Module 06-13520 (2011)
Intelligent Robotics
Level 3/H
Jeremy Wyatt | Semester 1 | 20 credits |
Co-ordinator: Jeremy Wyatt
Reviewer: Nicholas Hawes
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
Taught with
- 06-15267 - Intelligent Robotics (Extended)
Teaching methods
Approximately 18 lectures and 24 laboratory sessions
Assessment
- Sessional: Continuous assessment (100%).
- Supplementary: None; the module may only be repeated.
Detailed Syllabus
- Introduction
- What is robotics?
- Robotics and AI
- Embedded Systems
- Agent-Task-Environment model
- Embodied Systems
- Synthetic approaches to science
- Sensors and signal processing
- Common sensors and their properties
- 1D signal processing
- Vision
- Planning approaches to robot control
- STRIPS and SHAKEY
- Robot kinematics
- Limitations of planning approaches
- Control Theory
- Feedback, feedforward and open loop control
- Linear first order lag processes
- Limitations of control theory
- Probability Based Approaches
- Markov Decision Processes (MDPs)
- Partially Observable Markov Decision Processes
- Navigation using POMDPs
- Kinematics and Motion Planning
- Kinematics of differential drive robots
- Probabilistic road map planning
- Behaviour-Based Control
- The subsumption architecture
- Hybrid architectures
- Formalising behaviour based control
- Adaptive approaches to robot control
- Reinforcement learning for control
- Model based learning approaches to control
- Learning maps
- Evolutionary approaches
- Architectures for control
- CAS
- ROS
- Current research topics
- Learning for manipulation
- Gaze control
- Planning visual search
Programmes containing this module
- BEng Computer Science/Software Engineering [4753]
- BEng Computer Science/Software Engineering with an Industrial Year [9500]
- BSc Computer Science [4436]
- BSc Computer Science with an Industrial Year [9499]
- BSc Computer Science with Study Abroad [5571]
- MEng Computer Science/Software Engineering [4754]
- MEng Computer Science/Software Engineering with an Industrial Year [9501]