Part II Chapter 9: Topological Path Planning Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning

Chapter 9: Topological Path Planning 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

Chapter 9: Topological Path Planning Where am I going? Mission planning What’s the best way there? Path planning Where have I been? Map making Where am I? Localization Navigation Carto- grapher Mission Planner deliberative How am I going to get there? Behaviors reactive Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning

Chapter 9: Topological Path Planning 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

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

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

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

Chapter 9: Topological Path Planning Example Landmarks Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning

Gateway is an opportunity to change path heading floor plan Gateway is an opportunity to change path heading relational graph Relational Methods Nodes: landmarks, gateways, goal locations Edges: navigable path

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

Kuipers and Byun: Spatial Hierarchy Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning

Distinctive Place Approach Local control strategies (behaviors to get robot between DPs) Distinctive Places (recognizable, & at least locally unique) Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning

Actually Getting to a Distinctive Place: Neighborhoods boundary distinctive place (within the corner) path of robot as it moves into neighborhood and to the distinctive place Use one behavior until sees the DP (exteroceptive cueing) then swap to a landmark localization behavior

Chapter 9: Topological Path Planning 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 r1 r2 de1 de3 de2 r3 r4 t1 t2 t3 fh mtd Room 1 Room 2 Room 3 Room 4 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning

Chapter 9: Topological Path Planning 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

Chapter 9: Topological Path Planning Hd nodes because Have different perception R3->R7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning

Chapter 9: Topological Path Planning Transition Table TO FROM H F R Hd Navigate-Hall Undefined Navigate-Foyer Navigate-Door Navigate-door Navigate-hall Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning

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

Chapter 9: Topological Path Planning Execution Exception subscript Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning

Chapter 9: Topological Path Planning 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 Randal Nelson, URochester Daryl Lawton, Advanced Decision Systems Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning

Chapter 9: Topological Path Planning Image Signatures The world Tesselated (like faceted-eyes) Resulting signature for home Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning

Chapter 9: Topological Path Planning Move to match the template Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning

OR2 OR1 Topological Representation as Orientation Regions Metric Map tree building radio tower mountain OR1 OR2 Metric Map Topological Representation as Orientation Regions

Chapter 9: Topological Path Planning 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

Chapter 9: Topological Path Planning 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 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning