9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning1 Part II Chapter 9: Topological Path Planning.

Slides:



Advertisements
Similar presentations
MICHAEL MILFORD, DAVID PRASSER, AND GORDON WYETH FOLAMI ALAMUDUN GRADUATE STUDENT COMPUTER SCIENCE & ENGINEERING TEXAS A&M UNIVERSITY RatSLAM on the Edge:
Advertisements

Becerra-Fernandez, et al. -- Knowledge Management 1/e -- © 2004 Prentice Hall Chapter 7 Technologies to Manage Knowledge: Artificial Intelligence.
Graphs Chapter 12. Chapter Objectives  To become familiar with graph terminology and the different types of graphs  To study a Graph ADT and different.
Introduction This chapter explores graphs and their applications in computer science This chapter explores graphs and their applications in computer science.
University of Amsterdam Search, Navigate, and Actuate - Quantitative Navigation Arnoud Visser 1 Search, Navigate, and Actuate Quantative Navigation.
C++ Programming: Program Design Including Data Structures, Third Edition Chapter 21: Graphs.
Chapter 10 Artificial Intelligence © 2007 Pearson Addison-Wesley. All rights reserved.
ECE 7340: Building Intelligent Robots QUALITATIVE NAVIGATION FOR MOBILE ROBOTS Tod S. Levitt Daryl T. Lawton Presented by: Aniket Samant.
ECE 4340/7340 Exam #2 Review Winter Sensing and Perception CMUcam and image representation (RGB, YUV) Percept; logical sensors Logical redundancy.
Experiences with an Architecture for Intelligent Reactive Agents By R. Peter Bonasso, R. James Firby, Erann Gat, David Kortenkamp, David P Miller, Marc.
Graphs Chapter 12. Chapter 12: Graphs2 Chapter Objectives To become familiar with graph terminology and the different types of graphs To study a Graph.
Spring 2010CS 2251 Graphs Chapter 10. Spring 2010CS 2252 Chapter Objectives To become familiar with graph terminology and the different types of graphs.
ICRA, May 2002 Simon Thompson & Alexander Zelinsky1 Accurate Local Positioning using Visual Landmarks from a Panoramic Sensor Simon Thompson Alexander.
Using Search in Problem Solving
Introduction to Topological Navigation prof: S.Shiry Presenter: Masoomeh Bahreini M.Sc Computer Science Department of Computer Eng. and IT Amirkabir Univ.
Fall 2007CS 2251 Graphs Chapter 12. Fall 2007CS 2252 Chapter Objectives To become familiar with graph terminology and the different types of graphs To.
Route Planning Vehicle navigation systems, Dijkstra’s algorithm, bidirectional search, transit-node routing.
Solving problems by searching
Chapter 11: Artificial Intelligence
Chapter 10 Artificial Intelligence. © 2005 Pearson Addison-Wesley. All rights reserved 10-2 Chapter 10: Artificial Intelligence 10.1 Intelligence and.
Chapter 9 – Graphs A graph G=(V,E) – vertices and edges
Localisation & Navigation
M.Menelaou CCNA2 ROUTING. M.Menelaou ROUTING Routing is the process that a router uses to forward packets toward the destination network. A router makes.
DARPA Mobile Autonomous Robot SoftwareLeslie Pack Kaelbling; March Adaptive Intelligent Mobile Robotics Leslie Pack Kaelbling Artificial Intelligence.
Introduction to AI Robotics Chapter 2. The Hierarchical Paradigm Hyeokjae Kwon.
7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm1 Part 1: Overview & Managerial.
9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning1 Part II Chapter 9: Topological Path Planning.
Using Soar for an indoor robotic search mission Scott Hanford Penn State University Applied Research Lab 1.
7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm1 Part 1: Overview & Managerial.
Knowledge Representation CPTR 314. The need of a Good Representation  The representation that is used to represent a problem is very important  The.
TCP/IP Protocol Suite 1 Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. Chapter 11 Unicast Routing Protocols.
University of Amsterdam Search, Navigate, and Actuate - Qualitative Navigation Arnoud Visser 1 Search, Navigate, and Actuate Qualitative Navigation.
Copyright © 2008 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 11: Artificial Intelligence Computer Science: An Overview Tenth Edition.
Chapter 11 Artificial Intelligence Introduction to CS 1 st Semester, 2015 Sanghyun Park.
10 Chapter 10: Metric Path Planning a. Representations b. Algorithms.
11 Chapter 11: Localization and Map Making a. Occupancy Grids b. Evidential Methods c. Exploration Overivew Occupancy Grids -Sonar Models -Bayesian Updating.
Easiest-to-Reach Neighbor Search Fatimah Aldubaisi.
Topological Path Planning JBNU, Division of Computer Science and Engineering Parallel Computing Lab Jonghwi Kim Introduction to AI Robots Chapter 9.
Routing Networks and Protocols Prepared by: TGK First Prepared on: Last Modified on: Quality checked by: Copyright 2009 Asia Pacific Institute of Information.
Copyright © 2008 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 11: Artificial Intelligence Computer Science: An Overview Tenth Edition.
Turning Autonomous Navigation and Mapping Using Monocular Low-Resolution Grayscale Vision VIDYA MURALI AND STAN BIRCHFIELD CLEMSON UNIVERSITY ABSTRACT.
Introduction to Artificial Intelligence CS 438 Spring 2008 Today –AIMA, Ch. 25 –Robotics Thursday –Robotics continued Home Work due next Tuesday –Ch. 13:
Robotics Club: 5:30 this evening
Chapter 10. The Explorer System in Cognitive Systems, Christensen et al. Course: Robots Learning from Humans On, Kyoung-Woon Biointelligence Laboratory.
Overivew Occupancy Grids -Sonar Models -Bayesian Updating
9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning1 Part II Chapter 9: Topological Path Planning.
 A navigational display should serve these four different classes of tasks:  Provide guidance about how to get to a destination  Facilitate planning.
Graphs Chapter 12. Chapter 12: Graphs2 Chapter Objectives To become familiar with graph terminology and the different types of graphs To study a Graph.
© 2002, Cisco Systems, Inc. All rights reserved..
Graphs Chapter 28 © 2015 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. Data Structures and Abstractions with Java, 4e Frank Carrano.
Chapter 20: Graphs. Objectives In this chapter, you will: – Learn about graphs – Become familiar with the basic terminology of graph theory – Discover.
Learning Roomba Module 5 - Localization. Outline What is Localization? Why is Localization important? Why is Localization hard? Some Approaches Using.
Intelligent Agents Chapter 2. How do you design an intelligent agent? Definition: An intelligent agent perceives its environment via sensors and acts.
Basilio Bona DAUIN – Politecnico di Torino
Beard & McLain, “Small Unmanned Aircraft,” Princeton University Press, 2012, Chapter 12: Slide 1 Chapter 12 Path Planning.
Functionality of objects through observation and Interaction Ruzena Bajcsy based on Luca Bogoni’s Ph.D thesis April 2016.
Chapter 11: Artificial Intelligence
Chapter 11: Artificial Intelligence
PART IV: The Potential of Algorithmic Machines.
Spatial Semantic Hierarchy (SSH)
Intelligent Agents Chapter 2.
Part II Chapter 9: Topological Path Planning
Link state routing In link state routing, if each node in the domain has the entire topology of the domain list of nodes and links, how they are connected.
Sequence Pair Representation
Overivew Occupancy Grids -Sonar Models -Bayesian Updating
Chapter 7: Hybrid Deliberative/Reactive Paradigm
Chapter 10: Metric Path Planning.
Today: Localization & Navigation Wednesday: Image Processing
Presentation transcript:

9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning1 Part II Chapter 9: Topological Path Planning

9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning2 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

9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning3 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 Mission Planner Carto- grapher Behaviors deliberative reactive How am I going to get there?

9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning4 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

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

9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning6 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!

9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning7 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

9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning8 Example Landmarks

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

9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning10 Problems with early relational graphs Not coupled with how the robot would get there Shaft encoder uncertainty accumulates

9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning11 Kuipers and Byun: Spatial Hierarchy

9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning12 Distinctive Places (recognizable, & at least locally unique) Local control strategies (behaviors to get robot between DPs) Distinctive Place Approach

9 neighborhood boundary distinctive place (within the 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 (exteroceptive cueing) then swap to a landmark localization behavior

9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning14 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 1Room 2 Room 3 Room 4 r1 r2 de1 de3 de2 r3 r4 t1t2t3fh mtd

9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning15 Case Study Representation Sequencing of behaviors based on current perception (releasers) and subgoal Algorithm

9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning16 R3->R7 Hd nodes because Have different perception

9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning17 Transition Table TO FROMHFRHd HNavigate- Hall UndefinedNavigate- Hall F Navigate- Foyer Navigate- Door RUndefinedNavigate- door HdNavigate- hall Navigate- door Navigate- hall

9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning18 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?

9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning19 Execution Exception subscript

9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning20 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

9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning21 Image Signatures The worldTesselated (like faceted-eyes) Resulting signature for home

9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning22 Move to match the template

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

9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning24 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

9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning25 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