School of Computer Science

Module 06-15267 (2017)

Intelligent Robotics (Extended)

Level 4/M

Jeremy Wyatt Semester 1 20 credits
Co-ordinator: Jeremy Wyatt
Reviewer: Nicholas Hawes

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

Outline

None


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:

  1. design, build and program simple autonomous robots
  2. implement standard signal processing and control algorithms
  3. describe and analyse robot processes using appropriate methods
  4. write a detailed report on a robot project
  5. carry out and write up investigations using appropriate experimental methods
  6. describe, use, analyse and discuss the properties of a variety of algorithms in the robotics literature.

Restrictions

  • Note 1 There is a limit on the number of students allowed to take this module.

Available only to School of Computer Science students; registration limited to approx. 42 in combination with 06-13520 (Intelligent Robotics). May not be taken by anyone who has taken or is taking 06-13520 (Intelligent Robotics).


Taught with

Cannot be taken with


Teaching methods

Approximately 27 lectures and 22 laboratory sessions

Contact Hours: 50


Assessment

Sessional: 2 hour examination (40%), continuous assessment (60%)

Supplementary (where allowed): 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. Kinematics and Motion Planning
    • Kinematics of differential drive robots
    • Probabilistic road map planning
  7. Behaviour-Based Control
    • The subsumption architecture
    • Hybrid architectures
    • Formalising behaviour based control
  8. Adaptive approaches to robot control
    • Reinforcement learning for control
    • Model based learning approaches to control
    • Learning maps
    • Evolutionary approaches
  9. Architectures for control
    • CAS
    • ROS
  10. Current research topics
    • Learning for manipulation
    • Gaze control
    • Planning visual search

Programmes containing this module