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Course Catalog 2012-2013
IHA-3256 Autonomous Mobile Machines, 7 cr |
Additional information
The course is multidisciplinary and demands analytical problem solving, and programming as well. Priory knowledge about all the fields is not required. However, students must have taken at least one of the prerequisite courses. The term project will be selected based on the background of the group and their interest. The number of students is limited to 14. If the class is full and you are really interested, you can send us email to put you in the reserve list.
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 homework, 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 |
Prerequisites
| Course | Mandatory/Advisable | Description |
| ASE-7516 Dynamic Planning with Incomplete Information | Advisable | |
| IHA-3506 Robotics and Teleoperation | Advisable | |
| MAT-41176 Theory of Automata | Advisable | |
| MAT-45807 Mathematics for Positioning | Advisable | |
| MAT-51706 Bayesian Methods | Advisable | |
| OHJ-2556 Artificial Intelligence | Advisable | |
| SGN-2556 Pattern Recognition | Advisable | |
| SGN-2607 Statistical Signal Processing | Advisable |
Additional information about prerequisites
The course is multidisciplinary. Priory knowledge about all the fields is not required. However, students must have taken at least one of the courses. The term project will be selected based on the background of the group and their interest.
Prerequisite relations (Requires logging in to POP)
Correspondence of content
| Course | Corresponds course | Description |
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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 WhereAmI.pdf SLAMTutorial1.pdf SLAMTutorial2.pdf ch20.pdf ch22.pdf AMM_05.10.11_-_short[1].pptx Autonomous Mobile Machines.pdf Complementary_KF_MotionControl.zip AMM_schedule.pdf