Mini-project 4 (the final one) released!

The mini-project 4 has been released. Click on this link!

The deadline for report submission: Monday, Dec 02, 4:00pm

Demo and Oral exam: Tuesday, Dec 02, 11:30am either in Mechatronic lab or the FSDE ground floor. 

Module 4-2: Mechatronic systems - Design and Integration

A case study of developing an autonomous car was picked up to illustrate design and integration of a mechatronic system.

A general model, sense-think-act, was introduced. System modelling of differential driven robot was discussed with maneuverability (mobility + steerability). Forward and inverse kinematics of a mobile platform was presented. Fundamentals of open-loop control and feedback control were introduced. 

PID control was briefly discussed. Artificial neural network based control was also introduced. 

Behaviour-based control for mobile platform was addressed.

Theoretical lectures series was closed up with review of the course on the roadmap of Mechatronics Focus. 

Lecture 4-1: Mechatronic Systems - Conceptualized design principles

We studied conceptualized design principles of mechatronic systems. Mechanical, electronics, and computing components are the key components for any mechatronic systems. Modelling, simulation, and real-time testing are the development processes of mechatronic design.

A general process of mechatronic design was introduced. Sequential and parallel design approaches were discussed. General concepts of system modelling and control was briefed. Two methods of system testing and verification, rapid prototyping and hardware-in-the loop were discussed and illustrated.

Mini-project 3 released!

The mini-project 3 has been released. Click on this link!

The deadline for report submission: Monday, November 18, 2:00pm

Demo and Oral exam: Tuesday, November 19, 11:30am at the Mechatronic lab. 

Lab 3-1 Sensor reading, measurement and estimation

Click on Lab assignments to access Lab 3-1.

Note that this lab is to set the foundation for your mini-project 3, so you should follow the guidance and hints to complete all the requirements. 

Module 3-4: Measurement and Estimation

We  studied fundamentals of sensor measurement: measure magnitude of physical quantity and convert it to a signal that can be read and processed by a processor.

We also learnt how to estimate parameters based on collected sensory data through experiments. Approximation is mostly based on probabilistic model. Basic methods of circuit fitting techniques to find a mathematical model and standard deviation used to examine sensory data are briefed.

A case study of infrared ranging sensor is presented to illustrate the concepts of measurement and estimation with sensory data. 

Lecture 3-3: Perception

The lecture about sensing vs. perception was given. 

We emphasized on how a machine can interpret a situation based on raw sensory data and context. The recipe is a combination of raw data + probabilistic model + context.

All the concepts and processes were explained through examples and video demonstrations of our research works (www.morelab.org).