School of Computer Science

Module 06-15267 (2010)

Intelligent Robotics (Extended)

Level 4/M

Noel Welsh Semester 1 20 credits
Co-ordinator: Jeremy Wyatt
Reviewer: Richard Dearden

The Module Description is a strict subset of this Syllabus Page.

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:

  • design, build and program simple autonomous robots
  • implement standard signal processing and control algorithms
  • describe and analyse robot processes using appropriate methods
  • write a detailed report on a robot project
  • carry out and write up investigations using appropriate experimental methods
  • show appreciation of the research literature in one subfield of intelligent robotics

Taught with


Teaching methods

Approximately 18 lectures, 6 student presentations and 24 laboratory sessions


Assessment

  • Sessional: Continuous assessment (100%).
  • Supplementary: None; the module may only be repeated.

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

Programmes containing this module