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Course Catalog 2011-2012
IHA-3256 Autonomous Mobile Machines, 7 cr |
Additional information
Suitable for postgraduate studies
Person responsible
Mika Hyvönen, Reza Ghabcheloo
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
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Requirements
Completion of homeworks, presentations, and final paper
Completion parts must belong to the same implementation
Learning outcomes
Students learn about challenges in making autonomous mobile machines (robotic mobile manipulators): sensing, planning, control and manipulation. Course is composed of two parts: teaching and student work. Teaching includes introduction to several subsystems of a robotic mobile manipulator, problems involved, and solutions. It continues by introducing the instrumented mobile machine available in IHA, and its simulator. Components of such intelligent systems are also briefly presented: sensors (Inertial sensors, wheel odometry, GPS, range sensors, cameras), navigation and perception (sensor fusion, Kalman filter, SLAM, mapping, and world modelling), vision and recognition, Kinematic modelling, motion planning and control, visual servoing, among others. Teaching will include guest speakers from Aalto University. However, most of the course will be based on term project. Students will be divided into groups of two, and each group will concentrate on certain problem. Subjects are given. Students with special interest are encouraged to work out their ideas. Students will make presentations and discuss their problems, and report the results in a paper format at the end of the course. Development of functional computer codes is the goal of this course. Course demands analytical problem solving, and programming likewise. Real data and simulator will be available, and promising results will be tested on the real machine.
Evaluation criteria for the course
20% presentations + 80 % term project paper and project delivery
Assessment scale:
Numerical evaluation scale (1-5) will be used on the course
Partial passing:
Study material
| Type | Name | Author | ISBN | URL | Edition, availability, ... | Examination material | Language |
| Book | Computational Principles of Mobile Robotics | Gregory Dudek, Michael Jenkin | English | ||||
| Book | Introduction to Autonomous Mobile Robots | Roland Siegwart, Illah Nourbakhsh, Davide Scaramuzza | English | ||||
| Book | Probabilistic Robotics | Sebastian Thrun, Wolfram Burgard, Dieter Fox | 2005 | English | |||
| Book | Robotics: Modelling, Planning and Control | Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani, Giuseppe Oriolo | Springer, 2010 | English | |||
| Book | Springer Handbook of Robotics | Bruno Siciliano, Oussama Khatib (Eds.) | 2008 | English | |||
| Book | Where am I? Sensors and Methods for Mobile Robot Positioning | J. Borenstein , H. R. Everett , and L. Feng | 1996 | English |
Additional information about prerequisites
The course is multidisciplinary. Priory knowledge about all the fields is not required. However, students must have taken at least two of the following courses. The term project will be selected based on the background of the group and their interest.
Mathematics
* MAT-41126 Optimization Theory 1, 7 cr
* MAT-41176 Theory of Automata, 5 cr
* MAT-45807 Mathematics for Positioning, 4 cr
* MAT-51706 Bayesian Methods, 6 cr
Computer systems
* TKT-2546 Methods for Positioning, 3 cr
* TKT-2556 Basics of Inertial Navigation, 5 cr
Signal processing
* SGN-2556 Pattern Recognition, 5 cr
* SGN-2607 Statistical Signal Processing, 6 cr
* SGN-5456 3D Media Technology, 4 cr
Software
* OHJ-2556 Artificial Intelligence, 6cr
Automation Science and Engineering
* MIT-3236 Measurement Based on Digital Image 2
* ASE-7516 Dynamic Planning with Incomplete Information
* ASE-1256 Introduction to Control and Automation 6 cr
Intelligent Hydraulics and Automation
* IHA-2327 Simulation of Mobile Machines, 5 cr
* IHA-3206 Servo Systems, 5 cr
* IHA-3506 Robotics and Teleoperation, 7 cr
Prerequisite relations (Requires logging in to POP)
Correspondence of content
There is no equivalence with any other courses
More precise information per implementation
| Implementation | Description | Methods of instruction | Implementation |
| Students learn about challenges in making autonomous mobile machines. Course is composed of two parts: teaching and student work. Teaching includes introduction to several subsystems of a robotic mobile manipulator, problems involved, and solutions. Components of such intelligent systems are also briefly presented: sensors, navigation and perception, vision and recognition, kinematic modelling, motion planning and control, visual servoing, among others. Most of the course will be based on term project. Students will be divided into groups of two, and each group will concentrate on certain problem. Subjects are given. Students with special interest are encouraged to work out their ideas. Students will make presentations and discuss their problems, and report the results in a paper format at the end of the course. Development of functional computer codes is the goal of this course. Course demands analytical problem solving, and programming likewise. Real data and simulator will be available, and promising results will be tested on the real machine. |
Documents
Localization_and_slam_JariSaarinen.pdf CompFilter.zip WhereAmI.pdf SLAMTutorial1.pdf SLAMTutorial2.pdf AMM_schedule.xlsx Autonomous Mobile Machines_introduction.pdf ch20.pdf ch22.pdf AMM_05.10.11_-_short[1].pptx