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Advances in Distributed Operations and Mission Activity Planning for Mars Surface Exploration

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Good News == The successful landing of both rovers and their unexpected long life ... Ensure all users, whether local or remote, use the same tool in the same fashion ... – PowerPoint PPT presentation

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Title: Advances in Distributed Operations and Mission Activity Planning for Mars Surface Exploration


1
Advances in Distributed Operations and Mission
Activity Planning for Mars Surface Exploration
  • Jason M. Fox
  • Jet Propulsion Laboratory
  • June 21, 2006

2
Outline
  • Introduction
  • Limitations of Legacy Tools
  • Overview of Distributed Operations Enhancements
  • Downlink Enhancements
  • Uplink Enhancements
  • Future Work / Conclusions

3
Introduction
  • The Mars Exploration Rover Mission (MER)
    demonstrated that a centralized mission activity
    planning system for a long-term mission is not
    feasible
  • Budget constraints
  • Lifestyle constraints
  • What is Mission Activity Planning?
  • High level specification of scientific intent
  • Highly reactive planning environment
  • The focus is short-term, tactical operations of
    spacecraft activity
  • Assertions
  • The Ground Data System (GDS) must promote a
    secure, distributed mission activity planning
    system
  • MER did not get it right the first time around

4
Outline
  • Introduction
  • Limitations of Legacy Tools
  • Overview of Distributed Operations Enhancements
  • Downlink Enhancements
  • Uplink Enhancements
  • Future Work / Conclusions

5
(No Transcript)
6
SAP - The Legacy Tool
  • The Science Activity Planner (SAP) was the
    primary science operations tool for MER
  • Visualization and planning capabilities enabled
    mission operations team to direct the activities
    of both MER rovers
  • Budget cuts forced the descope of the original
    design for distributed operations features
  • Good News The successful landing of both
    rovers and their unexpected long life
  • Bad News Distributed operations capabilities
    are now required of SAP

7
Remote SAP to the rescue?
  • Remote SAP was developed to enable MER science
    team members the ability to use SAP at their home
    institutions
  • Addresses the following requirements
  • Distributed downlink data (e.g. rover images) to
    remote scientists in a timely fashion
  • Share labels assigned to surface features as they
    are created
  • Facilitate the sharing of files containing the
    planned scientific observations for the following
    day

8
Mixed Results
  • Pros
  • Remote SAP enabled MER to transition to
    distributed operations
  • Supported hundreds of sols of successful
    operations
  • The downlink distribution system successfully
    delivered data to users on most sols
  • Cons
  • The system is very brittle
  • Difficult to administer and monitor
  • Target sharing is slow and unreliable
  • The plan sharing capabilities often fail

9
We Learned a Few Lessons
  • Design operations tools to support distributed
    operations from the very beginning
  • Ensure that all users, whether local or remote,
    use the tool in the same way
  • Eliminate dependencies on share file systems that
    will not function across typical firewalls (e.g.
    NFS)
  • Avoid polling-based synchronization in favor of
    event notification
  • Use relational databases to persist all data that
    supports concurrent editing

10
Outline
  • Introduction
  • Limitations of Legacy Tools
  • Overview of Distributed Operations Enhancements
  • Downlink Enhancements
  • Uplink Enhancements
  • Future Work / Conclusions

11
(No Transcript)
12
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13
MaestroA Step in the Right Direction
  • Designed from the beginning to operate in a
    distributed environment
  • An authenticated user can accomplish her task
    using Maestro on any computer that has a network
    connection
  • This radical new approach to distributed
    operations requires a lot of server side support.
    These services must be
  • Reliable
  • Scalable
  • Secure
  • Flexible
  • And thanks to the open source community, each
    service Maestro requires is built upon a readily
    available, capable, and existing project

14
Server Architecture Diagram
MySQL Server
MySQL
Flight Firewall
Data Catalog
Plan DB
Target DB
dp-daemon
plans
Apache
metadata
targets
data products
data products
metadata
Maestro Client
Tomcat
OSS
range maps
range maps
JMS Server
messages, notifications
Key
physical machine boundary
server instance
data source
15
MySQL Relational Database
  • Maestro interfaces with a JPL institutional MySQL
    database instance installed in a clustered
    environment
  • Secure, flexible, and scalable
  • Restricts access via IP address
  • Auxiliary Data (e.g. authorization information)
  • Downlink
  • Catalog of downlinked data products
  • Rover localization information
  • Uplink
  • Central planning database
  • Targets

16
Other Servers
  • Web Server Apache
  • Data is first compressed and then cataloged
  • Access to cataloged downlinked data via web
    server
  • Pull model superior to Remote SAP system of
    complete replication
  • Messaging Service Java Message Service (JMS)
  • Synchronization required across all clients
  • Modifications to targets and plans are announced
    with JMS
  • Eliminates polling requirements in SAP
  • Application Server Tomcat
  • Range data is provided via large range map
    products
  • To save bandwidth, do not transfer the entire
    file but instead retrieve range data per mouse
    click via a servlet

17
Security
18
Outline
  • Introduction
  • Limitations of Legacy Tools
  • Overview of Distributed Operations Enhancements
  • Downlink Enhancements
  • Uplink Enhancements
  • Future Work / Conclusions

19
Downlink Assessment
  • Contextual awareness is a requirement for
    activity planning
  • For a mobile surface-based spacecraft (e.g. MER)
    the context for planning changes almost daily
  • After every traverse or in situ instrument
    placement
  • Assess the quality of the new science data
  • Understand the state of the rover
  • Identify and prioritize targets of opportunity in
    the locale
  • Automatic cataloging of data products and the
    Maestro tool allow scientists to perform the
    above tasks regardless of geographic location

20
Data Products
  • The Operations Product Generation Subsystem
    (OPGS) pipeline fuses actual images and science
    data with metadata
  • Time of acquisition
  • Rover position
  • Attitude
  • State of the instrument, etc
  • These data and metadata are combined into an
    Experiment Data Record (EDR)
  • Derived products (e.g. calibrated images, XYZ
    position maps for stereo image pairs, etc) are
    called Reduced Data Records (RDR)

21
Data Catalog
  • The OPGS publishes products to a file system
  • Distributed planning requires browsing data via
    remote access
  • The solution
  • Add a relational database driven catalog on top
    of the file system
  • Provide a web server to enable remote access to
    the data products

22
Metadata
23
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24
Compression
  • Image products
  • Compressed on the server side to reduce network
    traffic
  • JPEG compression with the same PDS-format
    metadata header reduces product size by a factor
    of 14
  • Reachability maps
  • Compressed (on average) to be three orders of
    magnitude smaller
  • Simplify the map to include only the information
    scientists require
  • Range Maps
  • Do not distributed the entire (large) range map
    product
  • Instead, provide answers to range queries on a
    click-by-click basis using an application server

25
Outline
  • Introduction
  • Limitations of Legacy Tools
  • Overview of Distributed Operations Enhancements
  • Downlink Enhancements
  • Uplink Enhancements
  • Future Work / Conclusions

26
Uplink
  • The uplink planning system must enable
    collaboration among a geographically distributed
    network of scientists
  • Two-pronged approach
  • A central database to ensure initial
    synchronization and accessibility for all Maestro
    clients
  • A messaging service to maintain synchronization
    via event notification

27
Message Ordering and Protocol
  • JMS does not guarantee message order when
    messages are sent from multiple sessions (i.e.
    multiple Maestro clients)
  • Maestro implements a message ordering layer
    through a combination of JMS headers and database
    tables
  • Modifications to the target and/or plan state are
    robust
  • Nominally, all clients receive notifications via
    JMS
  • Safeguards and a recovery protocol are in place
    to handle client crashes without compromising
    synchronization

28
Outline
  • Introduction
  • Limitations of Legacy Tools
  • Overview of Distributed Operations Enhancements
  • Downlink Enhancements
  • Uplink Enhancements
  • Future Work / Conclusions

29
More yet to be done
  • Additional server dependencies
  • Phoenix will use a headless APGEN (APcore) for
    detailed plan modeling
  • MSL and Phoenix will both make use of an Ames
    constraint engine and modeling tool (Europa)
  • Offline support
  • Better data discovery and catalog generation

30
Conclusion
  • Centralized mission activity planning system for
    any long-term mission is not feasible
  • Distributed system is more flexible while at the
    same time more cost effective
  • Maestro is a step towards a completely
    distributed mission operations tool that folded
    into its design the following lessons from MER
  • Design operations tools from the beginning to
    support distributed operations
  • Ensure all users, whether local or remote, use
    the same tool in the same fashion
  • Eliminate dependencies on shared file systems
  • Avoid polling-based synchronization in favor of
    event notification
  • Use relational databases to persist all data that
    needs to support concurrent editing
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