CE315-6-SP-CO:
Mobile Robotics
2016/17
Computer Science and Electronic Engineering (School of)
Colchester Campus
Spring
Undergraduate: Level 6
Current
15
12 April 2010
Requisites for this module
(none)
(none)
(none)
(none)
(none)
BENGH615 Robotic Engineering,
BENGH616 Robotic Engineering (Including Year Abroad),
BENGH617 Robotic Engineering (Including Placement Year)
Learning Outcomes
The aim of this module is to provide a general understanding of AI robotics that has widely potential applications in the real world. Various different approaches are reviewed together with associated design methodologies. Autonomous mobile robots are intelligent machines that have many embedded computers, sensors and actuators which interact intelligently in the real world. They are generally characterised by real-time performance, autonomous operation
and learning capabilities.
After completing this module, students will be expected to:
1. be aware of the rich variety of AI robotics applications in the real world;
2. understand the performance needs of mobile robots in terms of characteristics such as real-time operation, autonomy, asynchronous event handling, modularity, flexibility and robustness;
3. appreciate the advanced computer architectures that may be adopted to build intelligent machines in general, mobile robots in particular;
4. recognise different methods for data interpretation and representation, including sensor uncertainty, local and global map building, as well as multi-sensor data fusion
5. be able to design, program and evaluate autonomous mobile robots and intelligent machines, from sensing to action.
Outline Syllabus
. Introduction to the course: review of AI robotic systems and embedded computing architectures.
. Application domain characteristics: the complex, unpredictable and dynamic natures of the world; timeliness, autonomy and intelligence.
. Intelligent embedded machine characteristics: uncertainty such as sensor noise,
imprecision & sparseness of data; slow processing and small memory; field support such as user-interface and tools.
. Architectures for mobile robots & intelligent machines: comparison of reactive versus cognitive architecture; examination of hierarchical sensory-interactive and behaviour based approaches.
. Data interpretation & representation: local and global map building such as quadtree, occupancy grid, Veronoi diagram; representation of uncertainty; multi-sensor data fusion.
. Implementation issues: mapping models to hardware and software via modularization; configuration flexibility; multi/distributed processing; development tools and simulation environment.
No information available.
No information available.
STUDENTS SHOULD NOTE THAT THIS MODULE INFORMATION IS SUBJECT TO REVIEW AND CHANGE.
BASIC C/C++ PROGRAMMING SKILLS ARE NEEDED
Lectures and Laboratories
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Coursework |
Assignment 1- Simulation - wk21 |
|
50% |
| Coursework |
Assignment 2 - Real robot - wk 30 |
|
50% |
| Exam |
Main exam: 120 minutes during Summer (Main Period)
|
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
Professor Huosheng Hu
CSEE School Office, email: csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770
Yes
No
No
DR Pandelis Kourtessis
University of Hertfordshire
Reader
Available via Moodle
Of 62 hours, 22 (35.5%) hours available to students:
40 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
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