Cal Poly Pomona
Robot Navigation
Salomón Oldak, Ph.D.
Electrical and Computer
Engineering
2/8/06
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DARPA Grand Challenge
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Congress mandate to increase the use of ground unmanned vehicles.
Congressional Goal: 1/3 of armed forces combat vehicles unmanned by
2015.
Vehicles must be fully autonomous
Nearly 250 miles on and off road in 10 hours.
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First DARPA Challenge
2004
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First Challenge took place on 3/14/04
Between Los Angeles and Las Vegas
$1 Million Prize
25 Teams Participated
– CalTech, University of Florida, University of Alaska, Virginia Tech and others
– Palos Verdes High School
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Ghostrider(Blue Team,
Berkeley)
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Blue Team Movie
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Obstacles
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Paved Roads
Overpasses
Straight-Winding Roads
Sand, Rock
Underpasses
Water
Natural Obstruction
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1st Challenge Results
Failure:
No Team
Completed
Task.
Max Distance
7.4Mi
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2 nd DARPA Grand
Challenge (10/8/2005)
Success! 3 Robots Completed task!
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The Winner: Stanley
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Stanford University
(Sebastian Thrun)
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Sensors Used
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GPS Antenna
Laser Range Finder (Lidar) (30m)
Video Camera (80m)
Odometry (Photo Sensor on Wheel)
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Algorithm
Problems: Vibration would trick sensors to “imagine” ghost obstacles, The vehicle thought it’s own shadow is an obstacle.
Solution: Teach the car. Assess weights to pixels as a human driver operates the car.
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Future
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43000 people die in traffic accidents/year in the US
Robot driven cars will reduce # of fatalities
Accidents can be avoided
Liability?
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Spirit and Opportunity
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Navigation
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Rover’s are mostly teleoperated
2 stereo hazard avoidance cameras front and back
1 Stereo Navigation camera on mast
Rovers moves 0.5m at max speed of
34m/h = 0.02MPH
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Robot Paradigms
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Paradigm:
“A "view" of how things work in the world.”
“…a set of rules and regulations…”
Paradigm Primitives:
SENSE, PLAN, ACT
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Paradigms
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Hierarchical
1967-1990
Reactive
1988-1992
Hybrid Deliberative/Reactive
1990-
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Hierarchical Paradigm
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The robot operates in a top-down fashion, heavy on planning.
The robot senses the world, plans the next action, acts; at each step the robot explicitly plans the next move.
All the sensing data tends to be gathered into one global world model.
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Shakey (SRI)
First AI Robot
(1967-70)
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Assumptions
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Close World: World Model Contains everything the Robot needs to know
Frame Problem: The real-world situation is computationally feasible.
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Problem
Robots designed under Hierarchical
Paradigm were VERY slow.
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Reactive Paradigm
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Sense-act type of organization.
The robot has multiple instances of Sense-Act couplings.
These couplings are concurrent processes, called behaviours, which take the local sensing data and compute the best action to take independently of what the other processes are doing.
The robot will do a combination of behaviours .
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Biological Foundation of
Reactive Paradigm
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Reactive Paradigm is based on observations of ethologists (study of animal behavior) and cognitive psychology (how humans think and represent knowledge)
Biology provides existence proofs.
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Behavior Definition
(graphical)
BEHAVIOR
Sensory
Input
Pattern of Motor
Actions
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Arctic Terns
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Arctic terns live in Arctic (black, white, gray environment, some grass) but adults have a red spot on beak
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When hungry, baby pecks at parent’s beak, who regurgitates food for baby to eat
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How does it know its parent?
– It doesn’t, it just goes for the largest red spot in its field of view
(e.g., ethology grad student with construction paper)
– Only red thing should be an adult tern
– Closer = large red
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Types of Behaviors
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Reflexive
– stimulus-response, often abbreviated S-R (like knee tapped). “Hardwired”
Reactive
– learned or “muscle memory”. (Riding a bike, skiing, etc.)
Conscious
– deliberately stringing together (Building a Robot)
WARNING Overloaded terms:
Roboticists often use “reactive behavior” to mean purely reflexive,
And refer to reactive behaviors as “skills”
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Example: Cockroach Hide
 light goes on, the cockroach turns and runs
 when it gets to a wall, it follows it
 when it finds a hiding place (thigmotrophic), goes in and faces outward
 waits until not scared, then comes out
 even if the lights are turned back off earlier
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Behaviors are Concurrent
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What happens when there’s a conflict from concurrent behaviors?
?
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Equilbrium
– Feeding squirrels-feed, flee: hesitate inbetween
Dominance
– Sleepy, hungry: either sleep or eat
Cancellation
– Sticklebacks defend, attack: build a nest
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Reactive Robots
RELEASER behavior
SENSE ACT
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Most apps are programmed with this paradigm
Biologically based:
– Behaviors (independent processes), released by perceptual or internal events (state)
– No world models or long term memory
– Highly modular, generic
– Overall behavior emerges
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Example : My Real Baby
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Behaviors?
Touch-> Awake
Upside down & Awake-> Cry
Awake & Hungry -> Cry
Awake & Lonely -> Cry www.irobot.com
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Note can get crying from multiple behaviors
Note internal state (countdown timer on Lonely)
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Subsumption Architecture:
Rodney Brooks
From http://www.spe.sony.com/classics/fastcheap/index.html
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Runaway
PS
RUN AWAY
MS follow-corridor 2 wander 1 runaway 0
PS MS
HALT
COLLIDE
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Example Perception: Polar Plot
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 if sensing is ego-centric, can often eliminate need for
Plot is ego-centric memory, representation
Plot is distributed (available to whatever wants to use it)
Although it is a representation in the sense of being a data structure, there is no memory (contains latest information) and no reasoning (2-3 means a “wall”)
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Potential Fields (Example:
Navigation)
3
2
5
4
7
6
1
0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0
0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
0 1 2 3 4 5 6 7 8 9
1
0
1
1
1
2
1
3
1
4
1
5
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Wavefront Algorithm
3
2
5
4
7
6
1
0
18 17 16 15 14 13 12 11 10 9 9 9 9 9 9 9
17 17 16 15 14 13 12 11 10 9 8 8 8 8 8 8
17 16 16 15 14 13 12 11 10 9 8 7 7 7 7 7
17 16 15 15 1 1 1 1 1 1 1 1 6 6 6 6
17 16 15 14 1 1 1 1 1 1 1 1 5 5 5 5
17 16 15 14 13 12 11 10 9 8 7 6 5 4 4 4
17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 3
17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2
1 1 1 1 1
0 1 2 3 4 5 6 7 8 9
0 1 2 3
(0,7) -> (1,7) -> (2,7) -> (3,7) -> (4,7) -> (5,7) -> (6,7)
-> (7,7) -> (8,7) -> (9,7) -> (10,7) -> (10,6) -> (11,6) -
> (11,5) -> (12,5) -> (12,4) -> (12,3) -> (13,3) ->
(13,2) -> (14,2) -> (14,1) -> (15,1) -> (15,0)
4
1
5
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Hybrid Deliberate/Reactive
Paradigm
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The robot first plans (deliberates) how to best decompose a task into subtasks (also called “mission planning”) and then what are the suitable behaviours to accomplish each subtask.
Then the behaviours starts executing as per the Reactive
Paradigm.
Sensing organization is also a mixture of Hierarchical and
Reactive styles; sensor data gets routed to each behaviour the needs that sensor, but is also available to the planner for construction of a task-oriented global world model.
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Deliberation v.s. Planning
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Besides “planning” robot has to perform other tasks such as: map making, performance monitoring, learning, etc.
All these tasks together with planning are known as: DELIBERATION
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Sensing Organization
SENSOR 3
SENSOR 1
Deliberative functions
*Can “eavesdrop”
WORLD MAP/
KNOWLEDGE REP
*Can have their own
Sensors
*Have output which
Looks like a sensor virtual sensor Output to a behavior
(virtual sensor)
BEHAVIOR
BEHAVIOR ACTUATORS
BEHAVIOR
SENSOR 2
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Hybrid Behaviors
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Behaviors are extended to
Reflexive
Innate
Learned
(Just like in ethology)
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Architectures:
Common Functionality
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Mission planner
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Cartographer
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Sequencer agent
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Behavioral manager
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Performance monitor/problem solving agent
(fairly rare)
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Several Hybrid Approaches
Have Been Developed
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AuRA (Arkin 1986)
Atlantis (Gat 1991)
Sensor-Fusion Effects (SFX) (Murphy 1996)
3-Tiered (3T) (JPL1990s)
Saphira (Konolige 1998)
Tack Control Architecture (Simmons 1997)
Planner-Reactor (Lyons and Hendriks 1992)
Procedural Reasoning System (PRS) (Georgeff and Lansky 1987)
SSS (Connell 1992)
Multi-Valued Logic (Saffiotti 1995)
SOMASS Hybrid Assembly System (Malcom and Smithers 1990)
Agent Architecture (Hayes-Roth 1993)
Etc., Etc.
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Georgia Tech TMR(Tactical
Mobile Robot) Robots
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NAVIGATION
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Topological Navigation – Qualitative
Navigation
Metric Navigation – Quantitative
Navigation
Navigation Algorithm usually is part of
Deliberative part of Hybrid
Architecture.
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Qualitative Navigation uses Landmarks
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floor plan
Gateway is an opportunity to change path heading
Relational Methods
Nodes: landmarks, gateways, goal locations
Edges: navigable path relational graph

Quantitative Navigation
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Want to get from one point to another with an optimization criteria:
Minimize Time
Minimize Energy
Minimize Distance
Etc.
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Space Representation:
Voronoi Graphs
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Imagine a fire starting at the boundaries, creating a line where they intersect, intersections of lines are nodes
Result is a relational graph
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Space Representation:
Rectangular Graph
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A * Algorithm
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Discussion
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References
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Murphy R.R.; “Introduction to AI
Robotics, MIT Press, 2000 http://www.policyalmanac.org/games/ aStarTutorial.htm
(Accessed 2/6/06) http://robots.stanford.edu/ (Accessed
(2/6/06) http://www.ghostriderrobot.com/index
.php?id=robot (Accessed 2/6/06)
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