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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.
Washington Accord (V4) Summary of Graduate Attributes attained in this course: WA1 – Engineering Knowledge WA2 – Problem Analysis WA3 – Design/Development of Solutions WA5 – Tool UsageCourse topics with Learning Outcomes (and Washington Accord (WA) and UC Graduate Attributes) identified.1. Autonomous robots: Introduction to sensor fusion; 1-D Bayes filters (Kalman, histogram, and particle); Multivariate Bayes filters; Motion models; Mapping/Localisation/SLAM; Navigation and path planning 1.1. Implement Bayes filters (such as a particle filter and an extended Kalman filter) for robot sensor fusion (WA1, WA2, WA3, WA4, WA5, WA8, WA9) 1.2. Understand navigation and path planning algorithms to control an autonomous robot (WA1, WA2) 1.3. Implement a motion model for a wheeled robot (WA1, WA2,WA3, WA5, WA8, WA9)) 1.4. Understand the principles of simultaneous localisation and mapping (WA1, WA4, WA5)2. Articulated manipulators: Spatial descriptions and transformations; Forward kinematics; Inverse kinematics; Jacobians; Motion planning 2.1. 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 (WA3) 2.2. 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 (WA2) 2.3. Apply simple linear interpolative path planning techniques to control end-effector motion for an articulated manipulator (WA2) 2.4. Construct the Jacobian matrix for an articulated manipulator and use it to calculate static forces and torques and derive dynamic equations for each link (WA2)
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.
Employable, innovative and enterprising
Students will develop key skills and attributes sought by employers that can be used in a range of applications.
ENME403
Students must attend one activity from each section.
Michael Hayes
Chris Pretty
Sebastian Thrun, Wolfram Burgard and Dieter Fox; Probabilistic Robots ; MIT Press, 2005.
For detailed course, policy, regulatory and integrity information, please refer to the UC web site, or see relevant Course or Department LEARN pages, (which are available to enrolled students).
Artificial Intelligence Tools:The use of Artificial Intelligence (AI) tools for each of the assessments in ENEL290 is summarised in the Table below. No AI use is allowed in the tests and exam because these are closed-book invigilated assessments. Students are always responsible for the accuracy of the submitted works, regardless of which tools are used.Assessment Item Permitted use of AI:Laboratories: Generative AI tools are not restricted for this assessment. Laboratory Report: Generative AI Tools Are Permitted for Certain Parts of This Assessment Assignment: Generative AI Tools Are Permitted for Certain Parts of This Assessment Tests: Generative AI tools cannot be used for this assessment. Exam: Generative AI tools cannot be used for this assessment.Generative AI Tools Are Permitted for Certain Parts of This AssessmentIn these assessments (Lab Report, Assignment), you are permitted to use generative artificial intelligence (AI) for the purpose of proof reading and editing the document, and for gathering and summarising knowledge. No other use of generative AI is permitted. To assist with maintaining academic integrity, you must appropriately acknowledge any use of generative AI in your work. Please include a Statement of AI use (if no AI tool has been used, then this must also be stated) and a listing of all prompts provided to the AI tool, clearly indicating which AI tools were used and how they contributed to your assessment.
The examiners will award a failing grade to students who score less than 40% for the Exam.This note is put in place to ensure that each student has adequately shown to the examiners they have gained some mastery of the topic.Scaling of marksIn order to maintain consistency across courses and fairness for students, scaling of raw marks occurs. In the Faculty of Engineering, target course GPAs are calculated based on the performance of the cohort of students in their courses in the previous year. Scaling of the raw total course marks is normally performed so that when converted to grades (using UC Grade Scale) the outgoing GPA is in line with the target GPA for a course. Scaling up or down can occur.The Grading Scale for the University:https://www.canterbury.ac.nz/study/study-support-info/study-related-topics/grading-scale
Lateness PenaltiesFor the Assignment, a lateness penalty of 10% (in absolute terms) per day or part day late will be deducted from the original mark. For example, an assignment with a nominal mark of 83% submitted 0-24 hours late will receive a mark of 73% and submitted 24-48 hours late will receive 63%.
Contact HoursLectures: 36 hoursTutorials: 0 hoursWorkshops: 0 hoursLaboratories: 2 hours Independent studyReview of lectures: 28 hoursTest and exam preparation: 16 hoursAssignments: 68 hoursTotal 150 hours
Domestic fee $1,344.00
International fee $6,488.00
* 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 .