Space Applications for Distributed Constraint Reasoning

Space Applications forDistributed Constraint Reasoning

Brad Clement, Tony BarrettArtificial Intelligence GroupJet Propulsion Laboratory

California Institute of [email protected]://ai.jpl.nasa.gov/

2

Outline

• Applications– multi-spacecraft missions– “collaborative” mission planning– network scheduling

• Current approaches

• Challenges for DCR

• Unsolicited opinions

3

Multi-Robot Control

• Goal selection

• Future commanding

• Commanding now– mode estimation/diagnosis

• Perception & actuation

AnalystAnalyst

PlannerPlanner

ExecutiveExecutive

ControlControl

AnalystAnalyst

PlannerPlanner

ExecutiveExecutive

ControlControl

AnalystAnalyst

PlannerPlanner

ExecutiveExecutive

ControlControl

AnalystAnalyst

PlannerPlanner

ExecutiveExecutive

ControlControl

AnalystAnalyst

PlannerPlanner

ExecutiveExecutive

ControlControl

AnalystAnalyst

PlannerPlanner

ExecutiveExecutive

ControlControl

4

Control of/by Humans

• Goal selection

• Future commanding



AnalystAnalyst

PlannerPlanner

ExecutiveExecutive

ControlControl

AnalystAnalyst

PlannerPlanner

ExecutiveExecutive

ControlControl

AnalystAnalyst

PlannerPlanner

ExecutiveExecutive

ControlControl

AnalystAnalyst

PlannerPlanner

ExecutiveExecutive

ControlControl

AnalystAnalyst

PlannerPlanner

ExecutiveExecutive

ControlControl

AnalystAnalyst

PlannerPlanner

ExecutiveExecutive

ControlControl

http://nl.wikipedia.org/wiki/Afbeelding:Astronaut-EVA.jpg

5

Optimize a function of variable assignments with both local and non-local constraints.

Distributed Constrained Optimization

ControlControl

ExecutiveExecutive

PlannerPlanner

AnalystAnalyst

7

Space Applications

• multiple rovers• spacecraft constellation• Earth orbiters• Mars network• DSN antenna allocation• mission planning• construction, repair• crew operations

Decentralize decision-making?

• competing objectives (self-interest)

• control is already distributed

• communication constraints/costs

• computation constraints

8

Applications – Multiple Spacecraft

Over 40 multi-spacecraft missions proposed!– Autonomous single spacecraft missions

have not yet reached maturity.– How can we cost-effectively manage

multiple spacecraft?

Earth Observing System Sun-Earth Connections

Origins Program

Structure & Evolution of the Universe

Mars Network

NMP

NMP

9

Applications – Multiple SpacecraftClassification of Phenomena

(Underlying Scientific Questions)

Five Classification Metrics• Signal Location

– Where are the signals?

• Signal Isolation– How close are distinct signals in

phenomenon?

• Information Integrity– How much noise is inherent in

each signal?

• Information Rate– How fast do the signals change?

• Information Predictability– How predictable is the

phenomenon?

Five Classification Metrics• Signal Location

– Where are the signals?

• Signal Isolation– How close are distinct signals in

phenomenon?

• Information Integrity– How much noise is inherent in

each signal?

• Information Rate– How fast do the signals change?

• Information Predictability– How predictable is the

phenomenon?

x

y

Signals from Celestial Sphere

t

Signals from Magnetosphere

10

Isolation & IntegrityIsolation & Integrity Rate & PredictabilityRate & Predictability

Applications – Multiple SpacecraftMultiple Platform Mission Types

Rate

Predictability

Low

High

High

Low

SingleSpacecraft

Signal Separation

Signal SpaceCoverage

Signal Combination

Noise

Resolution Need

Low

High

High

Low

SingleSpacecraft

11

Space Applications – ScienceHow to Distribute?

GN&CGN&C

ExecutiveExecutive

PlannerPlanner

AnalystAnalyst

GN&CGN&CGN&CGN&C GN&CGN&C

ExecutiveExecutive

PlannerPlanner

AnalystAnalyst

GN&CGN&C

ExecutiveExecutive

PlannerPlanner

AnalystAnalyst

GN&CGN&C

ExecutiveExecutive

PlannerPlanner

AnalystAnalyst

Cross-links

Who gets which components?

GN&CGN&C

ExecutiveExecutive

PlannerPlanner

AnalystAnalyst

GN&CGN&C

ExecutiveExecutive

PlannerPlanner

AnalystAnalyst

GN&CGN&C

ExecutiveExecutive

PlannerPlanner

AnalystAnalyst

12

Autonomous Signal Separation

• Why many executives?– Each spacecraft can have

local anomalies.– During an anomaly

communications can be lost due to drift.

• Why only one planner?– During normal operations

the spacecraft are guaranteed to be able to communicate.

– Since spacecraft join to make an observation, only one analyst is needed.

GN&CGN&C

ExecutiveExecutive

PlannerPlanner

AnalystAnalyst

GN&CGN&C

ExecutiveExecutive

GN&CGN&C

ExecutiveExecutive

13

Autonomous Signal Space Coverage

• Why many planners?– Cross-link is lost during

normal operations, but spacecraft still have to manage local activities and respond to science events.

• Why communicate at all?– The value of local

measurements is enhanced when combined with data from others. Analysts must coordinate over collection.

GN&CGN&C

ExecutiveExecutive

PlannerPlanner

AnalystAnalyst

GN&CGN&C

ExecutiveExecutive

PlannerPlanner

AnalystAnalyst

GN&CGN&C

ExecutiveExecutive

PlannerPlanner

AnalystAnalyst

14

Autonomous Signal/Mission Combination

• How does this differ from signal space coverage?– Each entity has different

capabilities• Sensors: radar, optical, IR...• Mobility: satellite, rover...• Communications abilities.

– Each mission has its own motivations.

• There is a competition where each mission wants to optimize its own objectives in isolation.

GN&CGN&C

ExecutiveExecutive

PlannerPlanner

AnalystAnalyst

GN&CGN&C

ExecutiveExecutive

PlannerPlanner

AnalystAnalyst

GN&CGN&C

ExecutiveExecutive

PlannerPlanner

AnalystAnalyst

15

Space Applications – Mission OperationsDecentralize decision-making?





• multiple instruments on spacecraft contend for resources

• multiple scientists may compete for one instrument (HST)

• scientists work with operations staff to make sure goals can be safely achieved

• plans must be validated (carefully simulated)

• changes made by users in parallel invalidate validation

Payload GS Payload GS (i.e., Datalynx, USN)(i.e., Datalynx, USN)

Payload GS Payload GS (i.e., Datalynx, USN)(i.e., Datalynx, USN)

(X-band)(X-band)(X-band)(X-band) Spacecraft GS Spacecraft GS (i.e., RSC)(i.e., RSC)

CommandsCommands(L-Band)(L-Band)

CommandsCommands(L-Band)(L-Band)

Telemetry, Telemetry, QL PayloadQL Payload

(S-Band)(S-Band)

Telemetry, Telemetry, QL PayloadQL Payload

(S-Band)(S-Band)

{ AFSCN }{ AFSCN }{ AFSCN }{ AFSCN }Payload DataPayload DataPayload DataPayload Data

Payload Downlink Requests Payload Downlink Requests Payload Downlink Requests Payload Downlink Requests Payload DataPayload Data - - FTPFTP - Overnight (all)- Overnight (all)

Payload DataPayload Data - - FTPFTP - Overnight (all)- Overnight (all)

Telemetry (ftp)

Telemetry (ftp)

Telemetry (ftp)

Telemetry (ftp)

PagerPagerPagerPager

Mission PlanningMission PlanningMission PlanningMission Planning

Simulation EnvSimulation EnvSimulation EnvSimulation Env

Commanding Commanding SOH displaySOH displayTelemetry Telemetry

Commanding Commanding SOH displaySOH displayTelemetry Telemetry

ASPEN ASPEN ASPEN ASPEN

SCL SCL SCL SCL

Fight DynamicsFight DynamicsFight DynamicsFight Dynamics

Payload Payload Ops W/SOps W/SPayload Payload Ops W/SOps W/S

Activity schedules

Activity schedules

Activity schedules

Activity schedules

TS-21 EngrTS-21 EngrTS-21 EngrTS-21 Engr

Cmd VerificationCmd VerificationCmd VerificationCmd Verification Engineering ModelsEngineering Models Engineering ModelsEngineering Models

PPC ClusterPPC ClusterPPC ClusterPPC ClusterCmd VerificationCmd VerificationCmd VerificationCmd Verification

TT&C W/STT&C W/S TT&C W/STT&C W/S

TT&C W/STT&C W/STT&C W/STT&C W/S

Data CenterData Center Data CenterData Center

Pass PlaybackPass PlaybackSOH displaySOH displayTrendingTrendingAnom ResAnom Res

Pass PlaybackPass PlaybackSOH displaySOH displayTrendingTrendingAnom ResAnom Res

SCLSCLMatlabMatlabSCLSCLMatlabMatlab

TT&C W/STT&C W/S TT&C W/STT&C W/S

PTF PTF PTF PTF

MOCMOCMOCMOC

R/T MOCR/T MOCR/T MOCR/T MOC

MPW

local constraints

newactivities

rejectedactivities

rescheduledactivities

confirmationscheduleupdates

removedactivities

Techsat-21Techsat-21

16

Applications - Deep Space Network (DSN)

17

Applications - Deep Space Network (DSN)Decentralize decision-making?





• 56 missions• 12 antennas

– different capabilities– shared equipment– geometric constraints– human operator constraints

• some schedule as long as 10 years into future• some require schedule freeze 6 months out• complicated requirements originally from agreement with NASA

with flexibility in antennas, timing, numbers of tracks, gaps, etc.• schedule centrally generated, meetings and horse trading to

resolve conflicts• similar to coordination operations across missions

18

Applications – DSN ArraysDecentralize decision-making?





• NASA may build 3600 10m weather-sensitive antennas

• 1200 at each complex in groups of 100 spread over wide area

• High automation requested—one operator for 100 or 1200 antennas

• Spacecraft may use any number of antennas for varying QoS, and may need link carried across complexes

• Only some subsets of antenna signals can be combined

– depends on design of wiring/switching to combiners

– combiners may be limited• Local response time should be

minimized

DSCC

Array Signal Proc

Other DSN Systems

Array Sites

Sig ProcSig Proc

Sig Proc

Sig Proc

Sig Proc

Sig Proc

Sig Proc

Sig Proc

19

Mars Network

• Network traffic scheduled far in advance

• Windows of comm availability• Need to react to unexpected

events and reschedule• Missions must control own

spacecraft• Comm affects resources that

are needed for other operations

• Continual negotiation

MGS MEX Odyssey

MER A MER B

http://nssdc.gsfc.nasa.gov/thumbnail/spacecraft/mars_express.gif

20

How does DCR fit?

• Goal selection– task allocation

• Future commanding– meeting scheduling



AnalystAnalyst

PlannerPlanner

ExecutiveExecutive

ControlControl

AnalystAnalyst

PlannerPlanner

ExecutiveExecutive

ControlControl

AnalystAnalyst

PlannerPlanner

ExecutiveExecutive

ControlControl

AnalystAnalyst

PlannerPlanner

ExecutiveExecutive

ControlControl

AnalystAnalyst

PlannerPlanner

ExecutiveExecutive

ControlControl

AnalystAnalyst

PlannerPlanner

ExecutiveExecutive

ControlControl

http://nl.wikipedia.org/wiki/Afbeelding:Astronaut-EVA.jpg

21

Distributed Constraint Reasoningfor Planning & Scheduling

• Allocating events/resources to time slots (meeting scheduling)– Hannebauer and Mueller,

AAMAS 2001– Maheswaran et al.,

AAMAS 2004– Modi & Veloso, AAMAS 2005

• Coordinating plans by making coordination decisions variables– Cox et al., AAMAS 2005

A Goal

A’ C’ Goal’c

Init q

a

a

Znot(a)

z

Merge(A, A’)

Threat(A, C’, Z)(m, i)Merge(A’, A)

(m, m

)

(i)

(i)

Threat(A’, C’, Z)(i, i)

Threat(Z, A, A’)

(i, i)

Threat(A’, Goal, Z)(m, i)

(i, i)

22

ExecutiveExecutive

Planner

ExecutiveExecutive

Planner

ExecutiveExecutive

Planner

Shared Activity Coordination

Shared activities implement team plans, joint actions, and shared states/resources

=

=

=

23

Shared Activity Coordination(SHAC, Clement & Barrett, 2003)

– continual coordination algorithm– language for coordinating planning agents– framework for defining and implementing automated

interactions between planning agents (a.k.a. coordination protocols/algorithms)

– software• planner-independent interface• protocol class hierarchy• testbed for evaluating protocols

24

Shared Activity Model

• parameters (string, integer, etc.)– constraints (e.g. agent4 allows start_time [0,20], [40,50])

• decompositions (shared subplans)

• permissions - to modify parameters, move, add, delete, choose decomposition, constrain

• roles - maps each agent to a local activity

• protocols - defined for each role– change constraints– change permissions– change roles

• includes adding/removing agents assigned to activity

25

Control Protocols for a Shared Activity

• Chaos– A free-for-all among planners

• Master/Slave– The master has permissions, slaves don’t

• Round Robin– Master role passes round-robin among planners

• Asynchronous Weak Commitment (AWC)– Neediest planner becomes master

• Variations– how many planners share activity

– use of constraints

26

Asynchronous Weak CommitmentAWC::modifyPermissions()

– if have highest priority• remove self’s modification permissions (add, move,

delete)– else

• give self modification permissions

AWC::modifyConstraints()– if cannot resolve local conflicts and conflicts with constraints

of higher ranking agents• set own rank to highest rank plus one• generate parameter constraints (no-good) describing

locally consistent values

27

Experiments – Abstract Problem

• joint measurements

• capability matching

• 3-9 spacecraft

• 1-7 capabilities

• 1-9 joint goals each requiring 1-4 of each capability

28

Chaos - invalid solutions

M/S - not complete

Experimental Results(Progress over cpu time)

num

ber

of p

robl

ems

max cpu time (seconds)

AWC

RR

ChaosM/S

36

Computing Consensus Windows

Agent A Agent CAgent B1 1


2 2

Agent A

Agent B

Agent C

time execute

consensus window

highest rank decidesvoting or auction

37



2 2

Agent A

Agent B

Agent C

time execute

consensus window

voting or auction

38



2 2

Agent A

Agent B

Agent C

time execute

consensus window

voting or auction

vote

sco

llec

ted

39



2 2

Agent A

Agent B

Agent C

time execute

voting or auction

40



2 2

Agent A

Agent B

Agent C

time execute

consensus window

voting or auction

41

Causal Inconsistency

A SHAC protocol is proven sound if• the underlying planners are sound,• the protocol ensures that only one agent has permissions over

any piece of information, and• it employs causally consistent communication

ab

c

1

add

delete

add/master

update

2

7

3

add/master3

554

8 6

Order of events1. a is master and shares with (adds to roles) b2. b receives add from a3. a replaces b with c and makes c master4. c receives add message making it master5. c makes b master and removes self

(deletes)6. b receives add/master from c (before delete

from a)7. a receives update from c8. b receives delete from a

42

Summary

• Many space applications for distributed constraint reasoning

• Many involve model-based causal systems• Need to map these systems to DCRs

– how are CSPs mapped?• Need to handle

– continuous variables (including cpu)– limited computation– not 1000 computers, but 2-10– communication outages, unreliability, guarantees

Documents

Space Applications for Distributed Constraint Reasoning