- Faculty of Engineering Sciences
- Teaching department
- Computer Science
- Credit value
- This module is restricted to students registered on: MSc Computational Statistics and Machine Learning MSc Data Science and Machine Learning MSc Machine Learning MRes Robotics MSc Robotics and Computation
Alternative credit options
There are no alternative credit options available for this module.
The students will gain insight into robotics systems and the general concepts, mathematic and algorithms that underpin moving and actuating robotic arms and devices. Specific topics we cover: fundamental linear algebra, transformations, kinematics and inverse kinematics, actuation dynamics and mechanisms, and motion planning. These will all be applied to problems in simulation and programming of robotic systems.
On successful completion of the module, a student will be able to:
1. Understand robot kinematics;
2. Understand robot motion planning;
3. Understand different robotic mechanisms, specifically robotic arms;
4. Programme with Python and ROS (optional C++);
5. Apply learned theory to programming solutions for robotics problems in simulation.
-The aim of this module is to provide the basic theory required for solving problems involving the motion of robotics and autonomous systems from a practitioner point of view;
-The module presents theory and methodology for analysis and modelling of robot kinematics, and methods for moving robots within workspaces. Special emphasis is placed on:
-Linear algebra needed for robot motion and transformation;
-Robot kinematics and DH tables;
-Inverse kinematics and solving inverse systems;
-Planning and executing robot motion;
-Theoretical lectures will be accompanied by corresponding practical exercises using ROS and predominantly carried out in simulation;
In order to be eligible to select this module, a student must be registered on a programme for which it is a formally-approved option or elective choice AND must (i) be able to use Linux and have some background/experience in programming, especially using Python (and preferably ROS); (ii) be comfortable with linear algebra mathematics; and (iii) for the next academic year (2019-2020), the module will run on Ubuntu 18.04 (Bionic) and the lab materials/coursework will run on ROS Melodic. Students are required to have a laptop that has a minimum of 2GHz dual core processor, 2GB RAM, 30-40 GB of hard-drive space and an internet connection, can run an installation of ROS on Linux and be used during lab sessions.
Module deliveries for 2020/21 academic year
Intended teaching term: Term 1 Postgraduate (FHEQ Level 7)
Teaching and assessment
- Mode of study
- Methods of assessment
20% Coursework 130% Coursework 250% Coursework 3
- Mark scheme
- Numeric Marks
- Number of students on module in previous year
- Module leader
- Professor Danail Stoyanov
- Who to contact for more information
This module description was last updated on 5th March 2020.