Part II Chapter 9: Topological Path Planning
-
Upload
garrison-fisher -
Category
Documents
-
view
24 -
download
1
description
Transcript of Part II Chapter 9: Topological Path Planning
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning 1
9Part II
Chapter 9:Topological Path Planning
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning 2
9 Objectives
• Define the difference between natural and artificial landmarks; give one example of each
• Given a description of an indoor office environment and a set of behaviors, build a relational graph representation labeling the distinctive places and local control strategies for gateways
• Describe in one or two sentences: gateway, image signature, visual homing, viewframe, orientation region
• Given a figure showing landmarks, create a topological map showing landmarks, landmark pair boundaries, and orientation regions
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning 3
9 Navigation• Where am I going? Mission
planning
• What’s the best way there? Path planning
• Where have I been? Map making
• Where am I? Localization
MissionPlanner
Carto-grapher
BehaviorsBehaviorsBehaviorsBehaviors
deli
bera
tive
reac
tiveHow am I going to get
there?
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning 4
9 Spatial Memory
• What’s the Best Way There? depends on the representation of the world
• A robot’s world representation and how it is maintained over time is its spatial memory– Attention
– Reasoning
– Path planning
– Information collection
• Two forms– Route (or qualitative)
– Layout (or metric)
• Layout leads to Route, but not the other way
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning
9 Route, or Qualitative Navigation
• Two categories
• Relational– spatial memory is a relational graph, also known as a
topological map
– use graph theory to plan paths
• Associative– spatial memory is a series of remembered viewpoints,
where each viewpoint is labeled with a location
– good for retracing steps
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning 6
9 Topological Maps Use Landmarks
• A landmark is one or more perceptually distinctive features of interest on an object or locale of interest
• Natural landmark: configuration of existing features that wasn’t put in the environment to aid with the robot’s navigation (ex. gas station on the corner)
• Artificial landmark: set of features added to the environment to support navigation (ex. highway sign)
• Roboticists avoid artificial landmarks!
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning 7
9 Desirable Characteristics of Landmarks
• Recognizable (can see it when you need to)– Passive
– Perceivable over the entire range of where the robot might need to view it
– Distinctive features should be globally unique, or at least locally unique
• Perceivable for the task (can extract what you need from it)– ex. can extract relative orientation and depth
– ex. unambiguously points the way
• Be perceivable from many different viewpoints
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning 8
9 Example Landmarks
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning
9 floor plan
relational graph
Relational Methods
Nodes: landmarks, gateways,goal locations
Edges: navigable path
Gateway is an opportunityto change path heading
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning 10
9 Problems with early relational graphs
• Not coupled with how the robot would get there
• Shaft encoder uncertainty accumulates
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning 11
9 Kuipers and Byun: Spatial Hierarchy
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning 12
9
Distinctive Places (recognizable, &at least locally unique)
Local control strategies (behaviorsto get robot between DPs)
Distinctive Place Approach
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning
9neighborhoodboundary
distinctiveplace (withinthe corner)
path of robot as it moves into neighborhood and
to the distinctive place
Actually Getting to a Distinctive Place: Neighborhoods
Use one behavior until sees the DP (exteroceptivecueing) then swap to a landmark localization behavior
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning 14
9 Class Exercise
• Create a relational graph for this floorplan
• Label each edge with the appropriate LCS: mtd, fh
• Label each node with the type of gateway: de, t, r
Room 1 Room 2
Room 3 Room 4
r1 r2
de1
de3
de2r3 r4
t1 t2 t3fh fh fh
fh
fh
mtd
mtd
mtd
mtd
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning 15
9 Case Study
• Representation
• Sequencing of behaviors based on current perception (releasers) and subgoal
• Algorithm
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning 16
9
R3->R7
Hd nodes becauseHave different perception
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning 17
9 Transition Table
TO
FROM H F R Hd
H Navigate-Hall
Navigate-Hall
Undefined Navigate-Hall
F Navigate-Hall
Navigate-Foyer
Navigate-Door
Navigate-Door
R Undefined Navigate-door
Navigate-door
Navigate-door
Hd Navigate-hall
Navigate-hall
Navigate-door
Navigate-hall
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning 18
9 Path Planning Algorithm
• Relational graph, so any single source shortest path algorithm will work (Dijkstra’s)
• If wanted to visit all rooms, what algorithm would you use?
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning 19
9 Execution
Exception subscript
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning 20
9 Associative Methods• Visual Homing
– bees navigate to their hive by a series of image signatures which are locally distinctive (neighborhood)
• QualNav– the world can be divided
into orientation regions (neighborhoods) based on perceptual events caused by landmark pair boundaries
• Assumes perceptual stability, perceptual distinguishability
RandalNelson,URochester
DarylLawton,AdvancedDecision Systems
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning 21
9 Image Signatures
The world Tesselated (like faceted-eyes)
Resulting signaturefor home
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning 22
9
Move to match thetemplate
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning
9
tree
building
radiotower
mountain
OR1OR2
MetricMap
TopologicalRepresentationas OrientationRegions
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning 24
9 Summary
• Route, qualitative, and topological navigation all refer to navigating by detecting and responding to landmarks.
• Landmarks may be natural or artificial; roboticists prefer natural but may have to use artificial to compensate for robot sensors
• There are two type of qualitative navigation: relational and associative
Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning 25
9 Summary (cont.)
• Relational methods use graphs (good for planning) and landmarks– The best known relational method is distinctive places
– Distinctive places are often gateways
– Local control strategies are behaviors
• Associative methods remember places as image signature or a viewframe extracted from a signature– can’t really plan a path, just retrace it
– direct stimulus-response coupling by matching signature to current perception