School of Computer Science

Module 06-22382 (2017)

Robot Programming

Level 1/C

Phillip Smith Semester 2 10 credits
Co-ordinator: Phillip Smith
Reviewer: Jeremy Wyatt

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


This module teaches basic AI and robotic programming skills through a series of team exercises using small, mostly prebuilt, robots. Regular exercises will give each team the skills to build up a robot capable of tackling a competitive, arena-based, task that includes a variety of AI-requiring sub-problems.


The aims of this module are to:

  • present some of the core problems in building and programming intelligent robots (design trade-offs, choices of sensors etc.)
  • illustrate how problems can be decomposed
  • demonstrate how Java can be used to implement AI algorithms (e.g. informed search) and robot control paradigms (e.g. feeback controllers, the subsumption architecture)
  • present good programming and debugging practice in Java in an applied setting

Learning Outcomes

On successful completion of this module, the student should be able to:

  1. implement and deploy Java programs on a robot using the tools provided
  2. apply AI and Java knowledge to implement some classic AI/robotics representations and techniques
  3. develop a modular robotic system over an extended period of time



Teaching methods

Lectures, labs, group work.

Contact Hours: 22 hours taught lecture, 4 hours/week lab time


Sessional: continuous assessment (100%), via regular group exercises, assessed via demonstration and code submission.

Supplementary (where allowed): By repeat only

Exercises will increase in complexity over time. Early exercise will encourage the development of basic skills and techniques. Later exercises will require the application of these skills to solve more advanced problems.

Detailed Syllabus

  1. Basic knowledge
    • The NXT hardware.
    • The leJOS Java software.
    • Software Engineering for Robot Programming
  2. Dealing with sensors and motors
    • Motor control.
    • Event handling and polling.
    • The basic NXT sensors.
    • Colour-based vision and the NXTCam
  3. Sensor-based control
    • Feedback control
    • The Subsumption Architecture
  4. More advanced knowledge
    • Java collections: ArrayList, Stack and Queue
    • Sorting
    • Generic programming
  5. Search-based control
    • Uniformed search
    • Informed search
    • Adversarial search
  6. Navigation
    • Mapping and localisation
    • Probabilistic road maps

Programmes containing this module