Module 06-15267 (2019)

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. In this module we will address these issues in the context of intelligent mobile robots. The lectures will teach theories of perception, estimation, prediction, decision- making, learning and control, all from the perspective of robotics. In the laboratory sessions students will implement some of these theories on real robots to see how theory can be applied in practice.

• 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

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.

• 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).

May not be taken by students who have already completed or are currently registered for LH non-extended version

Approximately 27 lectures and 22 laboratory sessions

Contact Hours: 50

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

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