ENMT482-18S2 (C) Semester Two 2018

Robotics

15 points

Details:
Start Date: Monday, 16 July 2018
End Date: Sunday, 18 November 2018
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 29 July 2018
  • Without academic penalty (including no fee refund): Sunday, 14 October 2018

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.

Prerequisites

Restrictions

ENEL442, ENME413

Course Coordinator

Michael Hayes

Lecturer

Chris Pretty

Assessment

Assessment Due Date Percentage 
Assignment one 25%
Assignment two 12.5%
Assignment three 12.5%
Exam 50%

Indicative Fees

Domestic fee $1,059.00

International fee $4,875.00

* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.

For further information see Mechanical Engineering .

All ENMT482 Occurrences

  • ENMT482-18S2 (C) Semester Two 2018
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