Module manager: Prof Peter Culmer
Email: P.R.Culmer@leeds.ac.uk
Taught: Semesters 1 & 2 (Sep to Jun) View Timetable
Year running 2026/27
| MECH1010 | Computers in Engineering Analysis |
| MECH2620 | Vibration and Control |
| MECH2650 | Mechatronics & Measurement Sys |
| MECH3225 | Biomedical Engineering Design |
| MECH3470 | Vehicle Design and Analysis |
This module is not approved as a discovery module
This module will provide students with technical skills in the design and construction of robotic devices, programming of robotic controllers, as well as an understanding of their limitations. The module will also provide an introduction to Machine (or Artificial) intelligence (where systems emulate the human mind to learn, solve problems and make decisions without needing the instructions specifically programmed), considering applications in machine vision as exemplars. The module will also explore the ethical and societal implications of robotics and AI, as well as the challenges faced in designing and building intelligent machines.
On completion of this module students should have acquired a good understanding of:
1. the scientific principles of robotic system design i.e. sensors, actuators, powering methods, controllers, navigation. Students will be able to select robot components, perform kinematic analysis of robot movement using MATLAB and SolidWorks. Having completed this section student should be able to formulate a design of a robotic system that satisfies a given requirement (select, compare, contrast, understand limitations and apply appropriate methods).
2. the fundamental aspects of Machine Learning, including foundational techniques (classifiers, State Vector Machines) and the development and application of Artificial Neural Networks, using modern Python computational tools and associated Machine Learning frameworks.
On successful completion of the module students will be able to:
1. describe the different mechanical configurations for robot manipulators;
2. choose appropriate robot components for a given application (sensors, actuators, powering method, configurations, controllers etc.);
3. undertake kinematic analysis of robot manipulators;
4. analyse the dynamics of planar manipulations;
5. understand the methods for localisation;
6. appreciate the methods of navigation;
7. understanding the basic concepts of mobile robot systems;
8. understand basic concepts in machine vision and decision making;
9. describe the social and economic impact of industrial and service robots;
10. appreciate the current state and potential for robotics in new application areas (e.g. medical).
These module learning outcomes contribute to the following AHEP4 learning outcomes:
- Apply knowledge of mathematics, statistics, natural science and engineering principles to the solution of complex problems. Some of the knowledge will be at the forefront of the particular subject of study. [C1]
- Analyse complex problems to reach substantiated conclusions using first principles of mathematics, statistics, natural science and engineering principles. [C2]
- Select and apply appropriate computational and analytical techniques to model complex problems, recognising the limitations of the techniques employed. [C3]
Skills learning outcomes:Â
On successful completion of the module students will be able to demonstrate skills in:
a. information technology;
b. teamwork/collaboration;
c. critical thinking;
d. active learning;
e. systems thinking;
f. integrated problem solving;
g. effective communication.
PART I: INDUSTRIAL ROBOT MANIPULATORS
- Introduction to Robotic Systems: Industrial, Medical, Mobile
- History: Economic and Social Impacts of Robotics
- Robot Configurations: Degrees of Freedom and Transformations
- Robot Kinematic Design: Forward and Inverse
- Design: Actuators, End Effectors and Mechanisms
- Dynamics & Control: Trajectory and Waypoint Design
- Weekly Practical’s: Using MATLAB Robotics Toolbox
- Design of Robotic Systems using SolidWorks to produce URDF models
Part II: MACHINE INTELLIGENCE
- Fundamentals of Machine Learning and Optimisation
- State of Art Artificial Neural Network Architectures
- Artificial Neural Network Applications in Computer Vision and Intelligent Control
- Industrial Standards with the OpenVINO toolkit
- Biologically inspired Robots in Neuromorphic Robotics.
Methods of assessment
The assessment details for this module will be provided at the start of the academic year.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 44 | 1 | 44 |
| Practical | 2 | 2 | 4 |
| Private study hours | 152 | ||
| Total Contact hours | 48 | ||
| Total hours (100hr per 10 credits) | 200 | ||
Skills in robotics are best established through a combination of taught material supported by problem sheets and worked assignments provide a route to develop the required skills and provide formative feedback.
Check the module area in Minerva for your reading list
Last updated: 30/04/2026
Errors, omissions, failed links etc should be notified to the Catalogue Team