AuRA: Autonomous Robot Architecture From: Integrating Behavioral, Perceptual, and World Knowledge in Reactive Navigation Ron Arkin, 1990.

Slides:



Advertisements
Similar presentations
Mobile Robot ApplicationsMobile Robot Applications Textbook: –T. Bräunl Embedded Robotics, Springer 2003 Recommended Reading: 1. J. Jones, A. Flynn: Mobile.
Advertisements

Lecture 7: Potential Fields and Model Predictive Control
Stages of Learning Chapter 5.
EECE499 Computers and Nuclear Energy Electrical and Computer Eng Howard University Dr. Charles Kim Fall 2013 Webpage:
MICHAEL MILFORD, DAVID PRASSER, AND GORDON WYETH FOLAMI ALAMUDUN GRADUATE STUDENT COMPUTER SCIENCE & ENGINEERING TEXAS A&M UNIVERSITY RatSLAM on the Edge:
University of Minho School of Engineering Centre ALGORITMI Uma Escola a Reinventar o Futuro – Semana da Escola de Engenharia - 24 a 27 de Outubro de 2011.
Robotics applications of vision-based action selection Master Project Matteo de Giacomi.
CS 795 – Spring  “Software Systems are increasingly Situated in dynamic, mission critical settings ◦ Operational profile is dynamic, and depends.
Selection and Monitoring of Rover Navigation modes: A Probabilistic Diagnosis Approach Thierry Peynot and Simon Lacroix Robotics and AI group LAAS/CNRS,
Embedded System Lab Kim Jong Hwi Chonbuk National University Introduction to Intelligent Robots.
AuRA: Principles and Practice in Review
Bastien DURAND Karen GODARY-DEJEAN – Lionel LAPIERRE Robin PASSAMA – Didier CRESTANI 27 Janvier 2011 ConecsSdf Architecture de contrôle adaptative : une.
Robotics Intensive: Day 6 Gui Cavalcanti 1/17/2012.
A Robotic Wheelchair for Crowded Public Environments Choi Jung-Yi EE887 Special Topics in Robotics Paper Review E. Prassler, J. Scholz, and.
Autonomous Robot Navigation Panos Trahanias ΗΥ475 Fall 2007.
Autonomous Mobile Robots CPE 470/670 Lecture 11 Instructor: Monica Nicolescu.
Behavior-Based Formation Control for Multi-robot Teams Tucker Balch, and Ronald C. Arkin.
ECE 4340/7340 Exam #2 Review Winter Sensing and Perception CMUcam and image representation (RGB, YUV) Percept; logical sensors Logical redundancy.
Brent Dingle Marco A. Morales Texas A&M University, Spring 2002
Experiences with an Architecture for Intelligent Reactive Agents By R. Peter Bonasso, R. James Firby, Erann Gat, David Kortenkamp, David P Miller, Marc.
Cognitive Colonization Tony Stentz, Martial Hebert, Bruce Digney, Scott Thayer Robotics Institute Carnegie Mellon University.
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.
Integrating POMDP and RL for a Two Layer Simulated Robot Architecture Presented by Alp Sardağ.
Motor Schema Based Navigation for a Mobile Robot: An Approach to Programming by Behavior Ronald C. Arkin Reviewed By: Chris Miles.
Autonomous Mobile Robots CPE 470/670 Lecture 10 Instructor: Monica Nicolescu.
Robotics Industry Posts Second Best Year Ever North American robotics industry posted its second best year ever in 2000 [Robotic Industries Association.
Behavior- Based Approaches Behavior- Based Approaches.
Inventing Hybrid Control The basic idea is simple: we want the best of both worlds (if possible). The goal is to combine closed-loop and open-loop execution.
Robotica Lezione 1. Robotica - Lecture 12 Objectives - I General aspects of robotics –Situated Agents –Autonomous Vehicles –Dynamical Agents Implementing.
Abstract Design Considerations and Future Plans In this project we focus on integrating sensors into a small electrical vehicle to enable it to navigate.
Introduction to Behavior- Based Robotics Based on the book Behavior- Based Robotics by Ronald C. Arkin.
9/14/2015CS225B Kurt Konolige Locomotion of Wheeled Robots 3 wheels are sufficient and guarantee stability Differential drive (TurtleBot) Car drive (Ackerman.
DARPA Mobile Autonomous Robot SoftwareLeslie Pack Kaelbling; March Adaptive Intelligent Mobile Robotics Leslie Pack Kaelbling Artificial Intelligence.
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.
Autonomous Mobile Robots CPE 470/670 Lecture 11 Instructor: Monica Nicolescu.
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.
7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm1 Part 1: Overview & Managerial.
Final Presentation.  Software / hardware combination  Implement Microsoft Robotics Studio  Lego NXT Platform  Flexible Platform.
7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm1 Part 1: Overview & Managerial.
Disturbed Behaviour in Co-operating Autonomous Robots Robert Ghanea-Hercock & David Barnes Salford University, England.
University of Amsterdam Search, Navigate, and Actuate - Qualitative Navigation Arnoud Visser 1 Search, Navigate, and Actuate Qualitative Navigation.
Spatio-Temporal Case-Based Reasoning for Behavioral Selection Maxim Likhachev and Ronald Arkin Mobile Robot Laboratory Georgia Tech.
DARPA ITO/MARS Project Update Vanderbilt University A Software Architecture and Tools for Autonomous Robots that Learn on Mission K. Kawamura, M. Wilkes,
Georgia Tech / Mobile Intelligence 1 Multi-Level Learning in Hybrid Deliberative/Reactive Mobile Robot Architectural Software Systems DARPA MARS Kickoff.
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.
Multiagent System Katia P. Sycara 일반대학원 GE 랩 성연식.
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.
Learning Momentum: Integration and Experimentation Brian Lee and Ronald C. Arkin Mobile Robot Laboratory Georgia Tech Atlanta, GA.
Autonomous Mobile Robots CPE 470/670 Lecture 10 Instructor: Monica Nicolescu.
Extended Kalman Filter
SurveyBOT Final Report Chris Johnson Miguel Lopez Jeremy Coffeen July 24, 2003 Georgia Institute of Technology School of Electrical and Computer Engineering.
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.
Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Neural Networks Laboratory 1 Introduction To Neural Networks.
Slide no 1 Cognitive Systems in FP6 scope and focus Colette Maloney DG Information Society.
Learning Behavioral Parameterization Using Spatio-Temporal Case-Based Reasoning Maxim Likhachev, Michael Kaess, and Ronald C. Arkin Mobile Robot Laboratory.
Functionality of objects through observation and Interaction Ruzena Bajcsy based on Luca Bogoni’s Ph.D thesis April 2016.
Robotic Architectures: Schema Lecture 7a. Motor Schemas ● Based upon schema theory: Explains motor behavior in terms of concurrent control of many different.
Intelligent Mobile Robotics
Spatial Semantic Hierarchy (SSH)
Trends in Robotics Research
CIS 488/588 Bruce R. Maxim UM-Dearborn
Locomotion of Wheeled Robots
CIS 488/588 Bruce R. Maxim UM-Dearborn
CS 4630: Intelligent Robotics and Perception
Chapter 7: Hybrid Deliberative/Reactive Paradigm
Extended Kalman Filter
Extended Kalman Filter
Performance Monitoring and Feedback
Presentation transcript:

AuRA: Autonomous Robot Architecture From: Integrating Behavioral, Perceptual, and World Knowledge in Reactive Navigation Ron Arkin, 1990

Architecture

Hierarchical Planning 1. Generate possible linear segments

Hierarchical Planning 2. Find a piecewise linear plath

Hierarchical Planning 3. Generate a sequence of schema groupings

Using World Knowledge & Reactive Schemas World knowledge can be used to determine the general area in which to move or look for a landmark When the landmark is found, then reactive navigation takes over Arkin’s claim: World knowledge is necessary for efficient, flexible, and generalizable navigation Do you agree?

Docking

Docking in a Cluttered Environment

Long Term Memory for persistent, a priori info Short Term Memory for dynamically acquired info Cartographer for building a map Homeostati c control to monitor internal conditions

Discussion Questions What is the best way of handling traps in vector potential fields? Noise addition, deadlines, or ?? Can the noise schema ever cause the robot to become trapped beyond its capability to recover? Are there times when the methodology of low-level reactive planning will fail? What would happen if a motor schema failed to activate correctly, say, due to a hardware failure? Could the robot self-correct?

Discussion Questions The output of the schemas is a velocity vector. Are we assuming a nearly instantaneous change in velocity or a delay? In the experiments conducted, a particular velocity is used. What if the velocity were increased? How does the schema-based navigation relate to the way humans make navigational decisions? How would the potential field navigation be adaptable to 3 dimensions?

3D Schema-Based Navigation

Discussion Questions If the perceptual schema confidence exceeds the motor schema threshold for action, the motor schema starts to produce a repulsive field surrounding the obstacle. Why? What happens if obstacles suddenly appear or disappear? How does the robot adapt? What environments are idea for the use of schema-based navigation?