ELEC-E8111 - Autonomous Mobile Robots D, 02.03.2021-07.04.2021
This course space end date is set to 07.04.2021 Search Courses: ELEC-E8111
Topic outline
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1st Zoom Lecture March 2, 2021. Time 14:15 -16:00
Course introduction and arrangements
What do you expect to learn from the course?• Understand the main challenges in mobile robotics
• Understand what are mobile robots made of
– Sub-systems (perception, mobility, navigation, planning, power, …)
– Basic approaches and terminology for each sub-system
– Methodology and algorithms in mobile robotics
• Learn the characteristics in three applications areas
1. Indoor mobile service robot
2. Field Robot: Outdoor semiautonomous vehicle / heavy duty machine UGV
3. Autonomous vehicle, basics
• Not processing, manipulation and graspingTeaching: Lectures, Team work: Design of a case robot system, Indv.: Testing algorithms under ROS on Pioneer robot platformLectures (estimate)
Nro topics
1 Course introduction and arrangements, Introduction to mobile robots
2 Robot control system architecture , Reactive robotics
3 Team work: Design of a case robot system
Individual project: Testing algorithms under ROS/ Pioneer
Introduction to ROS
Locomotion, Kinematics, and Low level Motion Control
4 Sensing and perception, overview.
- Kalman-filter based traditional vehicle localization
5 SLAM = Simultaneous Localization and Mapping, Extended Kalman Filter SLAM (EKF SLAM)
6 Particle filter and Fast SLAM
7 Graph SLAM
8 Satellite Positioning - GNSS technologies
9 Pose Estimation, Inertial Navigation Systems (INS), Inertial Measurement Unit (IMU)
10 Visual localization and object recognition
11 Graph based Path and Motion Planning, Indoor A*. Path and Motion Planning, Outdoor D*
12 Outdoor Path and Motion Planning Motion ControlIntroduction to mobile robots
• What is a service robot? Field Robot?
• Which kinds of service and field robots exist?
– Examples
• Mobile robot subsystems
• Control architectures
• More on Field robotics
• Autonomous vehicles -
Lecture 2. Control architectures. 03/03/2021 Folder
Contents of the lecture
• Motivation and state-of-the-art issues
• Short introduction to control paradigms
• The Hierarchical Paradigm
• Biological foundations for the reactive paradigm
• The Reactive Paradigm
• The Hybrid Paradigm
+Hopefully some good examples and some inspiration
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Course Robot Projects intro
- Team Design Robot project
- Individual Pioneer Lab Robot project
Introduction to ROS
Locomotion, Kinematics and Low Level Motion Control
- Locomotion – Principles and mechanisms to make the robot move.
- Kinematics – How to model motion of (rigid) bodies?
- Differential drive robot, like Pioneer
- Wheeled Mobile Robot
- Motion control – How to make a robot attain a goal or follow a trajectory?
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Sensing and Perception, an overview
• Characterizing sensors
• Classification of sensors
• Sensor types for mobile robots
• Perception in mobile robotics -
Lecture 5, March 16, 2021: Localization, mapping and SLAM = Simultaneous Localization and Mapping, idea; EKF SLAM Folder
Localization, mapping and SLAM = Simultaneous Localization and Mapping, idea
- Main material as subset of ETH slides
- Kalman-filter based traditional vehicle localization in the tractor-trailer -case.Extended Kalman Filter SLAM (EKF SLAM)
Save videos for running. A video haehnel-RawOdometry-anim.avi demonstrates mapping with raw odometry localization - Just mapping, not SLAM. The odometry errors accumulate quickly too large.
A video haehnel-ScanMatching-anim.avi demonstrates first real SLAM, localization on the basis of laser scan matching, only one estimate for pose, Mapping works.
EKF SLAM video by Nebot can be run in the 4_EKF_SLAM 2020.ppt -slide set. -
Lecture 6, March 17, 2021: Particle filter and Fast SLAM Folder
Particle filter
Particle filters are introduced in particle-filters 2020.pdf.
Fast SLAM
Particle distribution for robot pose, for each robot pose particle, separate EKFs for positions of landmarks. Own realization of grid type maps
Fast SLAM: You can run videos in the fastslam 2020.ppt -slide set. pdf available, too.
Some Fast SLAM videos added as separate files. In order to run videos, save them.
bruceton-one-loop.avi ; fastslam-dmb-fastslam.avi ; haehnel-ScanMatchingFastSLAM-Seattle-anim-2.avi
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Lecture 7, March 23, 2021: Graph SLAM Folder
Graph SLAM
– Graph construction and optimization
Zoom-video from remote lecture
– Case forest SLAM
– Advanced topicsSupplementary paper, not required in Exam, Grisetti & al: A Tutorial on Graph-Based SLAM
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Lecture 8, March 24, 2021: Satellite positioning – GNSS technologies Folder
Satellite positioning – GNSS technologies
● GNSS (Global Navigation Satellite System
– Applications
– Constellations: GPS, Galileo, GLONASS, Beidou
● GPS architecture and system
● Multi-sensor data input
● Position determination
● Error sources
● GNSS performance
● GNSS shortcomings
● Practicals for coding (formats, libs, links)Zoom-recording of the remote lecture
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Pose Estimation, Inertial Navigation Systems (INS), Inertial Measurement Unit (IMU)
• Introduction
• Mathematics of Inertial Navigation
• Errors and Aiding in Inertial Navigation
• Example: Simple Odometry Aided AHRS
• Use of cheap MEMS IMUs in robotics
• SummaryA DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs - paper as additional material, not required in exam
Zoom-recording of the remote lecture
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Visual localization and object recognition
- Perception for Mobile Robots
- Machine Vision
- Stereo Vision
- Filtering, Edges, and Point-features
- Corner Detection
- Blob features
- Place Recognition, Line Extraction
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Lecture 11 April 6, 2021: Path and Motion Planning Folder
Path and Motion Planning
Graph based Path and Motion
Planning, Indoor A*.Path and Motion
Planning, Outdoor D*As supplementary material a journal paper, not required in the exam.
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Outdoor Motion Planning and Control
Predictive Modeling– Braking
– Turning
– Vehicle Rollover
– Wheel Slip and Yaw StabilityControl of mobile robot
1. Robot Trajectory Following
2. Perception Based Control
3. Steering Trajectory Generation
4. Optimal and Model Predictive ControlNonlinear Model Predictive Control (NMPC) in Semiautonomous Systems at Aalto
Example: Autonomous driving of Tractor and Implement5. Intelligent Control
Recording from the lecture
A journal paper as a supplementary material, not required in the exam,
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