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2019/20 Undergraduate Module Catalogue

MECH3460 Robotics and Machine Intelligence

20 creditsClass Size: 175

Module manager: Dr Abbas Dehghani
Email: a.dehghani@leeds.ac.uk

Taught: Semesters 1 & 2 View Timetable

Year running 2019/20

Pre-requisite qualifications

Students may select this module if either MECH 2650 Mechatronics and Measurement Systems OR MECH 2660 Mechatronics and Robotics Systems have been studied.

Pre-requisites

MECH2620Vibration and Control
MECH2650Mechatronics and Measurement Systems
MECH2660Mechatronics and Robotics Systems

Module replaces

MECH 3160, MECH 3115

This module is not approved as a discovery module

Objectives

Learning outcomes
At the end of the module students should be able to:
1. describe the different mechanical configurations for robot manipulators
2. choose appropriate robot components for a given application (sensors, actuators, powering method, configurations, controllers, etc)
3. undertake kinematic analysis of robot manipulators
4. analyse the dynamics of planar manipulators
5. understand the different methods of providing robot mobility (wheels, legs, tracks, climbing, etc)
6. understand the methods for localisation
7. appreciate the methods for navigation
8. perform basic conceptual designs of mobile robot systems
9. understand basic concepts in machine vision and decision making
10. describe the social and economic impact of industrial and service robotics
11. appreciate the current state and potential for robotics in new application areas (eg medical).


Upon successful completion of this module the following UK-SPEC learning outcome descriptors are satisfied:

A comprehensive knowledge and understanding of the scientific principles and methodology necessary to underpin their education in their engineering discipline, and an understanding and know-how of the scientific principles of related disciplines, to enable appreciation of the scientific and engineering context, and to support their understanding of relevant historical, current and future developments and technologies (SM1m)
Knowledge and understanding of mathematical and statistical methods necessary to underpin their education in their engineering discipline and to enable them to apply a range of mathematical and statistical methods, tools and notations proficiently and critically in the analysis and solution of engineering problems (SM2m)
Ability to apply and integrate knowledge and understanding of other engineering disciplines to support study of study of medical engineering and the ability to evaluate them critically and to apply them effectively (SM3m)
Awareness of developing technologies related to mechanical engineering (SM4m)
A comprehensive knowledge and understanding of mathematical and computational models relevant to the engineering discipline, and an appreciation of their limitations (SM5m)
Understanding of concepts from a range of areas, including some outside engineering, and the ability to evaluate them critically and to apply them effectively in engineering projects (SM6m)
Understanding of engineering principles and the ability to apply them to undertake critical analysis of key engineering processes (EA1m)
Ability to identify, classify and describe the performance of systems and components through the use of analytical methods and modelling techniques (EA2)
Ability to apply quantitative and computational methods, using alternative approaches and understanding their limitations, in order to solve engineering problems and implement appropriate action (EA3m)
Understanding of, and the ability to apply, an integrated or systems approach to solving complex engineering problems (EA4m)
Demonstrate the ability to generate an innovative design for products, systems, components or processes to fulfil new needs (D8m)
Knowledge and understanding of the commercial, economic and social context of engineering processes (EL2)
Knowledge of characteristics of particular equipment, processes or products, with extensive knowledge and understanding of a wide range of engineering materials and components (P2m)
Ability to apply relevant practical and laboratory skills (P3)
Ability to work with technical uncertainty (P8)
Ability to apply engineering techniques taking account of a range of commercial and industrial constraints (P10m)
Apply their skills in problem solving, communication, information retrieval, working with others, and the effective use of general IT facilities (G1)

Skills outcomes
Select robot components and perform kinematic analysis of robot movement, selection of robot sensors, dynamical control of robots, robot navigation.

Design of control systems using soft computing: artificial neural networks, fuzzy logic and hybrid systems.


Syllabus

PART I: INDUSTRIAL ROBOT MANIPULATORS
- Introduction: Robotics: a definition, History, The parts of a robot manipulator system, Robot component modularity, Robot manipulator classification, industrial, economic and social impact of robots
- Kinematics: Definitions, Transformations, Properties of transformation matrices, Forward kinematics, Matlab and the robotics toolbox, Inverse kinematics
- Design: Actuators, Internal state sensors, External state sensors, End effectors, Mechanical arrangement and specification: PUMA 500 series
- Dynamics and control: Inverse Dynamics, Forward Dynamics, Control
- Vision systems: Introduction, vision hardware, image processing, object recognition
- User Interfaces: Input and output devices; force feedback; virtual reality; natural interfaces.

PART II: MOBILE ROBOTICS
- Mobile robots: types of mobility; wheeled, tracked, legged etc, climbing robots
- Localisation; sensors for localisation, odometry, triangulation, trilateration
- Navigation; biological strategies, behaviours, motion planning, path planning
- Autonomous robots; Classical AI, behaviour based, learning
- Service robots; Introduction, application sectors, examples.

Part III: MACHINE INTELLIGENCE
- The Human Brain and How It Functions
- Machine Intelligence
- Comparison between human and machine intelligence
- Artificial neural network and intelligent control
> Linear Separable Patterns and Linear Classification
> Single Layer Perceptron
> Multi-layer Perceptron
> Back Propagation Algorithm
> Radial Basis Function Network
> Kohonen Self-Organisation Network
> Hopfield Network
> Neural Networks in Robotics and Control Applications.

- Fuzzy Logic and intelligent control
> Fuzzy Sets: Definitions and Relations
> Fuzzy Logic and Fuzzy Inference
> Fuzzy Logic Control
> Fuzzy Logic Control Design
> Fuzzy Logic Control Case Examples
> Mamdani and Sugeno Type Fuzzy Logic Systems
> Fuzzy Logic in Robotics and Control Applications.

- Hybrid Systems
> Neuro-Fuzzy control systems

- Biologically inspired robots
> Case studies of humanoid robots
> Case studies of robots inspired from animals.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Class tests, exams and assessment11.001.00
Class tests, exams and assessment12.002.00
Lecture441.0044.00
Practical22.004.00
Private study hours149.00
Total Contact hours51.00
Total hours (100hr per 10 credits)200.00

Private study

Problem sheets. preparation for assignments, reading and revision.

Opportunities for Formative Feedback

Formally through assignments, presentations and less formally through problem sheets.

Methods of assessment


Coursework
Assessment typeNotes% of formal assessment
AssignmentAssignment 130.00
AssignmentAssignment 210.00
ReportLab report20.00
Total percentage (Assessment Coursework)60.00

1) Coursework marks carried forward and 40% resit exam OR 2) 100% resit exam


Exams
Exam typeExam duration% of formal assessment
Standard exam (closed essays, MCQs etc)1 hr 30 mins40.00
Total percentage (Assessment Exams)40.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 14/05/2019

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