Robotics and Autonomous Systems


  1. To introduce the student to the concept of an autonomous agent;
  2. To introduce the key approaches developed for decision-making in autonomous systems;
  3. To introduce a contemporary platform for programming agents and multiagent systems;
  4. To introduce the key issues surrounding the development of autonomous robots;
  5. To introduce a contemporary platform for experimental robotics.


1. Strand one (2 hours lectures per week): Principles of Autonomous Agents.

what is an agent: agents and objects; agents and expert systems; agents and distributed systems; typical application areas for agent systems. abstract architectures for agents; tasks for agents; the design of intelligent agents - reasoning agents (e.g., Agent0), agents as reactive systems (e.g. subsumption architecture); hybrid agents (e.g., PRS); layered agents (e.g. Interrap); a contemporary programming language for autonomous robots (e.g., AgentSpeak).

2. Strand two (1 hour lectures per week): Principles of Robotics.

The sense -- decide -- act loop. Sensors: passive versus active sensors; light sensors; infra-red sensors; . . . Actuators: motors & servo motors; gearing; manipulators, . . .Movement: path planning; localisation; SLAM; . . .A contemporary experimental robotics platform (eg MINDSTORMS).

Recommended Texts

Teaching on the module will follow the following textbook closely:

  • M. Wooldridge: An Introduction to MultiAgent Systems, Second Edition. John Wiley & Sons, 2009.

Other reading:

  • M. Mataric: The robotics primer. MIT Press (most recent edition).
  • R. Murphy: Introduction to AI Robotics. MIT Press (most recent edition).

Learning Outcomes

At the end of the module the student will be able to:

  1. explain the notion of an agent, how agents are distinct from other software paradigms (e.g., objects), and judge the characteristics of applications that lend themselves to an agent-oriented solution;
  2. identify the key issues associated with constructing agents capable of intelligent autonomous action;
  3. describe the main approaches taken to developing such agents;
  4. use a contemporary agent programming platform (e.g., AgentSpeak) for developing significant software or hardware-based agents;
  5. identify key issues involved in building agents that must sense and act within the physical world;
  6. program and deploy autonomous robots for specific tasks.

Learning Strategy

Formal Lectures: In a typical week students will be expected to attend three hours of formal lectures per week. Formal lectures will be used to introduce students to the concepts and methods covered by the module.

Practicals: In a typical week students will be expected to attend one hour practicals in the newly established robot lab. These practicals are intended to allow students to undertake exercises with robots with the possibility of immediate feedback.

Private study: In a typical week students will be expected to devote about 6 hours of unsupervised time to private study. Private study will provide time for reflection and consideration of lecture material, background reading and completion of the assessment tasks.

Assessment: The module will be 100% continuously assessed, which will take the form of series of robot programming tasks to be carried out in the robot lab. The tasks will be team-based.