SPACE ROBOTICS AND AUTONOMY - 2018/9

Module code: EEEM029

Module Overview

Expected prior learning: Learning equivalent to a BEng Degree in Electronic Engineering, with appropriate study at FHEQ Level 6. To study this subject successfully requires an interest in mechatronics, artificial intelligence, and robotic space exploration. A good mathematical background and an adequate grasp of control engineering would be very helpful. Programming skill in either Matlab or C language is required to successfully complete the coding assignment.

Module purpose: This module covers the techniques and challenges involved in space robotic missions for on-orbit servicing and planetary exploration. A detailed mathematical analysis of the robotic arms will be provided. Control of robotic arm and traction control of planetary rovers will be taught. Various aspects and techniques of improving autonomy of space robotic systems will be introduced, including sensing, perception, localization, mapping, autonomous planning and navigation.

Module provider

Electrical and Electronic Engineering

Module Leader

GAO Y Prof (Elec Elec En)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

JACs code: H730

Module cap (Maximum number of students): 70

Module Availability

Semester 1

Prerequisites / Co-requisites

None.

Module content





Indicative content includes the following.

INTRODUCTION  (DR. C. SAAJ)

Week 1:  SpaceRobotic Missions(3 hrs):  Introduction to robotics, Space robotic vs terrestrial robotics, Robotic applications for On-orbit servicing and international space station, Robotic planetary exploration missions, Future robotic missions to Mars & Moon, Lab demonstration.

ROBOTIC MANIPULATOR KINEMATICS, DYNAMICS & CONTROL (DR C. SAAJ)

Week 2:  Manipulator Kinematics (3 hrs): Fundamentals of robot manipulator, Introduction to Space Freeflyer, Homogeneous transformation, Denavit-Hartenburg (DH) transformation, Lab demonstration of robot arm.

Week 3:  Manipulator Inverse Kinematics (3 hrs): PUMA 560 configurations, DH matrix for PUMA, Analytic solution to inverse kinematics, Introduction to programming in Matlab.

Week 4:  Manipulator Differential Kinematics and Space Freeflyer Kinematics (3 hours): Manipulator redundancy,Singularity avoidance, Forward & inverse differential kinematics, Introduction to Space Freeflyer Kinematics.

Week 5:  Manipulator Dynamics (3 hrs): Mass distribution, Inertia tensor, Parallel axis theorem, Holonomic & non-holonomic systems, Introduction to Lagrange-Euler method and Newton-Euler method.

Week 6:  Manipulator Motion Control (3 hrs): Robot control system, DC motor control of single joint, Proportional-Derivative control, Computed torque control.

PLANETARY ROVER NAVIGATION & CONTROL  (PROF. Y. GAO, DR. C. SAAJ)

Week 7:  Rover Traction Control (3 hrs): Planetary rover systems, Introduction to rover chassis, Ackermann steering, Bekker theory.

Week 8:  RoverNavigation System (3 hrs): introduction to rover navigation problem and major system architectural designs (hieratical, reactive and hybrid). Major navigation functions, localization challenges & strategies, map making and representation, metric path planning, topological path planning, planning algorithms such as A*/D*.

Week 9:  RoverSensing & Perception (3 hrs): Classification of sensors (e.g. proprioceptive vs. exteroceptive; and passive vs. active), sensor properties, motor sensors, heading sensors, ranging sensors, vision sensors, stereovision, vision processing techniques.

Week 10:  Rover Navigation Design Problems: (3 hrs): In-class quiz and work on design problems.





 

Assessment pattern

Assessment type Unit of assessment Weighting
Examination 2 HOUR CLOSED-BOOK WRITTEN EXAM 80
Coursework TAS-ORIENTED PROGRAMMING ASSIGNMENT 20

Alternative Assessment

Not applicable: students failing a unit of assessment resit the assessment in its original format.

Assessment Strategy





The assessment strategy for this module is designed to provide students with the opportunity to demonstrate the learning outcomes. The written examination will assess their analytical and problem solving skills, background understanding of manipulator control, rover locomotion and navigation. The coursework coding assignment is designed to develop professional programming skills to model an industrial robotic arm and the in-class test will assess their subject knowledge on autonomous rover navigation.

 

Thus, the summative assessment for this module consists of:

·         2-hour, closed-book written examination.

·         Single unit of coursework on Matlab/C coding of robot arm kinematics due in Week 10.

 

Any deadline given here is indicative. For confirmation of exact dates and times, please check the Departmental assessment calendar issued to you.

 

Formative assessment and feedback

For this module, students will receive formative assessment/feedback in the following ways:

·         After formative in-class quiz based on problem solving approach during the lecture in Week 10. The formative test will be self-marked by students in Week 10

·         During lectures, by question and answer sessions

·         During tutorials/Problem based learning sessions, enquiry based learning and research led teaching.

·         Oral and written feedback following coding viva.





 

Module aims

  • This module aims to introduce the student to the key principles and techniques of space robotics and autonomy.

Learning outcomes

Attributes Developed
1 Describe of the principles and techniques involved in the mechanical and electrical design of space robotic systems. KC
2 Analyse the kinematics and dynamics of robot manipulators and design control systems for manipulators. KCT
3 Describe basic principles and techniques in rover navigation. KCPT
4 Evaluate the operation of locomotion system for wheeled planetary rovers. KPT
5 Demonstrate an awareness of the challenges involved in space robotic missions. KP

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Overall student workload

Independent Study Hours: 117

Lecture Hours: 33

Methods of Teaching / Learning





The learning and teaching strategy is designed to achieve the following aims.


Learning through regular lectures from Week 1 to 10. These lectures will include the problem solving sessions, enquiry based learning, research led teaching and in-class discussions.
Prepare for summative assessment through intensive in-class revision in Week 11.
Lecture notes will be provided and students are expected to do independent learning in addition to attending lectures and tutorials.


Learning and teaching methods include the following.


3 hours lecture per week x 10 weeks which includes class discussion and problem solving sessions.
3 hours in-class revision in Week 11.


 





 

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

Reading list for SPACE ROBOTICS AND AUTONOMY : http://aspire.surrey.ac.uk/modules/eeem029

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Computer Vision, Robotics and Machine Learning (EuroMasters) MSc 1 Compulsory A weighted aggregate mark of 50% is required to pass the module
Computer Vision, Robotics and Machine Learning (EuroMasters) MSc 1 Compulsory Each unit of assessment must be passed at 50% to pass the module
Space Engineering MSc 1 Optional A weighted aggregate mark of 50% is required to pass the module
Electrical and Electronic Engineering MEng 1 Optional A weighted aggregate mark of 50% is required to pass the module
Electronic Engineering MEng 1 Optional A weighted aggregate mark of 50% is required to pass the module
Electronic Engineering (EuroMasters) MSc 1 Optional A weighted aggregate mark of 50% is required to pass the module
Electronic Engineering MSc 1 Optional A weighted aggregate mark of 50% is required to pass the module
Computer Vision, Robotics and Machine Learning MSc 1 Compulsory A weighted aggregate mark of 50% is required to pass the module
Space Engineering (EuroMasters) MSc 1 Optional A weighted aggregate mark of 50% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2018/9 academic year.