Optimisation Methods
| Module title | Optimisation Methods |
|---|---|
| Module code | ENSM003 |
| Academic year | 2025/6 |
| Credits | 15 |
| Module staff |
Module description
The module introduces you to key optimisation techniques in robotics. It begins with quadratic cost minimisation framed probabilistically as a product of Gaussians. You will learn Newton’s method and the Gauss-Newton algorithm for minimisation, and least squares with constraints. The module covers Jacobian estimation and task prioritisation with focus on forward and inverse kinematics for planar robot manipulators. Encoding with basis functions for both univariate and multivariate trajectories is detailed, as well as Linear Quadratic Tracking (LQT) and iterative Linear Quadratic Regulator (iLQR) optimisation. You will apply the theoretical principles to practical optimisation problems in robotics scenarios.
Module aims - intentions of the module
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
ILO: Personal and key skills
On successfully completing the module you will be able to...
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| NQF level (module) | 7 |
| Available as distance learning? | No |
| Origin date | 26/09/2024 |
| Last revision date | 26/09/2024 |
