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Adaptive Resource Modelling for Autonomous Planning & Scheduling
Andrew Carrel, Phil Palmer Surrey Space Centre, University of Surrey, UK

5th International Workshop on Planning and Scheduling for Space Baltimore, 24th October 2006


Contents Resources types in operations planning. The NEAT planner/scheduler. Example applications of NEAT. Basic adaptation for planning. Neural network resource modelling. Test results. Conclusions & future work plans.
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Planning with Resources User goals achieved by operations. Operations are groups of activities. Activities have temporal constraints. Activities use resources:
Non-depletable resources Depletable resources

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NEAT Planning/Scheduling NEAT (Near-optimal Evolutionary Autonomous Task-manager) designed for onboard autonomy.
Evolving Population fittest member Schedule Construction

Valid activity schedule: commands for next cycle
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discard
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Evolutionary Optimisation
Chromosomes are operation permutations. Schedules are constructed using backtracking. Only valid schedules are generated. Evolutionary search never terminates.
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Population of Chromosomes Schedule Construction Fitness Function Parent Selection Crossover & Mutation

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Test Scenarios Mars Rover
Mobile platform visiting science targets Battery charge & memory are depletable resources

UK-DMC Imaging Satellite
LEO, Sun-synchronous orbit Battery charge & 2 в memory are depletable resources
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Rover Scenario

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Rover Scenario

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UK-DMC Imaging Scenario

94 targets taken from imaging log for September 2004. NEAT manages resources effectively. NEAT shows 31% improvement in science return.
120 100 80 60 40 20 0 Existing P l an n i n g NEAT

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Dependency on Models Planning ahead requires estimating future resource levels. Projection of resources needs:
1. 2.

Values for current resource levels. Models that estimate the effect of future activities on resources levels.

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When the model is wrong? Two examples tested on the UK-DMC scenario:
1.

2.

Damaged solar array producing less power than expected. Damaged antenna downlinks data at a much lower rate than expected.

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Damaged Solar Array Open Loop:
100 95

Basic Feedback:
100 95

Battery Charge (%)
0 1 2 3 4 5 6 7 8 9 10

Battery Charge (%)

90

90

85

85

80

80

75

75

70

70

0

1

2

3

4

5

6

7

8

9

10

Time (days)

Time (days)

damage occurs
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damage occurs
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Damaged Antenna Open Loop:
Failed Images
6 4 2 0

0

1

2

3

4

5

6

7

8

9

10

Basic Feedback:
Failed Images
6 4 2 0

Time (days)

0

1

2

3

4

5

6

7

8

9

10

Time (days)

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Neural Network Learning

Evolving Population

Schedule Construction
Model corrections commands

resource Spacecraft data

Neural Network
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Neural Network Learning Inputs: Model estimates
Depletable resource rates Non-depletable resources
a x
1

u

1

a

2

u2

Outputs: Model corrections
Depletable resource rates Non-depletable resources

inputs W
1

outputs W hidden layer
2

Training method: Back-propagation.
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Damaged Solar Array

Battery Charge (%)

Neural network a d a p ts r e s o u r c e model over period of <1.5 days. Resource management fully recovered.

100

95

90

85

80

75

70

0

1

2

3

4

5

6

7

8

9

10

T ime (days)

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Damaged Antenna

Failed image capture activities completely eliminated. Neural network successfully models reduction in downlink c a p a c ity .

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Conclusions Autonomous planning/scheduling is very dependent on resource models. A neural network can be used effectively to capture consistent deviations in resource behaviour. NEAT can use this to adapt future plans as resource behaviour changes.

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Future Work Test this method on the rover scenario (e.g. damaged wheel/motor). Investigate alternative neural network inputs (e.g. subsystem on/off, modes). Assess tolerance to noise in resource level measurements.

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Thank you for listening.
Andrew Carrel University of Surrey a.carrel@surrey.ac.uk