Module 13520 (2009)

Syllabus page 2009/2010

06-13520
Intelligent Robotics

Level 3/H

Jeremy Wyatt
20 credits in Semester 1

Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus


The Module Description is a strict subset of this Syllabus Page. (The University module description has not yet been checked against the School's.)

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Outline

Artificial Intelligence is concerned with mechanisms for generating intelligent behaviour. When this behaviour occurs in the everyday physical world, with its uncertainty and rapid change, we find that all kinds of new problems and opportunities arise. We will try to understand some of these in the context of robotics. In a series of lectures we will look at some theories of how to sense the real world, and act intelligently in it. In a series of labs you will build your own robots to see how well (or badly) these theories actually work.


Aims

The aims of this module are to:

  • give an appreciation of the issues that arise when designing complete, physically embodied autonomous agents
  • introduce some of the most popular methods for controlling autonomous mobile robots
  • give hands on experience of engineering design
  • encourage independent thought on possible cognitive architectures for autonomous agents

Learning Outcomes

On successful completion of this module, the student should be able to: Assessed by:
1design, build and program simple autonomous robots Team Project
2implement standard signal processing and control algorithms Team Project
3describe and analyse robot processes using appropriate methods Team Project
4write a detailed report on a robot project Team Project
5carry out and write up investigations using appropriate experimental methods Team Project

Restrictions, Prerequisites and Corequisites

Restrictions:

Available only to School of Computer Science students; registration limited to approx. 42 in combination with 06-15267 (Intelligent Robotics (Extended)). May not be taken by anyone who has taken or is taking 06-15267 (Intelligent Robotics (Extended)).

Prerequisites:

None

Co-requisites:

None


Teaching

Teaching Methods:

Approximately 18 lectures and 24 laboratory sessions

Contact Hours:

44


Assessment

  • Sessional: Continuous assessment (100%).
  • Supplementary (where allowed): None; the module may only be repeated.
  • The continuous assessment consists of a writeup and demonstration of an autonomous robot project designed and constructed by each team.

Recommended Books

TitleAuthor(s)Publisher, Date
Behavior Based RoboticsR ArkinMIT Press, 1998
Robotic Explorations: A Hands-on Introduction to EngineeringF MartinAddison-Wesley, 2001
Vehicles: Experiments in Synthetic PsychologyV BraitenbergMIT Press, 1984
Lecture Notes on Intelligent Robotics, and lab handoutsJeremy Wyatt
Mobile Robotics: A practical introductionU NehmzowSpringer Verlag, 2000
Robot LearningS Mahadevan and J ConnellKluwer Academic, 1993
Mobile Robots: Inspiration to Implementation (2nd ed.)J Jones, B Seiger and A FlynnAK Peters, 1999

Detailed Syllabus

  1. Introduction
    • What is robotics?
    • Robotics and AI
    • Embedded Systems
    • Agent-Task-Environment model
    • Embodied Systems
    • Synthetic approaches to science
  2. Sensors and signal processing
    • Common sensors and their properties
    • 1D signal processing
    • Vision
  3. Planning approaches to robot control
    • STRIPS and SHAKEY
    • Robot kinematics
    • Limitations of planning approaches
  4. Control Theory
    • Feedback, feedforward and open loop control
    • Linear first order lag processes
    • Limitations of control theory
  5. Probability Based Approaches
    • Markov Decision Processes (MDPs)
    • Partially Observable Markov Decision Processes
    • Navigation using POMDPs
  6. Behaviour-Based Control
    • The subsumption architecture
    • Hybrid architectures
    • Formalising behaviour based control (SMDPs)
  7. Adaptive approaches to robot control
    • Reinforcement learning for control
    • Model based learning approaches to control
    • Learning maps
    • Evolutionary approaches
  8. Case studies and applications

Last updated: 19 Sep 2003

Source file: /internal/modules/COMSCI/2009/xml/13520.xml

Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus