missions
Challenges 1. Interactive Science and Engineering: Enable mission scientists to quickly take advantage of Opportunity Science / Targets of Opportunity to increase scientific value of mission Enable mission engineers to check performance of spacecraft and instruments for prelaunch testing and post-launch diagnostics 2. Fault Detection / Correction: Enable on-board faults to be detected and responded to quickly to preserve mission health and to reduce any loss of observing time 3. Unpredictable Environment: Respond to on-board faults in an environment that is often unpredictable
Challenges 4. Updatable Operations Parameters: Enable operational constraints, rules, sequences, displays, and algorithms to be updated during the flight based on current performance 5. Migration / Evolution of Autonomy: Enable the level of automation and autonomy to be updated during the mission and to migrate from ground to space 6. Cooperative User-Machine Ops: Enable Mission Scientists and Engineers to work cooperatively with operations system located both in-space and on-theground
Challenges 7. Distributed Monitoring: Enable students and other interested groups to monitor mission activities from their distributed locations 8. Remote Ops: Operate a NASA Mission (or a Shuttle Payload) from a University 9. Reduce Costs:... of Operating a space mission by reducing: number of ops personnel, number of shifts, and number of communication passes (and/or tracks). Enable these reductions through space and ground automation and autonomy
Challenges 10. Security: Provide a mission operations system that is secure against unauthorized users 11. Robust Systems: Provide robust and efficient systems both on-board and on the ground to support automation, autonomy, and user interactions 12. Distributed Ops Team: Enable a distributed team of ground operators, engineering analysts, and scientists to operate a space mission as a team
Solutions & Technologies C H A L L E N G E S O L U T I O N ( D e s i g n F e a t u r e s ) T E C H N O L O G Y & R A T I O N A L E 1. I N T E R A C T I V E S C I E N C E 2. F A U L T D E T E C T I O N / C O R R E C T I O N R e a l t i m e T e l e m e t r y / R e a l t i m e D i s p l a y s / R e a l t i m e C o m m a n d i n g O n - b o a r d & G r o u n d S o f t w a r e f o r F a u l t D e t e c t i o n, I s o l a t i o n, a n d R e s p o n s e I n t e r n e t l i n k s S C L ( S y s t e m C o n t r o l L a n g u a g e ) X - D e s i g n e r G U I T o o l O b s e r v a t i o n ( S C L ) D e t e c t i o n ( S E L M O N ) S e l e c t i o n a n d A c t i o n ( S C L ) a s M u l t i - A g e n t S y s t e m 3. U N P R E D I C T A B L E E N V I R O N M E N T 4. U P D A T A B L E O P E R A T I O N S 5. M I G R A T I O N & E V O L U T I O N O F A U T O N O M Y 6. C O O P E R A T I V E U S E R - M A C H I N E O P S R u l e - b a s e d, I n t e l l i g e n t A g e n t S y s t e m U p d a t a b l e D a t a b a s e o f l i m i t s, c o n s t r a i n t s, e t c. S c r i p t s a d d e d / applications added D a t a b a s e u p d a t es R e a l t i m e O p e r a t i n g S y s t e m w i t h m u l t i p r o c e s s i n g O b s e r v a t i o n ( S C L ) D e t e c t i o n ( S E L M O N ) S e l e c t i o n a n d A c t i o n ( S C L ) a s M u l t i - A g e n t S y s t e m ( S a m s c h a r t ) S C L S C L R T E M S (Realtime Executive for Multiprocessor Systems)
Solutions & Technologies C H A L L E N G E S O L U T I O N ( D e s i g n F e a t u r e s ) T E C H N O L O G Y & R A T I O N A L E 7. D I S T R I B U T E D M O N I T O R I N G I n t e r n e t a c c e s s t o d a t a, W W W a c c e s s t o d a t a b a s e 8. R E M O T E O P S I n t e r n e t, S e c u r i t y t e c h n i q u e s, D i s t r i b u t e d C o n t r o l H i e r a r c h y 9. R E D U C E C O S T S O n - B o a r d a n d G r o u n d A u t o n o m y a n d A u t o m a t i o n J u d i c i o u s u s e o f O T S / C O T S t o o l s. Earl y a n d E v o l u t i o n a l p r o t o t y p e s S t u d e n t d e v e l o p m e n t a n d o p s t e a m s 1 0. S E C U R I T Y U s e r A u t h e n t i c a t i o n, T r a n s a c t i o n A u t h e n t i c a t i o n 1 1. R O B U S T S Y S T E M S E a r l y a n d e v o l v i n g p r o t o t y p e s 1 2. D I S T R I B U T E O P S T E A M R o b u s t O p e r a t i n g S y s t e m H i e r a r c h i c a l O r g a n i z a t i o n, S C L, I n t e r n e t I n t e r n e t, W W W I n t e r n e t, K e r b e r o s, F i r e w a l l, S C L S C L, S E L M O N, R a t i o n a l i z e r S p i r a l D e v e l o p m e n t M o d e l K e r b e r o s, F i r e w a l l S p i r a l D e v e l o p m e n t R T E M S a n d A d v a n c e d C o n c e p t s S C L, I n t e r n e t
Teleoperations Semi-Autonomous Operations Concept Expensive continuous control and monitoring Latency and bandwidth limitations complicate coordination and synchronization Full Autonomy ( Zero Uplink ) Local monitoring and control simplifies coordination and synchronization, but... Full autonomy difficult in challenging environments Supervisory Control (Multi-Agent System) Distribution of monitoring and control between groundsegment operators, ground agent automation, and onboard agent automation
Operator Agent System Teleoperations - Continuous Monitoring and Control Ground Segment Space Segment Sensors Effectors Environment
Autonomous Agent System Full Autonomy Perception Action Sensors Effectors Environment
Semi-Autonomous Multi-Agent System Teleoperations Ground Segment Space Segment Perception Perception Supervisory Control Ground Autonomy On-board Autonomy Action Action Sensors Effectors Environment
Semi-Autonomous Modes of Operation Operator Agent - Remote Sensors -> Operator Perception and Control -> Remote Effectors Control Automation - Remote Sensors - > Operator Perception and Action Selection -> *Automated Action -> **Effectors Monitoring Automation - **Sensors -> *Automated Perception -> Operator Control -> Remote Effectors Supervisory Control - **Sensors -> *Automated Perception -> Operator Supervision -> *Automated Action Selection -> *Automated Action -> **Effectors Autonomous Agent - **Sensors -> *Automated Perception -> *Automated Action -> **Effectors * Ground/Space Segment, ** Local/Remote
Detailed Multi-Agent System Observation Detection Classification Action Selection Slower Goal-Oriented Response Quicker Reactive Response Planning Reflex Calibration & Filtering Environment Triggering & Scheduling Action Execution Sensors Effectors Device Commands
Observation and Action System Support Function Time Frame planning Planning Agent hours soft real-time Soft Reflex Agent seconds kernel-level hard real-time hardware control loops Hard Reflex Agent milliseconds microseconds
DATA End-to-End Agent System Concept Situated Agent (High Bandwidth Full State) INTERSEGMENT I/O SERVER EMBEDDED Reflex Agent SENSORS ACTUATORS Local Automation Agent Surrogates (Status) INTERSEGMENT I/O SERVER Remote Automation Operator-Agent Interface (Exception Only) GROUND Planning Agent GROUND Reflex Agent Supervisory Control Dist. Operations Manual Control
DATA Multi-Agent Implementation On-board Reactive Agent SCL RTE performs... Action selection with rule-chaining and constraints Actions from script and rule action database Custom DeviceIO interface performs action execution Ground Deliberative Planning Agent NASA JPL DATA-CHASER Planning System and Plan-IT II perform on-line Heuristically guided iterative repair to schedule based on system model and constraints Generates SCL rule activation and script scheduling commands
DATA Detailed Multi-Agent System SCL RTE Database Observation SELMON Detection SCL RTE Rationalizer Rule-chaining Classification SCL RTE Rule-chaining Action Selection Quicker Reactive Response DeviceIO Calibration & Filtering Slower Goal-Oriented Response DATA-CHASER Instruments DCAPS & Plan-IT II Planning Triggering & Scheduling SCL RTE Reactive Action Execution Environment Sensors Effectors DeviceIO Device Commands (command translation)
Implementation Gaps and Remedies SELMON written as tool in ground system Updated to operate in on-board computer Integrated with SCL Fault Detection, Classification, & Response not fully implemented on-board SCL and SELMON integrated Added Rationalizer and Action Selector to integrated onboard system Adding mode-dependent performance characterization SCL has some bugs, undocumented features, and documentation deficiencies Getting lots of help from ICS, Inc.
Implementation Gaps and Remedies Backup Comm (PPP modem) Scheduling Tool? Not integrated to Command & Control Tool Integrated DCAPS/PLAN-IT-2 with SCL on Ground Security (Sun Firewall, Kerberos, and PAP) Test of Command & Control System needed before S/C done Developed Spacecraft Simulator to enable early testing Training of Ops Team / Checkout of Ops procedures should not start with spacecraft Testbed
Visions for Future, Low-Cost Mission Ops On-board computers, operating system, software same as ground computer PC-like environment Utilization of new distributed open COTS technologies Flexible Real-Time Operating Systems kernels Tailor to embedded system Tailor to ground distributed computing environment PC-like spacecraft Constellation of Small Spacecraft to accomplish Virtual Missions
Visions for Future, Low-Cost Mission Ops Highly Interactive Scientist-in-the-Loop Ops Highly distributed experts on-call throughout network as part of Virtual Operations teams Colleges, K-12 Schools, and the General Public able to participate in missions by receiving live data, requesting sequences, and selective commanding of spacecraft Scientists work at home institutions