Transcript PhD Defense

PhD Defense
An Adaptive Planning Framework for
Situation Assessment and Decision-making
on an Autonomous Ground Vehicle
November 2, 2006
Bob Touchton, P.E.
Outline
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Introduction and Background
Adaptive Planning Framework
Reference Implementation
Field Testing
Future Research and Conclusions
Questions and Discussion
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Objectives of this Presentation
• Describe the Adaptive Planning Framework
(APF) concept and its Reference Implementation
• Demonstrate that the APF is a new and unique
approach to intelligent behavior and that the
research results are meaningful and useful
• Reinforce how the APF makes an important
contribution to the autonomous robotics
research community
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Motivation and
Statement of Problem
• Real-time (re)planning and decision-making is a
daunting issue, especially for complex missions
• Deliberation time vs. reaction time conundrum
• Specific robotic technologies and specific autonomous
behaviors are becoming reasonably robust
• Deriving situational knowledge from dynamic inputs and
autonomously selecting, sequencing, and controlling
behavior based on that situational knowledge are
essential capabilities for advanced applications
• A disciplined way of thinking about, organizing, and
applying situational knowledge to high-level planning
and decision-making is needed
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Thesis of Research
A well-organized, disciplined 3-stage, real-time
process that enables an AGV to:
– Understand the current situation
– Understand the suitability and viability of available
behavioral capabilities given that situation
– Make and execute plan-related decisions
provides new levels of intelligence and autonomy
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
The Adaptive Planning Framework (APF)
• A skeletal structure for enhancing AGV behavioral
intelligence
• Situational knowledge bridges the gap between
changing input data and AGV response
• Represented in terms of “Findings”
• Reported in terms of “Conditions,” “States,” “Events,”
and “Recommendations”
• Organized by virtual Situation Assessment Specialists,
Behavior Specialists and a Decision Specialist
• Empowered to manage the execution and modification of
the AGV high-level behavior
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
APF Specialists
• Situation Assessment Specialists: convert raw
input and derived knowledge (i.e., prior Findings
of itself and others) into new Findings
• Behavior Specialists: each paired with a
behavioral component to render
Recommendations on its suitability, capabilities,
dependencies and status
• Decision Specialist: a Decision Broker charged
with governing high-level AGV behavior based
on Findings and Recommendations
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Adaptive Planning Framework Conceptual Model
Situation
Assessment
Specialists
Planning
Specialists
Mission (incl. goal
attainment)
Deliberative
Planning
Plan Segment (incl.
goal attainment)
Reactive Behavior
Environment
Receding Horizon
Decision
Specialist
Decision Broker
Model-based
Planning
Sensors
Example-Based
Planning
Threats
Contingency
Planning
WMKS may be partitioned and
distributed to specialists
World
Model
Knowledge
Store
Vehicle
Incl. Specialists’
findings, objects, facts,
traversability
grids
Information Bus
(publishers write,
all others read)
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APF Knowledge Flow
Data
Finding
Finding
Data
Data
S
A
Data
S
p
e
c
Finding
Finding
Finding
Recommendation
B
e
h
S
p
e
c
Recommendation
Recommendation
D
e
c
Recommendation
S
p
e
c
Finding
Action
Data
Data
Data
Finding
?
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Inspiration: REALM Expert System
and its Cooperating Experts
• Reactor Emergency Action Level Monitor
• Developed for Electric Power Research Institute
to Assist in Nuclear Power Plant Emergency
Management in late 1980’s
• Expert System that used Collaboration of
Experts Modeled After the Technical Support
Group
• Fielded at Indian Point #2 (ConEdison) with
Real-time Sensor Feed from Plant Computer
• Demonstrated During Emergency Exercises
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Solution Metaphor
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Literature Review
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Architectural Compatibility
Situation Assessment
Behaviors
Decision-making
Knowledge Representation
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Publications
• Journal of Field Robotics (3rd author) – Sept ’06
• IEEE Computer (1st author) – Dec ’06
• Journal of Field Robotics (1st author) – planned
summary of this dissertation
• Influence JAUS Mission Generation Component
and Ontology initiatives currently under way at
the JAUS Working Group
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Research Milestones
• The Adaptive Planning Framework Initial Design
• Proof of Concept Prototype
• Early Implementation on the DGC2005
NaviGATOR
• The Adaptive Planning Framework Final Design
• Reference Implementation based on Milestone I
of the Team Gator Nation Urban Challenge
Project Plan
• Field Testing of Reference Implementation
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
The Adaptive Planning Framework
Final Design
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Findings
Specialists
Decision Broker Protocols
Knowledge Engineering Tools
Run-time Implementation of Design-time
Features
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
APF Findings
• Conditions, States, Recommendations, and
Events
• Derived or inferred using raw data, refined data,
Meta Data, command inputs, previous Findings
• Must be uniquely named within their namespace
• Must be assigned to exactly one Specialist
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
APF Conditions
• An independent, ongoing circumstance whose
value is either present or absent
• Only prove present
• Think of medical diagnostics… if what you care
about is whether a particular symptom is present
(like a rash), then make it a Condition
• Examples:
– Close-Range-Obstacle
– Excessive-Roll
– Adjacent-Lane-Safe
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
APF States and Recommendations
• Finding that has exactly 1 of 2 or more enumerated
values
• The value of a State only changes when conclusive
evidence of another value is found (i.e., focus on
state transitions)
• A Recommendation is a special type whose output
is in the form of advice, especially regarding its
associated Behavior
• Prioritization and a default value are allowed if that
helps to avoid/resolve ambiguities
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
APF States and Recommendations
• State Examples:
– Terrain is {Smooth | Rugged | Very-Rugged}
– Mission-Status is {Ahead-of-Schedule | Nominal |
Behind-Schedule}
– Mission-Mode is {Optimize-Speed | Optimize-Risk}
– Mobility-Mode is {Low-Speed | High-Speed}
• Recommendation Examples:
– Passing-Behavior is {OK | Not-Appropriate | Not-Legal |
Unsafe}
– Roadway-Navigation-Behavior is {OK | Blocked | Stuck |
Unsafe}
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
APF Events
• Finding whose mere occurrence is of interest
• Its value is set to true, with a time stamp
• The Event would still be reported as true @ timestamp,
even after the evidence of it occurrence is no longer
available
• Needs an expiration time or event reset rule to set it
back to false
• Allows reasoning about occurrences after their evidence
is gone and about the duration-of or time-since an event
• Examples:
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Enemy-Fire-Detected
Air-Conditioner-Failed
GPS-Signal-Lost
Intersection-Became-Clear
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
APF Situation Assessment Specialists
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Organized into categories
Must have a unique name
Must be responsible for one or more Findings
Purpose is for discipline and team assignment
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Example Situation Assessment
Specialists
SA Categories
Typical SA Specialists
Typical Situational Findings
Mission
Mission Goal Specialist
Mission Progress Specialist
Mission Goal State {Behind | Nominal | Ahead}
Mission Status {Waiting | In-Progress | Failed | Complete}
Mission Type {Seek-goal | Wander | Cover-Area}
Plan Segment
Boundary Specialist
Plan Element Specialist
Vehicle State {In Bounds | In Fringe | Out of Bounds}
Plan Segment Status {Waiting | In-progress | Complete}
Plan Segment Type {Navigate | Park | Retrieve-item}
Roadway
Terrain Specialist
Roadway Law Specialist
Roadway Convention Specialist
Terrain State {Smooth | Rugged | Very Rugged}
Legal to Pass Condition {Present | Absent}
Appropriate to Pass Condition {Present | Absent}
Mobility
Mobility Specialist
Mobility State {Operational | Stuck | Blocked}
Mobility Type {Cruising | Creeping | Waiting}
Intersection
Intersection Specialist
Intersection-Clear Event {True @ timestamp | False}
Intersection Type {Right-of-way | 2-way | 3-way | 4-way}
Obstacles
Close Range Safety Specialist
Close Range Left-Side-Safe Condition {Present | Absent}
Forward-Left-Safe Condition {Present | Absent}
Long Range Obstacle Condition {Present | Absent}
Long Range Safety Specialist
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
APF Behavior Specialists
• A Behavior Specialist (BS) is allocated for each
distinct Behavioral Component
• Each BS renders Recommendations and other
Findings regarding the performance and
suitability of its assigned Component
• It thus must understand the Behavior
Component’s constraints, requirements,
strengths, and weaknesses
• Typically will be embedded into its assigned
Behavior Component
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
APF Decision Specialist
• The Decision Broker assumes ultimate authority over
AGV’s autonomous behavior (called Subsystem
Commander in JAUS)
• Makes decisions about AGV behavior based on
Recommendations and other inputs
• Uses (currently 7) Decision Primitives to execute
Decision Protocols:
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Monitor a behavior
Verify a behavior
Enable a behavior
Disable a behavior
Set (maximum) travel speed
Wait
Execute another Protocol
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Knowledge Engineering Tools
• Behavior Use Case
• Findings Worksheet
• Decision Broker Protocol Worksheet
We will look at examples of these tools as part
of the Reference Implementation
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
APF Run-time
Concept of Operation
• Each Specialist Executes Independently
– Processes Algorithm/Rules
– Produces Findings/Actions
• Centralized Repository
– Blackboard
– Knowledge Store
• Decentralized Repository
– Broadcast
– Publish/Subscribe (point-to-point)
• May be event-driven/change-driven
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
APF Run-time
Reasoning and Control Strategy
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Asynchronous
Distributed
Iterative
Forward-chaining
May require output dampening or hysteresis to
avoid thrashing
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Reference Implementation
• Sketch out initial design for the DARPA Urban Challenge
behaviors
• Design/Develop/Deploy First Phase of DARPA Urban
Challenge deliverable
– Autonomously select between basic Roadway Navigation
behavior and n-Point Turn behavior (addresses a blockage of the
preplanned route)
– Add Specialists and their Findings as required
– Create a JAUS Subsystem Commander component and
incorporate the Decision Broker into it
• Modify/extend JAUS infrastructure and messaging
system accordingly
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Situation
Assessment
Specialists
Behavior
Specialists
Decision
Specialist
The APF Conceptual Model
Mission Planning
(high level)
Mission
Roadway Navigation
(current mission,
% attainment, progress,
priority, status)
(fixed/moving obstacle
avoidance (OA), seek
goal, find/stay in lane,
follow/queue behind
vehicle, merge with
vehicles, obey speed
limit)
Plan Segment
(current segment,
% attainment, progress,
priority, status)
Open Area
Mobility
(fixed/moving OA, seek
goal, obey speed limit)
(stuck, blocked, waiting,
creeping, cruising)
Parking
Roadway
(find/take/exit space)
(laws, safety,
conventions, roadway
condition/type)
Reverse Direction
(U-turn/n-point-turn)
(monitor/verify
behaviors, enable/
disable behavior(s), set
speed limit)
Handle Intersection
Intersection
(laws, safety,
conventions, intersection
condition/type)
(stop?, wait turn, check
legal, check safe, go or
turn, merge, fixed/
moving OA)
Obstacles
Passing
(close-range, mid-range,
long-range, fixed,
moving, oncoming)
(fixed/moving OA,
change lane, overtake,
change lane)
World
Model
Knowledge
Store
WMKS may be partitioned and
distributed to specialists.
Point-to-point delivery of
information may be used in lieu
of centralized knowledge store.
Decision Broker
Including
Specialists’ Findings,
objects, facts, Meta Data,
traversability
grids
Information Bus
(publishers write,
all others read)
?
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
APF Initial Design for DGC2007
High Level
Mission Planning and Re-Planning
Mission
Spooler
Situation Assessment
Specialists
Primitive
Driver
Primary Behaviors
Primitive Behaviors
Roadway Navigation
fixed and moving OA, seek goal,
find lane, stay in lane, follow
vehicle, merge, obey speed limit
Roadway Navigation
Avoid Obstacles
Queue
Find Same Lane
Merge
Feedback
Find Adjacent Lane
Plan Segement
current segment, % attained,
progress, priority, status
Roadway
laws, safety, conventions,
roadway condition & type
Selected
Behavior
Behavior
Specialists
Mission
current mission % attained,
progress, priority, status
Mobility
stuck, blocked, stopped,
creeping, cruising
a priori data
RNDF
MDF
World Model
Open Area
fixed and moving OA, seek goal,
obey speed limit
J
A
U
S
Parking Lot
find space, take space, exit space
n-Point Turn
Reverse Direction
B
U
S
Intersection
laws, safety, conventions,
intersection condition & type
Intersection
stop, wait turn, check if legal, check
if safe, go or turn, merge, fixed and
moving OA
Obstacles
close range, mid range, long
range, oncoming
Passing Maneuver
fixed and moving OA, change lane,
overtake, change lane
Open Area
Determine Proper Lane
Actuators
Stay in Lane
Decision
Broker
Sub-System
Commander
monitor and
select current
behavior(s),
set internal
speed limit,
request new
high level
plan
Follow Safely
Parking Lot
J
A
U
S
Obey Speed Limits
Seek/Take/Exit Space
n-Point Turn
Seek Goal Cell (grid)
Intersection
Seek Stop Sign/Marker
Seek Intersection
B
U
S
Vehicle
Check Intersection
Emergency Braking
Passing Maneuver
Smart
Arbiter 1
Smart
Arbiter 2
Defensive Driving
Smart
Arbiter 3
Smart
Arbiter 4
World
JAUS Bus
Path
Smart Sensor
TYSS
Traversability
Smart Sensor
Moving Objects
(LADAR)
Smart Sensor
Lane Finder
(Streets)
Smart Sensor
Path Finder
(Dirt Roads)
Smart Sensor
Moving Objects
(Cameras)
Smart Sensor
Global
Position
Sensor
Velocity State
Sensor
Data
Logger
Sensor/Perception Bus
Front
LADAR
Left
LADAR
Right
LADAR
Roof
LADAR
Roof
LADAR
Rear
LADAR
Long
Range
LADAR
Camera
1
Camera
2
Camera
3
Camera
4
Camera
GPS
GPS
Encoder
NFM
5
Center for
Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Scope of Reference Implementation
• Minimalist approach to Knowledge
Representation
• Remain JAUS-compliant via creation of Userdefined messages for transmission of Findings
• Use the DGC2005 NaviGATOR as a surrogate
• Simulate Perception Element as needed
• Adapt Tom Galluzzo’s Receding Horizon Planner
to serve as initial Roadway Navigation behavior
• Implement n-Point-Turn behavior on NaviGATOR
(Greg Garcia, Lead)
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
NaviGATOR
Component
Blockfor
Diagram
Simplified
NaviGATOR
Architecture
a 2-Behavior System
The APF Conceptual Model
Wrench
Primitive
Driver
Intelligence
Element
Actuators
Vehicle
Actuator Feedback
Subsystem
Commander
Vehicle State
Disable
DARPA
Remote
Kill
Pause
1
Velocity State
n-Point Turn
Behavior
nPT
Behavior
Specialist
4
RN
Behavior
Specialist
Roadway
Navigation
Velocity State
Sensor
GPS INS
Encoder
3
Global
Position
Sensor
Position State
Path Plan
Planning
Element
Raster
Traversability
Grid
3
Control
Element
DARPA
Remote Kill
Smart
Arbiter
Offline
Path Planner
World Model
1
3
2
2
DARPA
RDDF
World
Raster
Traversability
Grids
a priori data
RDDF
Vector
Data
4
Boundary
Smart Sensor
Path
Smart Sensor
Smart Sensor
Smart Sensor
Path
Finder
Neg
Obst
LADAR
Smart Sensor
Smart Sensor
Close Range
Safety
Specialist
3
Perception
Element
Terrain
LADAR
Planar
LADAR
?
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Knowledge Representation
• Appendix C
• Behavior Use Cases
– Roadway Navigation
– n-Point Turn
• Findings Worksheets
– Roadway Navigation Behavior Specialist
– n-Point Turn Behavior Specialist
– Close-Range Safety Specialist
• Decision Broker Protocols
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Portrayal of safety buffers for the three
n-Point Turn Reactive Actions
reverse right
forward left
reverse straight
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
JAUS Meta Data Concept
• “Data about the data”
• Needed a mechanism to transmit Findings (in the form of
strings) in a fashion consistent with JAUS
• Can be used for transmitting any valid JAUS data type
• Reserved for data that is not addressed by an existing
JAUS message (violating this rule will cause the system
to be deemed out of compliance with JAUS)
• Introduced “Meta Data Element” to refer to an individual
Meta Data entity
• Namespace integrity is maintained via unique Meta Data
name + publishing Component ID
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Implemented Meta Data Message Set
• Report Meta Data Message - Automatically sent to
subscribers whenever there is a significant change to the
publisher’s Meta Data (at publisher’s discretion)
• Extremely flexible  complex
• Each Meta Data Element in the message can have a
distinct data type  JAUS Variant data type
• Publish/Subscribe “Handshake” Messages:
– Meta Data Changed Event Setup
– Meta Data Changed Event Confirmation
• Developed Supporting Structures and Utilities
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Adding APF capabilities to a
Component
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Add basic Meta Data capability
Add Specialist code for Publishers of Findings
Modify Behavioral components
Incorporate Decision Protocols into Subsystem
Commander component
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Other Reference Implementation
Details
• Added associated Behavior Specialist and simulated
blockage to the Roadway Navigation behavior
• Removed “nudging” sub-behavior from the Roadway
Navigation behavior
• Incorporated a simulated Close Range Safety
Specialist into the Subsystem Commander
component
• Created the n-Point Turn Behavior and associated
Behavior Specialist (Greg Garcia, Lead)
• Modified the Primitive Driver (Eric Thorn, Lead)
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Testing the Reference Implementation
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Unit Testing (with GPOS/VSS simulation)
On blocks (with GPOS/VSS simulation)
Gainesville Raceway
UF Solar Park
UF IFAS Research Farm near Citra, FL
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Testing Sites
UF IFAS (Citra)
Road Course at
Gainesville Raceway
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Test Plans
• Ensure proper coverage of test cases to bound
experimentation
• Test Plan contains:
– Scope and Objective
– Preconditions, constraints, test bed requirements, and situational
artifacts
– Safety, equipment and crew requirements
– Data, measurements, logs and readings to be captured and how
– Steps for conducting the experiment
– Anticipated results
• Built a software tool to present Test Plans
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Test Plans
• Devised Test Plans that avoid route re-planning
– Inverted Start
– Temporarily Blocked
• View in Test Control Unit
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Test Results
Inverted Start at Solar Park
Launch
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Test Results
Normal Start at Citra (with visualizer)
Launch
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Future Research - Theoretical
• Advanced Conflict Resolution Strategies
• Truth Maintenance (viability and ‘shelf life’ of
Findings over time) Techniques
• Explanation Facility (e.g., “The forwardLeftSafe
Condition is Present”)
• Behavior/Action Transition Assurance (continuity,
stability, safety)
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Future Research - Implementation
• System-wide Temporal “Instrumentation”
Scheme
• Meta Data Manager
• APF Visualization and Validation Toolkit
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Conclusion
• The Adaptive Planning Framework is an
important contribution to the AGV research
community
• It represents a new and unique approach to
achieving AGV intelligent behavior
• The goals of the research were achieved as
demonstrated in the Reference Implementation
• The results of the research will have a positive
impact on the JAUS Working Group and Team
Gator Nation going forward
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Questions and Discussion
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Backup Slides
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DGC2005 NaviGATOR Design
Traversability Grids
DGC Event Overview
Desert Testing
Qualification Event at California Speedway
Grand Challenge Race Day
Why Not “Multi-Agent”?
Lexicons, Taxonomies and Ontologies
World Model Knowledge Store
Literature Review
Earlier Prototypes
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Vehicle System
• rock crawler vehicle platform
• transverse Honda engine/transaxle mounted
longitudinally
• locked transaxle that drives front and rear
Detroit Locker differentials
• hydraulic steering
• two independent 24V alternator systems;
5600 W continuous power
• air conditioned and vibration isolated
electronics enclosure
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Sensor Systems
• design of sensing system
– pose
• Starfire GPS
• Smiths Aerospace IMU
monocular vision
ladar
– obstacles
• bumper height ladar
• long range radar
– terrain
radar
• two stationary ladar
• image processing
– implementation of sensor
arbitration via traversability
grid
ladar
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
NaviGATOR Component Block Diagram
NaviGATOR
Component Diagram
Wrench
Primitive
Driver
Vehicle
Actuators
Actuator Feedback
Disable
DARPA
Remote
Kill
Pause
Situation Assessment
Specialists:
- Long Range Horizon
State (uses Planar LADAR
raw data)
- Terrain Ruggedness
State (uses roll-rate and
pitch rate)
Internal
Speed Limit
Vehicle State
1
Receding
Horizon
Planner
Velocity State
Velocity State
Sensor
GPS INS
Encoder
3
Global
Position
Sensor
Position State
Raster
Traversability
Grid
3
1
Darpa
Remote Kill
Smart
Arbiter
World Model
Offline
Path Planner
3
2
DARPA
RDDF
World
Raster
Traversability
Grids
a priori data
RDDF
2
Vector
Data
Boundary
Smart Sensor
Smart Sensor
SA
Smart Sensor
Smart Sensor
Path
Finder
Neg
Obst
LADAR
Smart Sensor
Smart Sensor
Path
Smart Sensor
3
Planar
LADAR
Terrain
LADAR
Stereo
Vision
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
60 m  60 m grid with
grid resolution of 0.5 m  0.5 m
receding
horizon
planner
path segment
driver
primitive
driver
obstacle
detection
sensor(s)
world
model
sensor
aribiter
a priori path
terrain
evaluation
sensor(s)
DARPA Grand Challenge
• Build a system to travel up to 200 miles in 10
hours in a desert environment for a prize of $2M.
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
DARPA Grand Challenge
• What to expect in the MOJAVE
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
DARPA Grand Challenge
• Created in response to a Congressional and
DoD mandate, DARPA Grand Challenge is a
field test intended to accelerate research and
development in autonomous ground vehicles
that will help save American lives on the
battlefield.
• DoD has goal of having 1/3 of all military
vehicles unmanned by 2015.
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
DARPA Grand Challenge
• 1st event held in March 2004
• ~90 teams applied
• 25 teams selected to attend QID
• 15 teams in race
• furthest distance
traveled was 7 miles
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
2005 Team Selection
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195 initial applications
~140 video submissions
118 received site visits in May
43 teams selected to attend NQE at the
California Speedway
• 23 teams advanced to the 8 Oct 05 race
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
2005
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Topics
• Event Description
• Team Selection
• Team CIMAR
– team members
– vehicle system design
• System Testing
• NQE – Qualification at California Speedway,
27 Sep to 6 Oct 05
• Grand Challenge Race, 8 Oct 2005
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Desert Testing
Barstow, CA, 40 mile test course
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Desert Testing
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Topics
• Event Description
• Team Selection
• Team CIMAR
– team members
– vehicle system design
• System Testing
• NQE – Qualification at California Speedway,
27 Sep to 6 Oct 05
• Grand Challenge Race, 8 Oct 2005
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
NQE Course
completed entire course 3 of 5 times
NQE
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Topics
• Event Description
• Team Selection
• Team CIMAR
– team members
– vehicle system design
• System Testing
• NQE – Qualification at California Speedway,
27 Sep to 6 Oct 05
• Grand Challenge Race, 8 Oct 2005
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
course map supplied two hours
before vehicle start time
start / finish
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Grid at Start
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Hugging Right Side of Road
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Navigating a Very Cluttered Path
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Why Not “Multi-Agent”?
• Agents in multi-agent systems usually act independently
and display “emergent” behavior, whereas APF entities
are fully orchestrated
• Multi-agent decision-making is typically via negotiations,
whereas the APF will have one entity in governance
• Agents in multi-agent systems often are clones of one
another, whereas in APF, each is designed and tuned to
do one specific job
• Note that a Planning Specialist may be overseeing a
component that is participating in multi-agent behaviors
• These APF entities will be referred to as “Specialists”
B/U
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Lexicons, Taxonomies and Ontologies
• Lexicon: domain-specific dictionary of terms
• Taxonomy: logical ordering/categorization
• Ontology: formal specification of entities and their
relationships/interpretations
• Ontologies are a rich source of Knowledge
Representation content (entities/relationships) and ideas
(how to represent knowledge)
• General Ontologies: general purpose or “common sense,”
e.g., OpenCyc (www.opencyc.org/) and DARPA Agent
Markup Language (www.daml.org)
• AGV domain: Intelligent Systems Ontology underway at
NIST (Schlenoff 2005)
B/U
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
World Model Knowledge Store
• AGV’s representation of data, information, knowledge, and
meta-knowledge (knowledge about the knowledge)
• May be a priori, perceived, inferred, or received
• Each entity must have a precise definition and format
• Relational or object-oriented data bases are often used
• Much content is geo-spatial in nature extensions for GIS,
topographical, polygonal objects
• May be centralized (JAUS), distributed (4D/RCS), or
localized (publish/subscribe)
• Some (e.g., NIST) extend WMKS concept to include
simulation and prediction
B/U
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Architectural Compatibility
• Compatibility with emerging standards is
important to ensure interoperability and
consumer adoption
• Examined three mainstream standardization
efforts plus one lesser-known, but relevant
architecture:
– Joint Architecture for Unmanned Systems (JAUS)
– NIST 4D/RCS
– Service Oriented Architecture (SOA)/Component
Oriented Architecture (COA)
– Distributed Architecture for Mobile Navigation (DAMN)
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
JAUS
• CIMAR work based on version 3.2 of the JAUS
Reference Architecture with extensions under
investigation by the JAUS Working Group
• Defines components and their interfaces
• Defines messaging construct (header and content), legal
data types and all messages
• JAUS Tenets include:
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Vehicle platform independence
Mission isolation
Computer hardware independence
Technology independence
• Allows for “User-defined Components” and
“Experimental messages”
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
NIST 4D/RCS
• Real-time Control System
architecture under
development since early
’90s
• Defines functions and
interfaces
• Defines 8 hierarchical
temporal regimes (5 shown)
• Defines distributed,
functional decomposition
50s
10min
5Km
1Hz
5s
50m
(source: NIST)
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
SOA/COA
• SOA:
– Industrial standard driven by W3C and Web Services
– Provides loose coupling and anonymous
interoperability via strict interface compliance, high
granularity, and self-disclosure of service capabilities
– XML is the messaging language of choice
• COA:
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Predecessor to SOA
Pre-assigns capabilities to components
Multiple services per component allowed
Focuses more on functional decomposition and loose
coupling, less on interoperability
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
DAMN
• 1997 Ph.D. dissertation by J. K. Rosenblatt
• Scope limited to navigation and obstacle
avoidance
• Supports distributed, heterogeneous entities
• Blends centralized and decentralized processing
(source: Rosenblatt 1997)
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Situation Assessment
• Defined as transforming raw
data and stored information
into into more general
situational conclusions,
usually via inference
• Numerous situation
assessment “nuggets”
mentioned in the literature
that can be harvested
• Differentiate use of term
when alluding to human
situational awareness in the
context of manned combat
systems
Situation Assessment
Car turning left (position, velocity)
Oncoming cars (position, velocity)
Traffic signals (stop)
Truck on own road (position, velocity)
Own road edges (Old Georgetown Road, heading North)
Intersecting road edges (Democracy Boulevard, to West)
Self in lane 2 (position, velocity) intent (go straight)
(source: NIST)
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Behavior
• Sense-Plan-Act
– Creates a plan/updates world model based on perception
– Puts that plan into motion
– Uses perception of world to correct differences between planned results
and perceived results
– Most deliberative planners begin with this planning style
– Widely used, including the NaviGATOR
• Reactive Behavior
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Based on Brooks’ Subsumption Architecture
Perception maps directly to behaviors (no localized planning)
Possible Behaviors are prioritized into levels
Higher levels subsume the behaviors below them
“Emergent” behaviors result due to extemporaneous blending
• Juxtaposition of extremes  Hybrids
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Decision-making
• Decision Theory: insights provided by classical
approaches
– Collaborative Decision-making via Argumentation (Karacapilidis
2001)
– Decision modeling, e.g. planning trees (Rauenbusch 2003)
– 3-step decision-making process (Hoffman 2005) – e.g., Situation
Assessment, Planning, and Commitment to a course of action
• Behavior Arbitration: each behavioral component
delivers its vote on control action and a Behavior Arbiter
fuses them into a single command
– Key element of DAMN concept
– Extended to “utility fusion” based on utility theory
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Decision-making
• Action Selection: intelligent choice from menu of
behavioral actions
– Survey of 10 approaches by 8 criteria (Pirjanian 1998)
– One level of abstraction deeper than APF, but quite informative
– Used by NIST 4D/RCS
• Adaptive Planning: altering a plan already in progress
based on new information or a new situation
– Field began as an Expert System for military planners (Seares
1987)
– Hayes-Roth (1995) developed an adaptive planning architecture
addressing 5 areas of adaptive behavior based on situation
– Altering planning time or Quality of Service (Hassan 2001) based
on situation
– Used by NIST 4D/RCS
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Knowledge Representation
• Schemas and constructs used to document,
standardize, normalize and utilize domain
entities
• Must address semantics/meanings of
relationships among entities
• Must capture their names, descriptions,
attributes and mechanism for determining their
state or value
• Lexicons, Taxonomies and Ontologies
• World Model Knowledge Store
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
NIST Knowledge Representation
Scheme for on-road Driving
Task decomposition decision tree.
(source: Barbera et al. 2004a)
Hierarchy of agent control modules
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
NIST Knowledge Representation
Scheme for on-road Driving
 Situational Conditions Tree
Behavior State Transition Table
(source: Barbera et al. 2004a).
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Proof of Concept Prototype
• LISP-based Intelligent Situation Assessment
System (emphasis on Situation Assessment)
• Modeled 12 inputs, values are manually entered
• Determined 5 Conditions and 6 States owned by
3 Specialists
• Used 20 generalized production rules and a
Blackboard architecture
• Provided concept clarification and validation
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
Implementation for DGC2005
• Simple APF-based Speed Limiter implemented on the
NaviGATOR
• Modeled 2 Conditions related to possible obstacles
beyond the planning horizon and 1 State related to
terrain ruggedness
• Data feeds from the PLSS (long-range ladar data) at 35
Hz and the VSS (roll rate, pitch rate) at 20 Hz
• Output JAUS Set Travel Speed message with one of 4
maximum speeds running at 20 Hz
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Top speed (25 mph)
Caution speed (20 mph)
Obstacle Avoidance speed (16 mph)
Low speed (4 mph)
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering
NaviGATOR Component Block Diagram
SA Implementation for DGC2005
Wrench
Primitive
Driver
SA:
Situation Assessment Specialists:
- Long Range Horizon State (uses
Planar LADAR raw data)
- Terrain Ruggedness State (uses
roll-rate and pitch rate)
Vehicle
Actuators
Actuator Feedback
Disable
DARPA
Remote
Kill
Pause
1
Receding
Horizon
Planner
Velocity State
Velocity State
Sensor
GPS INS
Encoder
3
Global
Position
Sensor
Position State
Raster
Traversability
Grid
Internal
Speed Limit
Vehicle State
3
1
Darpa
Remote Kill
Smart
Arbiter
World Model
Offline
Path Planner
3
2
DARPA
RDDF
World
Raster
Traversability
Grids
a priori data
RDDF
2
Vector
Data
Boundary
Smart Sensor
SA
Smart Sensor
Smart Sensor
Smart Sensor
Path
Finder
Neg
Obst
LADAR
Smart Sensor
Smart Sensor
Path
Smart Sensor
3
Planar
LADAR
Terrain
LADAR
Stereo
Vision
Center for Intelligent Machines and Robotics
Department of Mechanical and Aerospace Engineering