Introduction to Computer Science

Similar documents
Hybrid architectures. IAR Lecture 6 Barbara Webb

ROBOTICS ENG YOUSEF A. SHATNAWI INTRODUCTION

Outline. Agents and environments Rationality PEAS (Performance measure, Environment, Actuators, Sensors) Environment types Agent types

Autonomous and Mobile Robotics Prof. Giuseppe Oriolo. Introduction: Applications, Problems, Architectures

Lecture 23: Robotics. Instructor: Joelle Pineau Class web page: What is a robot?

Agent. Pengju Ren. Institute of Artificial Intelligence and Robotics

Robotics Enabling Autonomy in Challenging Environments

EE631 Cooperating Autonomous Mobile Robots. Lecture 1: Introduction. Prof. Yi Guo ECE Department

Last Time: Acting Humanly: The Full Turing Test

Recommended Text. Logistics. Course Logistics. Intelligent Robotic Systems

CORC Exploring Robotics. Unit A: Introduction To Robotics

Advanced Robotics Introduction

Revised and extended. Accompanies this course pages heavier Perception treated more thoroughly. 1 - Introduction

CS494/594: Software for Intelligent Robotics

Funzionalità per la navigazione di robot mobili. Corso di Robotica Prof. Davide Brugali Università degli Studi di Bergamo

Robo$cs Introduc$on. ROS Workshop. Faculty of Informa$on Technology, Brno University of Technology Bozetechova 2, Brno

Plan for the 2nd hour. What is AI. Acting humanly: The Turing test. EDAF70: Applied Artificial Intelligence Agents (Chapter 2 of AIMA)

Advanced Robotics Introduction

COS Lecture 1 Autonomous Robot Navigation

Using FMI/ SSP for Development of Autonomous Driving

What is a robot? Introduction. Some Current State-of-the-Art Robots. More State-of-the-Art Research Robots. Version:

EE631 Cooperating Autonomous Mobile Robots. Lecture 1: Introduction. Prof. Yi Guo ECE Department

Overview Agents, environments, typical components

HIT3002: Introduction to Artificial Intelligence

CS 380: ARTIFICIAL INTELLIGENCE RATIONAL AGENTS. Santiago Ontañón

Autonomous Vehicle Simulation (MDAS.ai)

Glossary of terms. Short explanation

MULTI-LAYERED HYBRID ARCHITECTURE TO SOLVE COMPLEX TASKS OF AN AUTONOMOUS MOBILE ROBOT

Structure of Intelligent Agents. Examples of Agents 1. Examples of Agents 2. Intelligent Agents and their Environments. An agent:

CMPUT 412 Introduction. Csaba Szepesvári University of Alberta

VSI Labs The Build Up of Automated Driving

Cognitive Robotics 2017/2018

Cognitive Robotics 2016/2017

Intelligent Agents p.1/25. Intelligent Agents. Chapter 2

MEM380 Applied Autonomous Robots I Winter Feedback Control USARSim

CS148 - Building Intelligent Robots Lecture 2: Robotics Introduction and Philosophy. Instructor: Chad Jenkins (cjenkins)

Inf2D 01: Intelligent Agents and their Environments

Human-robot relation. Human-robot relation

Key-Words: - Fuzzy Behaviour Controls, Multiple Target Tracking, Obstacle Avoidance, Ultrasonic Range Finders

ROBOTICS 01PEEQW. Basilio Bona DAUIN Politecnico di Torino

A Robust Neural Robot Navigation Using a Combination of Deliberative and Reactive Control Architectures

CS 380: ARTIFICIAL INTELLIGENCE

Introduction to Robotics

CPS331 Lecture: Intelligent Agents last revised July 25, 2018

Robotics Introduction Matteo Matteucci

Planning in autonomous mobile robotics

Humanoid robot. Honda's ASIMO, an example of a humanoid robot

Collective Robotics. Marcin Pilat

CISC 1600 Lecture 3.4 Agent-based programming

Key-Words: - Neural Networks, Cerebellum, Cerebellar Model Articulation Controller (CMAC), Auto-pilot

CS 730/830: Intro AI. Prof. Wheeler Ruml. TA Bence Cserna. Thinking inside the box. 5 handouts: course info, project info, schedule, slides, asst 1

DENSO

Robotics and Autonomous Systems

Introduction to Mobile Robotics Welcome

Computational Principles of Mobile Robotics

Russell and Norvig: an active, artificial agent. continuum of physical configurations and motions

Distribution Statement A (Approved for Public Release, Distribution Unlimited)

CORC 3303 Exploring Robotics. Why Teams?

* Intelli Robotic Wheel Chair for Specialty Operations & Physically Challenged

Unit 1: Introduction to Autonomous Robotics

Human Robot Interaction (HRI)

CS 486/686 Artificial Intelligence

Autonomous Control for Unmanned

Teleoperation and System Health Monitoring Mo-Yuen Chow, Ph.D.

Intro to Intelligent Robotics EXAM Spring 2008, Page 1 of 9

Real-time Cooperative Behavior for Tactical Mobile Robot Teams. September 10, 1998 Ronald C. Arkin and Thomas R. Collins Georgia Tech

Lecture Overview. c D. Poole and A. Mackworth 2017 Artificial Intelligence, Lecture 1.1, Page 1 1 / 15

Autonomous Robotics. CS Fall Amarda Shehu. Department of Computer Science George Mason University

TECHNOLOGY DEVELOPMENT AREAS IN AAWA

Automation and Mechatronics Engineering Program. Your Path Towards Success

Service Robots in an Intelligent House

Intelligent Agents & Search Problem Formulation. AIMA, Chapters 2,

Cognitive robots and emotional intelligence Cloud robotics Ethical, legal and social issues of robotic Construction robots Human activities in many

NCCT IEEE PROJECTS ADVANCED ROBOTICS SOLUTIONS. Latest Projects, in various Domains. Promise for the Best Projects

Robotics Evolution: From Production Rate to Human Productivity

Building Perceptive Robots with INTEL Euclid Development kit

Mobile Robots (Wheeled) (Take class notes)

Introduction to Vision & Robotics

CPS331 Lecture: Agents and Robots last revised April 27, 2012

Jane Li. Assistant Professor Mechanical Engineering Department, Robotic Engineering Program Worcester Polytechnic Institute

Autonomous Mobile Robots

COMP9414/ 9814/ 3411: Artificial Intelligence. Week 2. Classifying AI Tasks

Course Info. CS 486/686 Artificial Intelligence. Outline. Artificial Intelligence (AI)

Combining Artificial Neural Networks and Symbolic Processing for Autonomous Robot Guidance

What is a robot. Robots (seen as artificial beings) appeared in books and movies long before real applications. Basilio Bona ROBOTICS 01PEEQW

Overview of Challenges in the Development of Autonomous Mobile Robots. August 23, 2011

CS686: High-level Motion/Path Planning Applications

Robot: Robonaut 2 The first humanoid robot to go to outer space

CPS331 Lecture: Agents and Robots last revised November 18, 2016

Eurathlon Scenario Application Paper (SAP) Review Sheet

Dipartimento di Elettronica Informazione e Bioingegneria Robotics

Moving Obstacle Avoidance for Mobile Robot Moving on Designated Path

Robosemantics: How Stanley the Volkswagen Represents the World

Intelligent Robotics Sensors and Actuators

Available theses (October 2012) MERLIN Group

SEMI AUTONOMOUS CONTROL OF AN EMERGENCY RESPONSE ROBOT. Josh Levinger, Andreas Hofmann, Daniel Theobald

Available theses (October 2011) MERLIN Group

MTRX 4700 : Experimental Robotics

Administrivia. CS 188: Artificial Intelligence Spring Agents and Environments. Today. Vacuum-Cleaner World. A Reflex Vacuum-Cleaner

The Future of AI A Robotics Perspective

Transcription:

Introduction to Computer Science CSCI 109 Andrew Goodney Fall 2017 China Tianhe-2 Robotics Nov. 20, 2017

Schedule 1

Robotics ì Acting on the physical world 2

What is robotics? uthe study of the intelligent connection of perception to action [Brady] uoperationally: An intelligent robot is a machine able to extract information from its environment and use knowledge about its world to move safely and perform tasks in a meaningful and purposeful manner 3

What makes a robot? Sensors, effectors, locomotion/manipulation system, and an on-board computer system 4

What can be sensed? udepends on the sensors on the robot uthe robot exists in its sensor space (all possible values of sensory readings) ualso called perceptual space urobot sensors are very different from biological ones ua roboticist has to try to imagine the world in the robot s sensor space 5

State ua sufficient description of the system ucan be: v Observable: robot always knows its state v Hidden/inaccessible/unobservable: robot never knows its state v Partially observable: the robot knows a part of its state v Discrete (e.g., up, down, blue, red) v Continuous (e.g., 3.765 mph) 6

Types of state uexternal state: state of the world v Sensed using the robot s sensors v E.g.: night, day, at-home, sleeping, sunny uinternal state: state of the robot v Sensed using internal sensors v Stored/remembered v E.g.: velocity, mood uthe robot s state is a combination of its external and internal state 7

State and intelligence ustate space: all possible states the system can be in ua challenge: sensors do not provide state! v Examples? uhow intelligent a robot appears is strongly dependent on how much it can sense about its environment and about itself 8

Internal models uinternal state can be used to remember information about the world (e.g., remember paths to the goal, remember maps, remember friends vs. enemies, etc.) uthis is called a representation or an internal model urepresentations/models have a lot to do with how complicated the control program on the robot needs to be 9

Actuators ua robot acts through its actuators (e.g. motors), which typically drive effectors (e.g., wheels) urobotic actuators are very different from biological ones, both are used for: v locomotion (moving around, going places) v manipulation (handling objects) u This divides robotics into two areas v mobile robotics v manipulator robotics 10

Actions and behavior ubehavior is what an external observer sees a robot doing. urobots are programmed to display desired behavior. ubehavior is a result of a sequence of robot actions. uobserving behavior may not tell us much about the internal control of a robot. Control can be a black box. 11

Autonomy uautonomy is the ability to make one s own decisions and act on them. ufor robots, autonomy means the ability to sense and act on a given situation appropriately. uautonomy can be: v complete (e.g., R2D2) v partial (e.g., tele-operated robots) 12

Control urobot control refers to the way in which the sensing and action of a robot are coordinated. uthe many different ways in which robots can be controlled all fall along a well-defined spectrum of control. v Reactive Control: Don t think, (re)act. v Behavior-Based Control: Think the way you act. v Deliberative Control: Think hard, act later. v Hybrid Control: Think & act independently, in parallel. 13

Control tradeoffs uthinking is slow ureaction must be fast uthinking enables looking ahead (planning) to avoid bad solutions uthinking too long can be dangerous (e.g., falling off a cliff, being run over) uto think, the robot needs (a lot of) accurate information => world models. 14

A historical note: reactive beginnings https://www.youtube.com/watch?v=llulrlmxkko 15

A historical note: Shakey and planning u First general-purpose mobile robot to be able to reason about its own actions u Could analyze each human command and break it down into basic chunks autonomously a planning process u https://www.youtube.com/watc h?v=qxdn6ynwpii 16

Where we are today u Boston Dynamics leading robot firm v Specializes in humanoid and similar u https://www.youtube.com/watch?v=frj34o4hn4i 17

Robotics today 18

Robotics today uhow is the software/control on these organized? v Self-driving car v Industrial robots v Mars rovers v Underwater vehicle uhumanoids near LA v DARPA robotics challenge (http://www.theroboticschallenge.org/) 19

Self driving cars 20

How many players? u 33 according to CB Insights, Aug 2016 u Could be as many as 100 worldwide 21

Sensing on a self-driving car u GPS unit, Inertial navigation system, Laser rangefinders, Radar, Cameras u Position and orientation from GPS + inertial navigation system (localization) u Laser, radar and cameras used to build a threedimensional image of environment (mapping) u Interplay of localization and mapping 22

Control u Control is hybrid (mix of deliberative and reactive) u Car maintains an internal map of their world u Uses the map to find an optimal path to destination that avoids obstacles (e.g., pedestrians and other vehicles) from a set of possible paths. u Once the best path is determined, it is broken down into commands, which are fed to the car s actuators. These control the car s steering, braking and throttle 23

A typical piece of a map 24

Modern approaches tradeoff u How much computation on the car vs. cloud u How much to rely on what is being sensed vs. what is already in the map u How often to update the map u How much to rely on human driver u How much to rely on sensors embedded in the road u How to signal intentions to human drivers u How much to automate the environment (e.g., traffic lights) 25

Next week: final review + quiz 26

Quiz #7 u http://bit.ly/2zsz3mr 27