# Presentation: 001027

## AUTONOMY LEVELS FOR UNMANNED SYSTEMS (ALFUS)OVERVIEW

## BACKGROUND AND NEEDSForum

- Autonomy Levels for Unmanned Systems (ALFUS)
- An Ad Hoc Working Group

## ALFUS OBJECTIVESUser Needs

- Standard terms and definitions for characterizing the levels of autonomy for unmanned systems.
- Metrics, methods, and processes for measuring autonomy of unmanned systems.

- From what we heard from Army users, they need approach for them to specify auto vhs, std vacab & std def for auto lvls.
- army, af wanting to specify or evaluate ums. Helpful if there are std def and/or methods to set up metrics

## Participation

**DOD – AATD*, AFRL, AMRDEC*, ARL*, MANCEN*, CERDEC*, DARPA, NAVAIR, NRL, OSD/JPO, TARDEC*, TRADOC*, UAMBL*, ...**
**DOC – NIST**
**DOE – HQ, INEEL**
**DOT – FHWA**
**Collaborations with JAUS WG, NASA, PerMIS, ...**
**Documents provided for AIAA, NATO, American Helicopter Society, ...**
***U.S. Army**

- MOTIVATION, NEEDS, OBJECTIVES

## ALFUS FRAMEWORK Autonomy

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

**huang:**
- perceive, analyze, plan, decide, act, not ants and bugs

## ALFUS FRAMEWORK

**hhuang:**
- For prgs, some mtrcs more important

## Generic framework covering all UMSs.
From remote control through full autonomy.
From single UMS tactical behavior to joint missions.

- From remote control through full autonomy.
- From single UMS tactical behavior to joint missions.

**ALFUS FRAMEWORK SCOPE**

## 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 Plan.
- Seventh (October 19-20, 2004, AFRL, Dayton, Ohio)
- 	Tool Updated. Began Summary Model.

## ALFUS PRODUCTS

- Volume 1:  Terminology
- Volume 2:  Framework White Paper
- Volume 3:  Executive/Summary Model:  			Definitions, Descriptors, Guidelines
- Volume 4:  Detailed Model:  Tool and Manuals

## TERMINOLOGY

**Autonomy Levels for Unmanned Systems**
**(ALFUS) Framework**
**Volume I: Terminology**
**Version 1.1**
- NIST Special Publication 1011

## AUTONOMY LEVELS FOR UNMANNED SYSTEMS (ALFUS) FRAMEWORK

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

**huang:**
- UMS pf, level or general, can be measured with 3 aspects, types of env it can operate in, mission that it can perform, and human interaction levels.
- hidden behind the simple scales needs a complex model FACTORS AFFECTING ALFUS

**huang:**
- not ready to conclude that less HRI necessarily correspond to higher level of autonomy, HRI may enhance autonomy

## DETAILED MODEL TOOL

- Headed by Charlie Bishop of LSI.
- Compute autonomy level in spreadsheet based  on metrics.
- Use ANS as an example.
- Apply the NIST scenario.

## GENERIC ALFUS FRAMEWORKSummary Model

- A 0 – 10 (or 1 – 10) scale indicating Autonomy Levels.

## SUMMARY MODEL FORMAT(draft)

- Level and descriptor

- Capability

- Metrics and Definition

## ALFUS Framework Summary Model

**huang:**
- from 3 sets of detailed metrics, there needs to be processes that integrate them to form summarized indices.

## ALFUS FRAMEWORK Illustrative Application

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

## SUMMARY MODELalternative 2:

- The summary model becomes focused largely on the mission complexity or another axis that fits the Program requirements best.

## Slide 19

## Some possible, additional 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.

## Generic framework covering all UMSs.
From remote control through full autonomy.
From single UMS to team to joint missions.
End products to be mission/goal/task/system specific.

- From remote control through full autonomy.
- From single UMS to team to joint missions.
- End products to be mission/goal/task/system specific.

- SUMMARY

- _[http://www.isd.mel.nist.gov/projects/autonomy_levels/](http://www.isd.mel.nist.gov/projects/autonomy_levels/)_
- _[hui-min.huang@nist.gov](mailto:hui-min.huang@nist.gov)_

## backups

## 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

- Intelligent tactical behavior
- Cooperative behavior
- Payload mission behavior
- O.A.
- Off-road mobility
- Road following
- Mission control (operator)
- Remote control

*low*

*high*

- Full autonomy, human level pf
- Collaborative operations
- Coordinated operations
- On/off road, moving objects
- O.A.
- Pre-planned missions
- Remote control

- Full autonomy
- Battlespace (Bsp) swarm cognizance
- Bsp cognizance
- Bsp knowledge
- multi vh cooperation
- multi-vh coordination
- Event adaptive
- Preplanned mission
- Remote control

**huang:**
- you see general corrspndnces among
- issue is whether a generic suporting all, whether a gen model for people developspecific levels

## 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

## 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

## GENERIC ALFUS FRAMEWORK Terminology

- Generic
- 	autonomy, environment, fusion, human robot interface (HRI), mission planning, mode of operation, perception, robotic follower, situation awareness, task decomposition, unmanned system

- Domain Specific
- 	cooperative engagement, sensor to shooter, unattended ground sensors (UGS)

## ALFUS GENERIC FRAMEWORKDetailed Model  Characterization of Mission Complexity 

- Tactical Behavior
- subtask types, commanding levels
- decision points
- Coordination/Collaboration
- teaming
- interfaces--data types,frequencies, interdependency/idling time
- Performance
- planning capability, prior/during/post mission
- allowed latencies and errors, success rate
- Sensory Processing/World Modeling
- situation awareness required
- information dependency

## 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 FRAMEWORKDetailed Model  Characterization of Human-Robot Interaction** **

## 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 Difficulty

**hhuang:**
- Show its file

## ALFUS FRAMEWORKClarification

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

**huang:**
- based on def, are the two same?
- mission complexity, not understanding types of clothes, spinning cycles, detergent consumption rates, etc.

## JOINT MISSIONS

- mission integration

**MISSION TYPES**

- mobility

- RSTA

- UGV

- UAV

- UUV

- maritime reconn

- sub track and trail

- scan

- react to threats

## Examples of Levels of Discourse and Independence

**Multiple vehicle cooperation, high level of task knowledge, high tasking uncertainty, large spatial and temporal scope**

**Single vehicle, multiple subsystems, medium level of task knowledge, high uncertainty, medium spatial and temporal scope**

**Single subsystem, low level of task knowledge, medium-low uncertainty, medium-small spatial and temporal scope**

**Single subsystem, no task knowledge, very low uncertainty, small spatial and temporal scope**