# Presentation: 024854

## AUTONOMY LEVELS FOR UNMANNED SYSTEMS (ALFUS)

## ALFUS OBJECTIVES

- 	Framework to facilitate characterizing and articulating autonomy for unmanned systems:
- Standard terms and definitions for requirements specification
- Metrics, processes, and tools for evaluation/measurement

## Generic framework covering all UMSs.
From remote control through full and intelligent autonomy.
From single UMS low level operational behavior to joint missions.

- From remote control through full and intelligent autonomy.
- From single UMS low level operational behavior to joint missions.

**ALFUS SCOPE**

## HISTORY

- Stage I:	Started in 2003 as Cross-Government Ad Hoc Workgroup.  Published Terminology.
- Stage II:	Ongoing collaboration with FCS.  Published Framework.
- Stage III:	January 2008, SAE AS4-D Unmanned Systems Committee Performance Measures Subcommittee

## ALFUS CHARACTERISTICS

- Metrics based—measurable levels with smooth transitions
- Multiple layers of abstraction for autonomy requirements and capabilities
- Basis for a general performance metrics framework for unmanned systems

## AUTONOMY

- A UMS’s own ability of sensing, perceiving, analyzing, communicating, planning, decision-making, and acting/executing, to achieve its goals as assigned by its human operator(s) through designed HRI.

## ALFUS FRAMEWORK

## ALFUS METRICS

**Mission Complexity**
- Subtasks, decision
- Organization, collaboration
- Performance
- Situation awareness, knowledge requirements

**Environmental Complexity**
-   Solution ratios on:
- Terrain variation
- Object frequency, density, intent
- Climate
- Mobility constraints
- Communication dependencies

**Human Independence**
- Frequency, duration, robot initiated interactions
- Workload, skill levels
- Operator to UMS ratio

**UGV-1**

**UMS team Alpha**

## LAYERS OF DETAIL

***Contextual Autonomous Capability:***
***- Mission/Task/UMS*** 	***Autonomy***
***- Mission Complexity***
***- Environmental ******	******Complexity***

***- Metrics Groups***
***- Root Autonomous ******	******Capability ******	******Autonomy***

***Metric Decomposition***

## CAC AXES

## ALFUS FRAMEWORK Illustrative Application

**ALFUS FRAMEWORK ****Illustrative Application**

## Slide 12

## ALFUS ILLUSTRATION 

- Remote control

- Full, intelligent autonomy

- 100% HRI

- approaching 0 HRI
- highest complexity, all missions
- extreme environment

- 1

- 2

- 4

- 5

- 7

- 8

- 9

- 10

- 0

**ALFUS ILLUSTRATION **

**Autonomy Level**

- mid level HRI
- mid complexity, multi-functional missions
- moderate environment

- high level HRI
- low level tactical behavior
- simple environment

- 3

- low level HRI
- collaborative, high complexity missions
- difficult environment

- 6

## ALFUS MODELCOMBINED MISSION AND ENVIRONMENT

## ALFUS FRAMEWORK APPLICATION

**ALFUS FRAMEWORK APPLICATION**

## ALFUS FRAMEWORK

## ALFUS GENERALIZATIONPerformance Test Methods

## 	Framework to facilitate characterizing/ articulating autonomy levels for unmanned systems:
Generic framework covering all UMSs.
From remote control through full autonomy.
From single UMS to team to joint missions.

- Generic framework covering all UMSs.
- From remote control through full autonomy.
- From single UMS to team to joint missions.

**SUMMARY**

- _[http://www.isd.mel.nist.gov/projects/autonomy_levels/](http://www.isd.mel.nist.gov/projects/autonomy_levels/)__ _

## BACKUPS

## SUMMARY MODEL FORMAT (1)

**Level and Descriptor**

**Capability**

**Metrics and Definition**

## PARTICIPATION (partial)

**DOD – AATD*, AFRL, AMRDEC*, ARL*, MANCEN*, CERDEC*, DARPA, NAVAIR, NSWC, OSD/JPO, TARDEC*, TRADOC*, ...**
**DOC – NIST**
**DOE – HQ, INEEL**
**DOT – FHWA**
**Industry**
**Collaborations with AIAA, JAUS WG, NASA, PerMIS, ...**
***U.S. Army**

## COTRIBUTORS (partial)

- The following, in alphabetical order, contributed to the ALFUS Framework effort: **Air Force**:  Bruce Clough, Robert Smith, Jeff Wit.  **Army (inc. AATD, AMRDEC, ARL, CERDEC, MENCEN, TARDEC, ****TSM FCS,**** UAMBL)**: Curt Adams, Keith Arthur, Robert Barnhill, Bruce Brendle, Marsha Cagle-West, Jeff Cerny, Sanjiv Dungrani, Mike Dzugan, Woody English, Bill Fedak, Ray Higgins, Susan Hill, Julie Hirtz, Kelley Hodge, Jeffrey Jaczkowski, Robert Kania, David Knichel, Brian Maijala, Peter Melick, Brian Novak, Kerry Pavek, Richard Pena, Jason Pusey, John Rovegno, Kent Schvaneveldt, Charles Shoemaker, Stephen Swan, David Thomas, Terrance Tierney, Robert Wade.  **DARPA**:  LTC Gerrie Gage, Doug Gage, Dennis Overstreet. **DOE**: David Bruemmer, Tom Weber. **DOT** **FHWA**:  Robert Ferlis, Peter Huang. **Industry (inc. FCS)**:  Thomas Adams, Thomas Altshuler, John Bergman, Charles Bishop, Dale Fleck, William Klarquist, Mark Peot, Dan Rodgers, Chiraq Tasker. **IDA**: Julianna Connelly, David Sparrow. **NASA**:  Jeremy Hart, Ryan Proud. **Navy**:  Darryl Brayman, Eric Hansen, Caesar Mamplata, Marc Steinburg. **NIST**:  James Albus, Brian Antonishek, Tony Barbera, Maris Juberts, Elena Messina, Jean Scholtz, Harry Scott, Albert Wavering. **OSD**:  Richard Abraham, Keith Anderson, Jeffrey Kotora. **apologize for the omitted contributors.**
- Project Funding:  	DHS and NIST

## Each Level of Autonomy Scale is broken into 8 levels. The levels for the Decide functions are shown.

- More Autonomy

- Less Autonomy

- Existent Work--NASA SMART

## EXISTENT WORKEssential Foundations

- OODA -- Observe, Orient, Decide, Act.
- 4D/RCS Reference Architecture
- Generic Node consistent with OODA
- System Hierarchy harmonizes many, many OODA nodes.
- Hierarchical Task Decomposition organizes missions of different complexity.

## EXISTENT WORK

## EXISTENT WORK“Sheridan” Model 

- 1) Computer offers no assistance, human must do it all.
- 2) Computer offers a complete set of action alternatives, and
- 3) narrows the selection down to a few, or
- 4) suggests one, and
- 5) executes that suggestion if the human approves, or
- 6) allows the human a restricted time to veto before automatic execution, or
- 7) executes automatically, then necessarily informs the human, or
- 8) informs him after execution only if he asks, or
- 9) informs him after execution if it, the computer, decides to.
- 10) Computer decides everything and acts autonomously, ignoring the human.

## EXISTENT WORKArmy Science Board Study

- 0. Manual remote control, like a remote controlled toy
- 1. Simple automation
- 2. Automated tasks and functions, like a Hunter
- 3. Scripted mission, like an Shadow or Predator UAV
- 4. Semi-automated missions with simple decision making, like an Cruise Missile
- 5. Complex missions-specific reasoning
- 6. Dynamically mission adaptable
- 7. Synergistic multi-mission reasoning
- 8. Human-like autonomy in a mixed team
- 9. Autonomous teams with unmanned leader or mission manager
- 10. Autonomous conglomerate.

## Existent Work

## Intervention frequency/duration
Robot initiation percentage
Operator workload
Operator to UMS ratio
Operator Skill

- Robot initiation percentage
- Operator workload
- Operator to UMS ratio
- Operator Skill

**ALFUS GENERIC FRAMEWORK****Detailed Model**** **** ****Characterization of Human Independence**

## static: terrain, soil, water,
dynamic: frequency/density/types of objects 
electronic/electromagnetic
urban: traffic, road, barriers, controlling devices
rural: vegetation, biologics,
weather: climate, lighting, temperature,   
operational: threats, decoy, mapping, mobility,

- dynamic: frequency/density/types of objects
- electronic/electromagnetic
- urban: traffic, road, barriers, controlling devices
- rural: vegetation, biologics,
- weather: climate, lighting, temperature,
- operational: threats, decoy, mapping, mobility,

- ALFUS GENERIC FRAMEWORKDetailed Model  Characterization of Environmental Complexity

## Autonomy Level Algorithms mission complexity axis 

**1.****	**“Serial”:  Add values for all metrics and scale them from 0 to 10.
- Advantage:  simple and mechanic,
- Drawback:  final number may be misleading or vague. For example, a level 7 may be a result of very high subordinate level numbers but very poor latency and that may not be what the user wanted.
- 2. “Parallel”:  line up all the metric values and pick the lowest.
- Advantage:  assurance
- Drawback: over constraining, high cost for ums development.
- 3.	“Hybrid”: Categorize mission metrics into: O, O, D, and A.  Use serial within each category but parallel for the 4 categories?

## ALFUS FRAMEWORKClarification

- Autonomy vs. Automation
- -  washing machine vs. scouting mission
- -  human-less operation vs. human-like performance

## Some possible uses of the framework

- Classify environments, HRI systems, or missions
- Missions can be certified with the other two axes, e.g., perform mission A in level N environment, mission B is designed to operate with level M HRI, etc.
- Detailed metrics could be used as general performance metrics for intelligent, unmanned systems.

## JOINT MISSIONS

- mission integration

**MISSION TYPES**

- mobility

- RSTA

- UGV

- UAV

- UUV

- maritime reconn

- sub track and trail

- scan

- react to threats

## Workshop Accomplishments

- Inaugural Workshop (July 18, 2003, NIST)
- 	Established Working Group Objectives
- Second (September 11, 2003, BWI)
- 	Identified Terms and Started Definitions
- Third (November 22, 2003, SRS Tech., Arlington, VA)
- 	Identified Metrics, Terminology Published
- Fourth (February 25-26, 2004, Titan Sys., Huntsville, AL)
- 	Identified Summary Model Representation

## Workshop Accomplishments

- Fifth (May 3-4, 2004, Atlanta Airport, GA)
***	*****Metrics and Measures Presented.*****  *****Began Interaction with FCS**
- Sixth (July 28-29, 2004, FCS LSI, Huntsville, GA)
**	****Tool Conceptualized. Exit Strategy Planned.**
- Seventh (October 19-20, 2004, AFRL, Dayton, Ohio)
**	****Tool Updated. Began Summary Model.**
- Eighth (February 8 - 9, 2005, NIST, Gaithersburg, Maryland)
**	****Continued Developing Models.  NIST 4D/RCS Task Analysis Method Presented. DOT ITS Briefed.**

## Workshop Accomplishments

- Ninth (May 4-5, 2005, TARDEC, Warren, Michigan)
***	*****Focused on Metric Scale Development. TARDEC Programs Presented. ASBS and UACO Programs Briefed.**** **
- Tenth (July 20-21, U.S.Army Futures Center Forward, Arlington, Virginia)
***	*****Further***** *****Development on Metric Scales.  Additional Representation Presented.**