# Presentation: 766015

## Coordination Challenges for Autonomous Spacecraft 

- Coordination Challenges for Autonomous Spacecraft

- Brad Clement & Tony Barrett
- Artificial Intelligence Group
- Jet Propulsion Laboratory
- California Institute of Technology
**{bclement,barrett}@aig.jpl.nasa.gov**
**http://www-aig.jpl.nasa.gov/**

## Motivation

**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**

## Outline

- What kinds of distributed missions are there?
- How does single-spacecraft autonomy evolve to adapt to distributed missions?
- How can we make multiple autonomous spacecraft work together?

## Science missions to observe phenomena
Example: Each ST-5 spacecraft has a magnetometer that measures local magnetic field strength and the mission transfers that data to a scientist’s workstation for analysis.

- Example: Each ST-5 spacecraft has a magnetometer that measures local magnetic field strength and the mission transfers that data to a scientist’s workstation for analysis.

- Signals: Magnetic field vectors
- Source: Points in the magnetosphere
- Destination: Scientist’s workstation

- Restriction to Science Missions

- Concepts not addressed here:

## Motivation for a Science Mission

- Science missions are flown to answer questions.
- What are the processes that permit and control the reconnection of magnetic field lines across collisionless plasma boundaries? (MMS)
- How does the magnetotail store, transport, and release matter and energy? (MC)
- What are the compositions of the atmospheres of terrestrial planets orbiting nearby stars? Is water or carbon dioxide present? (TPF)
- What happens near a black hole? (Constellation-X)

## Classification 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?**

## Isolation & Integrity

- Rate & Predictability

- Multiple Platform Mission Types

**Noise**

**Resolution Need**

**Low**

**High**

**High**

**Low**

**Single****Spacecraft**

- Signal
- Separation

- Signal Space
- Coverage

- Signal Combination

## Signal Separation Missions

- Motivating issues
- Separate signals from each other and/or from noise.
- Observation characteristics
- Spacecraft coordinate to take a single observation.
- Control characteristics
- Activity coordination
- Precise formation flying

**Origins Program**

**Structure & Evolution **
**of the Universe**

**NMP**

**Earth Observing System**

## Signal Space Coverage Missions

- Motivating issues
- Capture all rapidly changing signals even if unpredictable.
- Observation characteristics
- Identical spacecraft take observations  that are subsequently correlated.
- Control characteristics
- Either no coordination or coincident observations.

**Earth Observing System**

**Sun-Earth Connections**

**NMP**

## Signal (Mission) Combination

- Motivating issues
- The collection is greater than the sum of the parts.
- Observation characteristics
- Multiple mission spacecraft coordinate to improve results.
- Control characteristics
- Inter-mission coordination
- Formation knowledge

**Mars Network**

**Earth Observing System**

## Multiple Platform Issues(not a complete list)

- Signal Separation Missions
- Distributed control (e.g. formation flying)
- Distributed fault protection: SAFE-MODE unsafe
- Signal Space Coverage Missions
- Huge observation & telemetry data volume
- Commanding large populations that slowly turn heterogeneous due to wear
- Signal Combination Missions
- Distributed control for coincident observations
- Inter-mission negotiation

## How does autonomy evolve to adapt to the three different kinds of missions?

## Why Autonomy?

- Phenomena with high information rates and low predictabilities motivate autonomy.
- There is just too much data to downlink it all.
- An autonomous system identifies the important data to
- reduce downlink volume by discarding uninteresting data and
- improve information availability by performing follow-up measurements depending on the data.
- Examples of such phenomena:
- Martian imagery passing under Odyssey with evidence of water.
- Dynamic plasma interaction processes in the Magnetosphere.
*Telemetry surrounding a spacecraft anomaly...*

## Current Single-Spacecraft Autonomy Experiments

- ST-6 on TechSat21
- 3 spacecraft with an autonomous master and 2 slaves.
- Slaves have GN&C components and isolated executives.

- Three Corner Sat
- 3 spacecraft with an autonomous master and 2 slaves.
- Evolves into 3 isolated autonomous spacecraft as they loose the cross-link.

## “Standard” Operations

- Science Ops.

- Mission Ops.

- DataProducts

- Data Requests

- Raw
- Data

- Time-Tagged Commands

- DownlinkPackets

*Scientific*
*Community*

- “Standard” Operations

- Prioritized
- Observation
- Goals

- Mission & Science Ops. turn data requests into time-tagged commands for the C&DH.
- C&DH feeds commands to make GN&C  collect and downlink both observation data and spacecraft health telemetry.
- Mission Ops. evaluates telemetry and passes raw observation data to Science Ops.
- Science Ops. processes the raw data into validated data products.

## Autonomous Operations

- Science Ops.

- Mission Ops.

- DataProducts

- Data Requests

- Raw
- Data

- Planner & Analyst
- Routines

- DownlinkPackets

*Scientific*
*Community*

- ObservationPlans
- & Analysis Routines

- Mission & Science Ops. turn data requests into onboard planner and analyst routines.
- Analyst, Planner, and Exec. subsystems robustly command the spacecraft to collect- analyze-downlink interesting observations & telemetry
- Mission Ops. evaluates telemetry and passes raw observation data to Science Ops.
- Science Ops. processes the raw data into validated data products.

## Robust Executive

- Science Ops.

- Mission Ops.

- DataProducts

- Data Requests

- Raw
- Data

- Planner & Analysis
- Routines

- DownlinkPackets

*Scientific*
*Community*

- ObservationPlans
- & Analysis Routines

- Motivation
- To robustly perform required activities.
- Execution Loop
- Interpret telemetry in the context of current commands to estimate the current state.
- Send new commands to perform the current activities in the context of current state estimate.
- Go to 1.
- Technologies
- SCL, TDL
- Livingstone  Titan
- MDS
- CRL execution
- Knowledge compilation

## Planning and Scheduling

- Science Ops.

- Mission Ops.

- ObservationPlans
- & Analysis Routines

- Motivation
- Support adding activities and avoiding destructive activity interactions.
- Execution loop
- Update projection to reflect activities & current state estimate.
- Fix problems found in projection using planning routines
- Update current activities & observation plan directives and accept new prioritized activities.
- Go to 1.
- Technologies
- CASPER
- Europa

## Science Analyst

- Science Ops.

- Mission Ops.

- DataProducts

- Data Requests

- Raw
- Data

- Planner & Analysis
- Routines

- DownlinkPackets

*Scientific*
*Community*

- ObservationPlans
- & Analysis Routines

- Motivation
- Support situation dependent observation plans.
- Execution loop
- Wait for an observation plan directive.
- Execute observation script that responds to directive by analyzing new data, updating models, and generating new activities.
- Prioritize activities and pass to planner.
- Go to 1.
- Technologies
- ASC (Techsat-21)
- MISUS (rovers)

- ObservationPrioritization

- ObservationPlan Exec.

- ObservationPlan Directives

- New
- Activities

- Analysis Routines

- Observation Data&
- Experiment Models

## How to Distribute?

*Who gets which components?*

## 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.

## 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.  Planners must coordinate over collection.

## Autonomous 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.

## Working Together: The General Problem

- The ultimate motivation for any technology:
- Reduce cost while increasing value
- An underlying motivation for autonomy
- Reduce risk & cost while increasing data value
- The general underlying problem for distributed autonomy:
- Maximize some global value function subject to local constraints.

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

- Distributed Constrained Optimization

## Coordinating Analysts

**Problem**
- Maximize total downlinked data value subject to local planning restrictions.
- The value of an observation depends on both local properties and properties of observations made by others.
**Approach**
- Compute & combine local models of phenomena to focus global data collection
- ASC, MISUS
**Challenges**
- Constrained communication of large images
- Constrained processing computation
- Metrics for evaluating science value within and across missions
- Uncertainty in value of observations due to noise and resolution

**Local Model **
**Updates**

**Collect **
**Summaries**

**Compute ****Global Model**

**Disseminate ****Global Model**

**...**

## Coordinating Planners

**Problem**
- Maximize priority weighted sum of satisfied goals subject to local constraints.
**Approaches**
- Goal delegation with abstract planning and/or contract networks.
- Schedule coordination with distributed constraint satisfaction based algorithms
- ShAC, MISUS
**Challenges**
- Local, shared, and exogenous activities with temporal duration
- Complex local and shared states and resources
- Constrained communication in windows
- Inter- and intra-mission metrics
- Limited computational resources
- Uncertainty - breaking team commitments
- Protocols for cooperation and negotiation
- Establishing consensus before execution

## Coordinating Executives

- Reconfiguration

- Distributed ModeEstimation

- Telemetry

- Commands

- Current State
- Estimate

- Current
- Activities

**Estimation Problem**
- Find the local/global mode with the maximal probability subject to local observation constraints.
**Reconfiguration Problem**
- Find the coordinated reconfiguration paths to the target mode that has the cheapest resource cost subject to local constraints.
**Challenges**
- Coordinating communication, diagnosis, processing and commanding for measurements
- Local and shared resources
- Uncertainty in timing, state, and resource consumption
- Recovering from failure

## Coordinating Controllers

- Problem
- Coordinate multiple local and global behaviors for optimal control.
- Approach
- Subscription-based communications between behaviors on different platforms.
- Priority & state-based behavior arbitration
- Behavior fusion based on consensus computation mechanisms

*Campout Architecture*

## Conclusions

- What kinds of distributed missions are there?
- (1) Separation, (2) Coverage, (3) Combination
- How does autonomy evolve to adapt to a distributed mission?
- It depends on the kind of mission, which maps to a component distribution.
- How can we make multiple autonomous spacecraft work together?
- Getting components to work together is an instance of a constrained optimization problem.
- Technologies must address the challenges posed at different levels of control.

## Extra Directions for Improvement

- Capabilities
- Ground operations control of autonomous systems
- Fairly arbitrating conflicting demands from different instrument teams, possibly on different missions
- Functioning in a low power (picosat) environment
- Autonomous artifact building
- Learning to improve coordination
- Getting above TRL 3
- Flight-like multiple platform testbeds are needed to evaluate actual performance of different techniques.
- System interfaces are currently adhocly defined.
- Not enough communication between research activities.