ENMT482-20S2 (C) Semester Two 2020

Robotics

15 points

Details:
Start Date: Monday, 13 July 2020
End Date: Sunday, 8 November 2020
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Friday, 24 July 2020
  • Without academic penalty (including no fee refund): Friday, 25 September 2020

Description

This course is structured as two parts: (1) articulated robot manipulators and (2) autonomous mobile robotics. Articulated manipulators form an important class of robots that are commonly used in industrial situations. The purpose of this part of the course is to introduce students to fundamental concepts of geometry, kinematics, dynamics, and control of robotic systems allowing students to model and analyse a robot manipulator. The autonomous mobile robotics part of the course is an introduction to the probablistic robotics techniques that underpin self-driving cars and other autonomous robots. This course is project-based and students will be given the opportunity to apply the material in both simulation and with real industrial and research robots through project work.

Learning Outcomes

Develop and apply forward kinematics to obtain the end-effector
position and orientation in the base coordinate frame as a function
of the joint parameters for an articulated manipulator

Apply inverse kinematics to calculate all possible sets of
joint parameters that result in a given end-effector position and
orientation relative to the base coordinate frame

Construct the Jacobian matrix for an articulated manipulator and
use it to calculate static forces and torques and derive dynamic
equations for each link
   
Apply simple linear interpolative path planning techniques to
control end-effector motion for an articulated manipulator

Understand the principles of Bayes filters for probabilistic robotics and their application to sensor fusion, mapping, localisation, and simultaneous localisation and mapping

Implement a particle filter and a Kalman filter for robot sensor fusion

Apply navigation and path planning algorithms to control a robot using the robot operating system (ROS)

University Graduate Attributes

This course will provide students with an opportunity to develop the Graduate Attributes specified below:

Critically competent in a core academic discipline of their award

Students know and can critically evaluate and, where applicable, apply this knowledge to topics/issues within their majoring subject.

Pre-requisites

Timetable 2020

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Wednesday 10:00 - 11:00 A3 Lecture Theatre (15/7-19/8)
Rehua 009 (9/9-14/10)
13 Jul - 23 Aug
7 Sep - 18 Oct
Lecture B
Activity Day Time Location Weeks
01 Monday 16:00 - 17:00 A3 Lecture Theatre
13 Jul - 23 Aug
7 Sep - 18 Oct
Lecture C
Activity Day Time Location Weeks
01 Thursday 09:00 - 10:00 E5 Lecture Theatre
13 Jul - 23 Aug
7 Sep - 18 Oct
Lab A
Activity Day Time Location Weeks
01 Wednesday 15:00 - 16:30 Elec 109 Automation Lab 13 Jul - 23 Aug
02 Tuesday 09:00 - 10:30 Elec 109 Automation Lab 13 Jul - 23 Aug
Lab B
Activity Day Time Location Weeks
01 Tuesday 09:00 - 11:00 Mech 113 Robotics Lab (8/9-13/10)
Mech 214 Mechatronics Lab (8/9-13/10)
7 Sep - 18 Oct

Course Coordinator

Michael Hayes

Lecturer

Chris Pretty

Assessment

Assessment Due Date Percentage 
Assignment two 25%
Assignment one 25%
Exam 50%

Indicative Fees

Domestic fee $1,102.00

International fee $5,500.00

* Fees include New Zealand GST and do not include any programme level discount or additional course related expenses.

For further information see Mechanical Engineering.

All ENMT482 Occurrences

  • ENMT482-20S2 (C) Semester Two 2020