Embedded System Lab Kim Jong Hwi Chonbuk National University Introduction to Intelligent Robots.

Slides:



Advertisements
Similar presentations
ARCHITECTURES FOR ARTIFICIAL INTELLIGENCE SYSTEMS
Advertisements

Elephants Don’t Play Chess
5-1 Chapter 5: REACTIVE AND HYBRID ARCHITECTURES.
Lecture 4: Command and Behavior Fusion Gal A. Kaminka Introduction to Robots and Multi-Robot Systems Agents in Physical and Virtual Environments.
AuRA: Principles and Practice in Review
Lecture 2: Reactive Systems Gal A. Kaminka Introduction to Robots and Multi-Robot Systems Agents in Physical and Virtual Environments.
Concrete architectures (Section 1.4) Part II: Shabbir Ssyed We will describe four classes of agents: 1.Logic based agents 2.Reactive agents 3.Belief-desire-intention.
A Summary of the Article “Intelligence Without Representation” by Rodney A. Brooks (1987) Presented by Dain Finn.
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.
IofT 1910 W Fall 2006 Week 5 Plan for today:  discuss questions asked in writeup  talk about approaches to building intelligence  talk about the lab.
Chapter 4: Towards a Theory of Intelligence Gert Kootstra.
Behavior Coordination Mechanisms – State-of-the- Art Paper by: Paolo Pirjanian (USC) Presented by: Chris Martin.
Motor Schema - Based Mobile Robot Navigation System - Ronald C. Arkin.
Autonomous Mobile Robots CPE 470/670 Lecture 8 Instructor: Monica Nicolescu.
Motor Schema Based Navigation for a Mobile Robot: An Approach to Programming by Behavior Ronald C. Arkin Reviewed By: Chris Miles.
Topics: Introduction to Robotics CS 491/691(X) Lecture 8 Instructor: Monica Nicolescu.
Behavior- Based Approaches Behavior- Based Approaches.
A Robust Layered Control System for a Mobile Robot Rodney A. Brooks Presenter: Michael Vidal.
AuRA: Autonomous Robot Architecture From: Integrating Behavioral, Perceptual, and World Knowledge in Reactive Navigation Ron Arkin, 1990.
The Need of Unmanned Systems
Statistical Techniques in Robotics
Robotica Lezione 1. Robotica - Lecture 12 Objectives - I General aspects of robotics –Situated Agents –Autonomous Vehicles –Dynamical Agents Implementing.
Behavior Based Systems Behavior Based Systems Lezione 5.
Mobile Robot Control Architectures “A Robust Layered Control System for a Mobile Robot” -- Brooks 1986 “On Three-Layer Architectures” -- Gat 1998? Presented.
Introduction to Behavior- Based Robotics Based on the book Behavior- Based Robotics by Ronald C. Arkin.
Artificial Intelligence Chapter 2 Stimulus-Response Agents
©Ian Sommerville 1995 Software Engineering, 5th edition. Chapter 13Slide 1 Architectural Design u Establishing the overall structure of a software system.
Reactive Paradigm – Overview Subsumption Architecture By Ian Jonkers Studies in Machine Learning: Intelligent Robotics.
5-1 LECTURE 5: REACTIVE AND HYBRID ARCHITECTURES An Introduction to MultiAgent Systems
Robotica Lecture 3. 2 Robot Control Robot control is the mean by which the sensing and action of a robot are coordinated The infinitely many possible.
4 Introduction to AI Robotics (MIT Press)Chapter 4: The Reactive Paradigm1 The Reactive Paradigm Describe the Reactive Paradigm in terms of the 3 robot.
Software Agents: An Overview by Hyacinth S. Nwana and Designing Behaviors for Information Agents by Keith Decker, Anandeep Pannu, Katia Sycara and Mike.
Towards Cognitive Robotics Biointelligence Laboratory School of Computer Science and Engineering Seoul National University Christian.
Outline: Biological Metaphor Biological generalization How AI applied this Ramifications for HRI How the resulting AI architecture relates to automation.
7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm1 Part 1: Overview & Managerial.
Robotica Lecture 3. 2 Robot Control Robot control is the mean by which the sensing and action of a robot are coordinated The infinitely many possible.
5-1 LECTURE 5: REACTIVE AND HYBRID ARCHITECTURES An Introduction to MultiAgent Systems
University of Amsterdam Search, Navigate, and Actuate - Qualitative Navigation Arnoud Visser 1 Search, Navigate, and Actuate Qualitative Navigation.
Intelligent Robotics An Introduction The King’s Academy November 2, 2007.
Robot Intelligence Kevin Warwick. Reactive Architectures I: Subsumption Perhaps the best known reactive architecture, developed in the ‘80s by Rodney.
Intelligent Robotics Today: Robot Control Architectures Next Week: Localization Reading: Murphy Sections 2.1, 2.3, 2.5, 3.1, 3.5, 3.6, 4.1 – 4.3, 4.5,
Robotica Lecture Review Reactive control Complete control space Action selection The subsumption architecture –Vertical vs. horizontal decomposition.
Ann Nowe VUB 1 What are agents anyway?. Ann Nowe VUB 2 Overview Agents Agent environments Intelligent agents Agents versus objects.
Brooks’ Subsumption Architecture EEL 6838 T. Ryan Fitz-Gibbon 1/24/2004.
Introduction to Artificial Intelligence CS 438 Spring 2008 Today –AIMA, Ch. 25 –Robotics Thursday –Robotics continued Home Work due next Tuesday –Ch. 13:
4 Introduction to AI Robotics (MIT Press)Chapter 4: The Reactive Paradigm1 The Reactive Paradigm Describe the Reactive Paradigm in terms of the 3 robot.
Subsumption Architecture and Nouvelle AI Arpit Maheshwari Nihit Gupta Saransh Gupta Swapnil Srivastava.
Advanced Mobile Robotic The City College of the City University of New York Advanced Mobile Robotic The City College of the City University of New York.
Behavior-based Multirobot Architectures. Why Behavior Based Control for Multi-Robot Teams? Multi-Robot control naturally grew out of single robot control.
4 Introduction to AI Robotics (MIT Press)Chapter 4: The Reactive Paradigm1 The Reactive Paradigm Describe the Reactive Paradigm in terms of the 3 robot.
Autonomous Mobile Robots CPE 470/670 Lecture 10 Instructor: Monica Nicolescu.
Trends in Robotics Research Classical AI Robotics (mid-70’s) Sense-Plan-Act Complex world model and reasoning Reactive Paradigm (mid-80’s) No models: “the.
Robotic Architectures: Schema Lecture 7a. Motor Schemas ● Based upon schema theory: Explains motor behavior in terms of concurrent control of many different.
Done by Fazlun Satya Saradhi. INTRODUCTION The main concept is to use different types of agent models which would help create a better dynamic and adaptive.
Chapter 11: Artificial Intelligence
Part 3 Design What does design mean in different fields?
Build Intelligence from the bottom up!
Build Intelligence from the bottom up!
CIS 488/588 Bruce R. Maxim UM-Dearborn
Artificial Intelligence Chapter 2 Stimulus-Response Agents
Build Intelligence from the bottom up!
Chapter 7: Hybrid Deliberative/Reactive Paradigm
Subsuption Architecture
Robot Intelligence Kevin Warwick.
CS 4630: Intelligent Robotics and Perception
Chapter 4: The Reactive Paradigm
Chapter 12: Building Situated Robots
Behavior Based Systems
Presentation transcript:

Embedded System Lab Kim Jong Hwi Chonbuk National University Introduction to Intelligent Robots

Chonbuk National University  Chapter Objectives Define what the reactive paradigm List the characteristics of a reactive robotic system Describe the two dominant methods for combining behaviors in a reactive architecture: subsumption and potential field summation Be able to program a behavior using a potential field methodology Be able to construct a new potential field form primitive potential fields, and sum potential fields to generate an emergent behavior

Chonbuk National University

 The Reactive Paradigm emerged in the late 1980’s grew out of dissatisfaction with the hierarchical paradigm -summarized by Roney Brooks

Chonbuk National University  The Reactive Paradigm layered in a vertical decomposition access to sensors and actuators independently

Chonbuk National University  Attributes of Reactive Paradigm all actions are accomplished through behaviors motor schema : algorithm for generating the pattern of action in physical actuator perceptual schema : algorithm for extracting the percept and its strength

Chonbuk National University  S-A organization

Chonbuk National University  Behavior-specific sensing organization in the Reactive Paradigm

Chonbuk National University  Connotations of reactive behaviors executes rapidly have no memory

Chonbuk National University  5 Characteristics of reactive behaviors Robots are situated agents operating in an ecological niche Behaviors serve as the basic building blocks for robotic actions, and the overall behavior of the robot is emergent Only local, behavior-specific sensing is permitted These systems inherently follow good software design principle Animal models of behavior are often cited as a basis for these systems or a particular behavior

Chonbuk National University  Representative architectures Subsumption – how behaviors are combined Potential Field Methodologies – require behaviors to be implemented as potential fields and the behaviors are combined by summation of the fields Rule encoding, fuzzy methods, winner-take-all voting …

Chonbuk National University  Subsumption architecture Rodney Brooks’s architecture the most influential of the purely Reactive Paradigm systems

Chonbuk National University  Subsumption architecture Modules are grouped into layers of competence (low layer ~ high layer) Modules in a higher layer can override, or subsume, the output from behaviors in the next lower layer The use of internal state is avoided A task is accomplished by activating the appropriate layer

Chonbuk National University  Example

Chonbuk National University  Level 0 recast as primitive behaviors

Chonbuk National University  Level 1 : wander

Chonbuk National University  Level 1 recast as primitive behaviors

Chonbuk National University  Level 2 : follow corridors

Chonbuk National University  Potential Field force field on the surrounding space exerted from perceivable objects  Vector – Magnitude; real number between 0.0 and 1 – Direction

Chonbuk National University  Potential Field

Chonbuk National University  Potential Field

Chonbuk National University  Programming a single potential Field

Chonbuk National University  Programming a single potential Field

Chonbuk National University  Programming a single potential Field

Chonbuk National University  Programming a single potential Field

Chonbuk National University  Programming a single potential Field

Chonbuk National University  Programming a single potential Field

Chonbuk National University  Programming a single potential Field

Chonbuk National University  Programming a single potential Field

Chonbuk National University  Programming a single potential Field

Chonbuk National University  Advantages and disadvantages Advantages – easy to visualize over a large region of space – easier for the designer to visualize the robot’s overall behavior – easy to combine fields, and languages such as C++ – well works behaviors developed for 2D in 3D inviroment Disadvantages –Local minima (vector with 0 magnitude) » producing vectors with a small magnitude from random noise »NaTs (navigation templates)

Chonbuk National University  Summary subsumption and potential fields appear to be largely equivalent in practice The ease of portability to other domains is relative to the complexity of the changes in the task and enviroment Neither style of architecture explicitly addresses robustness

Chonbuk National University33 Thank you for listening!