sops: the science operations planning system for the first esa lunar mission smart-1

SOPS: The Science Operations Planning System for the

first ESA Lunar Mission SMART-1

System Level View

What is a Science Operation Planning System?

Scientific

goals

TargetsMission Objectives

Operational Constraints

Pointing Profile?

Principal Investigator

PORs

Operation Time LinesEnvironmental Constraints

Simulation

Scheduling

Science Opportunity Window

Payload

System Level View

Science Operations Planning System

Input Output

Interface Interface

What I would like to do

What are the constraints

Consolidated and conflict-free Plan for operations of

Payload!!!

•MIRA Request•Observation Request•SPL•…

•Environmental Constraints•Thermal Constraints•Payload Constraints•S/C sub-systems constraints•…

•ITLs•PORs•PTRs•…

System Level View

Input: What I want

- Concrete request including required Execution Time:

Take an image of Target YYY in Orbit ZZZ

Perform a dust particle analyse as long as possible in the time window XXX

- Generic Input without concrete execution time:

Take an Image of Target XXX, whenever the distance is YYY and the

local solar elevation angle is ZZZ and ….

Perform a dust particle analyse as long as possible, whenever the

concentration of particles is higher than XXX and S/C Thrusters are off

and …

System Level View

Input: What are the constraints

- Environmental Constraints: Local Solar Elevation and Azimuth angles,

distances, phase angles, particle concentration, target visibilities, …

-Thermal Constraints: Max illumination of panel XXX shall be YYY for max

duration of ZZZ, pre-defined Thermal profiles for operational phases

-Resource Constraints: Power consumption, Data generation, Satellite Orientation

-S/C and Sub-System Constraints: Reaction wheels saturation, Star tracker blindings,

Slew times between two satellite orientations, ..

-Payload Constraints: Interference between different Payloads and S/C, Internal

payload constraints, Mode level constraints

Science Operations Planning Concepts

Decentralized Science Operation Planning through Conflict Resolving

Centralized Science Opportunity Analysing Operation Planning through Conflict Avoidance

Payload Team 1

SOC

Payload Team 3

Payload Team 4

Flight Control TeamFlight Dynamic Team DataOperationalConstraints

ConsolidatedPlan

Payload Team 2 ScienceKnowledge

Base

Management of all relevant operational Data

Performing environmental and sub/system level simulations

Analysing the simulation results and identifying available science opportunity windows

Selecting some of available science opportunities

Resource management and conflict resolution Prioritising and selecting among overlapping science opportunity windows

Preparation of the final, consolidated science operations planning products

Detailed operational Timeline files Detailed S/C orientation/pointing request files

Tracking of all performed observations and achieved scientific objectives of the mission.

System Requirements

System Architecture

Operation Planning

Knowledgebase

SimulatorScience

OpportunityAnalyzer

Planner &Scheduler

Visualisation Modules

Operation Plan Generator

Environmental Model

Payload Models

S/C Sub-System Models

Thermal Model

Slew Est. Model

Observation Requests

Target Definitions

Operation Profiles

Pointing Profiles

Payload Information

Constraint Definitions

Scientific Objectives

Performed Observations

Science Opportunity

Windows

OperationalOpportunity

Windows

FCT/FDTMDS Systems

PTR

POR

ITL

Payload Team Observation Request

Environmental and operational constraints

Observation Type: Required S/C orientation towards the target

Target Group: All the targets, for which observation is required

Operations: Detailed Operation Timeline during the observation

Detailed Science Themes: Scientific objectives, to which this observation would contribute

defines

Environmental Simulator

Flight Dynamic Team Auxiliary Data

provides

Input to

Science Opportunity Windows: Exact time slot such as

15.03.2006 12:14:56 UTC

To 15.03.2006 12:52:11 UTC

STOC

Identifies

Interface to

Science and Technology Operation

Coordination

Process Flow View of the System

Consolidated/Constraint Free

Plan

Management of all relevant operational DataUnderlying TechnologiesJ2EE: Container managed Enterprise Java BeansRelational, SQL-based databaseWeb-Client: Servlets and Java Server Pages

Modelled Knowledgebase Entities Target Target group Payload Constraint Type Constraint Science Theme Observation Profile Payload Operations Profile Observation Request Performed Observations Science Opportunities Orbits, Planning Cycles

SOPS Knowledgebase

Performing environmental and sub/system level simulations

004_23:32:06 LM_LSE_70_80_START (COUNT = 4170001)

004_23:32:14 LM_VIS_ALG_20_30_START (COUNT = 1980041)

004_23:32:14 LM_VIS_ALG_30_90_END (COUNT = 1980041)

004_23:32:15 LM_VIS_LIM_END (COUNT = 2110041)




004_23:32:19 LM_VIS_LIM_START (COUNT = 2230041)

004_23:32:23 LM_VIS_ALG_5_10_START (COUNT = 2170041)

004_23:32:23 LM_VIS_ALG_10_20_END (COUNT = 2170041)


004_23:32:25 LM_VIS_LIM_START (COUNT = 1930041)

PTB: Project Test BedExisting Simulator based on EuroSim Frame-Work

Reports changes in the environmental properties as events

Result of one week simulation: 45 MB ASCII event file

Science Operations Analyzer

100s of opportunities per week

Visibility and geometry constraints

Different pointing modes nadir, cross-track, tracking, inertial Conflicting pointing

Platform thermal constraints

Payload geometric constraints e.g Sun in FoV.

Payload maintenance

No ground station schedule


Underlying Technology J2EE Client Server – Client Architecture TCP/IP connection to the knowledgebase Platform independence Import / Export Functionality Generation of interface documents for other ESA planning software

#----------------------Orbit 2319126_07:26:21 AM_PHT_MOR_START (COUNT = 1010001)126_07:26:40 AM_PHT_MOR_START (COUNT = 4240002)126_07:26:40 POLAR_MON_START (COUNT = 4240001)126_07:27:53 AM_PHT_MOR_START (COUNT = 1000003)126_07:29:08 AM_MAPPING_START (COUNT = 4220001)126_07:29:08 D_CIXS_GLOBAL_MAPPING_START (COUNT = 4220001)126_07:29:08 SIR_POLE_TO_POLE_START (COUNT = 4220001)126_07:33:16 AM_PHT_MOR_END (COUNT = 1010001)126_07:34:04 AM_PHT_MOR_END (COUNT = 4240002)126_07:34:04 POLAR_MON_END (COUNT = 4240001)126_07:35:15 AM_PHT_MOR_END (COUNT = 1000003)

#CDT BLOCK126_05:18:24 STOC INERT_START ( POINTING_AXIS = X OBJECT = EARTH SLEW_POLICY = SMOOTH YDIR = POSITIVE )126_05:48:24 STOC INERT_END#Light side start126_07:26:21 STOC NADIR_START ( OBJECT_TO_BE_POINTED = Z SLEW_POLICY = SMOOTH YDIR = POSITIVE ) #Light side end126_09:18:49 STOC NADIR_END #WOL + Inertial Cool Down start126_09:47:21 STOC INERT_START ( OBJECT = WOL SLEW_POLICY = SMOOTH YDIR = POSITIVE )#End of WOL126_11:28:07 STOC INERT_END #Light side start126_12:25:24 STOC NADIR_START ( OBJECT_TO_BE_POINTED = Z SLEW_POLICY = SMOOTH YDIR = POSITIVE )

Science Operations Scheduler

Constraint-Based Scheduling and optimizing using the constraint programming library of the Fraunhofer FIRST, firstCS Pure CSP modeling of the scheduling problem

Finding an optimized solution using Labeling algorithms (Reduction of Domains)

Research Study (not part of the official SOPS development work)

final CS cs = new CS();

//Task 1Variable start = new Variable(0, 12);Variable duration = new Variable(9);Variable end = new Variable(9, 15);

Sum s = new Sum(start,duration,end);Cs.add(sum);

...

Tracking and Analysing of Performed Observations

The results of analysing/planning sessions are feed back into the same knowledgebase:

- Planning Cycles

- Orbits

- Communication Opportunities

- Science Opportunities

- Performed Observations

- All entities are time-taged and inter-related.

- Any kind of queries (SQL or prepared Masks) can be carried out to perform detailed scientific analysis.

- Closing the loop in the planning by taking the planning history and future into account.

SOPS Features Summary

Single Repository for all relevant information about science operations in a knowledgebase

Web-based and easy access via the Internet to the knowledgebase

Platform independent Java client for analyzing, scheduling, visualizing and planning

Identification of all available science opportunity windows in a planning cycle

Several visualization forms of analyzing results

Partly automated scheduling of the identified science opportunity windows

Generating interface files for other ESA planning software and the flight control team

Reporting and Tracking functionality for all performed observations

Target Coverage during the Push Broom 1 Phase of the Mission

SMART-1 Results, achieved using SOPS

Coverage Image from the ESA MAPPS tool

Coverage Image from the ESA MAPPS tool

SMART-1 Results, achieved using SOPS

Target Coverage during the Medium Solar Elevation Phase of the Mission

Implementation

J2EE Architecture

Implementation

Implementation

Services Provided by the J2EE Server:

• The J2EE security model lets you configure a web component or enterprise bean so that system resources are accessed only by authorized users.

• The J2EE transaction model lets you specify relationships among methods that make up a single transaction so that all methods in one transaction are treated as a single unit.

• JNDI lookup services provide a unified interface to multiple naming and directory services in the enterprise so that application components can access naming and directory services.

• The J2EE remote connectivity model manages low-level communications between clients and enterprise beans. After an enterprise bean is created, a client invokes methods on it as if it were in the same virtual machine.

Implementation

TargetPayload Target GroupFacade Session Bean

DataManager

Entity EJBsWith CMP

J2EE Clients

Implementation

sops: the science operations planning system for the first esa lunar mission smart-1

Documents

subsystem constraints

system level viewwhat

operations of payload

internal payload constraints

subsystem level simulations

concrete execution time

image of target yyy

time window