EE-565
From CYPHYNETS
| EE-565/CS-5313: Mobile Robotics |
|---|
Instructors
Assistant Professor of Electrical Engineering
Email: ahmad.kamal [at] lums.edu.pk, abubakr [at] lums.edu.pk
Office: Room 9-313, Left Wing, 3rd Floor, SSE Bldg
Office Hours: Tuesday[1400-1500] Thursday[1400-1500]
Teaching assistant.
Course Details
Year: 2018-19
Semester: Spring
Category: Grad
Credits: 3
Elective course for electrical engineering, computer engineering and computer science majors
Course Description
This course is designed to provide students a hands-on experience on real aerial and ground mobile robots. It provides an overview of problems / approaches in mobile robotics. Most of the algorithms described in this course are probabilistic in nature, dealing with noisy data. The students shall be given an opportunity to implement state of the art probabilistic algorithms for mobile robot state estimation, with a strong focus on vision as the main sensor.
This course is NOT about
- Mechatronics or robot building.
Learning Outcomes
The students should be able to:
- Understand basic wheel robot kinematics, common mobile robot sensors and actuators knowledge.
- Understand and able to apply various robot motion and sensor models used for recursive state estimation techniques.
- Demonstrate Inertial/visual odometeric techniques for mobile robots pose calculations.
- Use and apply any one of the Simultaneous Localization and Mapping (SLAM) technique.
- Understand and apply path planning and navigation algorithms.
Pre-requisites
Courses. CS310 OR EE361 OR By permission of instructor.
Topics/Skills. Programming proficiency in C/C++; linear algebra and probability
Text books & Supplementary Readings
The course will be taught from a combination of the following textbooks.
- A. Probabilistic Robotics by Sebastian Thurn.
- C. Introduction to Autonomous Mobile Robots by Roland Siegwart
- D. Autonomous Land Vehicles by Karsten Berns
- Lecture notes & Research papers.
Grading Scheme
- Final Project : 20%
- Midterm Examination: 25% + 15%
- Lab Tasks: 40%
Course Delivery Method
Lectures. Tues, Thurs: 0800-0850. Lab. Fri: 0800-1050.
Schedule
| WEEK | TOPICS | READINGS/REFERENCES |
|---|---|---|
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Week 1
|
Lecture:
Tutorial: Introduction to ROS | |
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Week 2
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Lecture:
Lab Task: ROS Interface with simulation environment |
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Week 3
|
Lecture:
Lab Task: ROS Interface with low level control |
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Week 4
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Lecture:
Lab Task: IRobot setup with ROS and implement odometeric motion model |
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Week 5
|
Lecture:
Lab Task: AR Drone setup with ROS and sensor data fusion using AR Drone’s accelerometer and gyroscope | |
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Week 6
|
Lecture:
Mid-Term Examination 1 | |
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Week 7
|
Lecture:
Lab Task: Inertial Odometry using AR Drone’s IMU and calculating measurement’s covariance |
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Week 8
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Lecture:
Lab Task: Calibrate AR Drone’s camera and perform online optical flow. |
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Week 9
|
Lecture:
Lab Task: Using AR Drone’s camera, perform visual odometry by SFM algorithm |
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Week 10
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Lecture:
Mid-Term Examination 2 |
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Week 11
|
Lecture:
Lab Task: Creating grid map using IRobot-Create equipped with laser scanner. | |
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Week 12
|
Lecture:
Lab Task: Create a 3D grid map using IRobot equipped with Microsoft Kinect |
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Week 13
|
Lecture:
Lab Task: Setup and perform navigation using ROS navigation stack and stored map | |
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Week 14
|
Lecture:
Lab Task: Hands-on introduction to sampling based planners via Open Motion Planning Library (OMPL) | |
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Week 15
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