UPenn - Robotics: Capstone
- Offered byCoursera
Robotics: Capstone at Coursera Overview
Duration | 26 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Robotics: Capstone at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 6 of 6 in the Robotics Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Approx. 26 hours to complete
- English Subtitles: English
Robotics: Capstone at Coursera Course details
- In our 6 week Robotics Capstone, we will give you a chance to implement a solution for a real world problem based on the content you learnt from the courses in your robotics specialization. It will also give you a chance to use mathematical and programming methods that researchers use in robotics labs.
- You will choose from two tracks - In the simulation track, you will use Matlab to simulate a mobile inverted pendulum or MIP. The material required for this capstone track is based on courses in mobility, aerial robotics, and estimation. In the hardware track you will need to purchase and assemble a rover kit, a raspberry pi, a pi camera, and IMU to allow your rover to navigate autonomously through your own environment
- Hands-on programming experience will demonstrate that you have acquired the foundations of robot movement, planning, and perception, and that you are able to translate them to a variety of practical applications in real world problems. Completion of the capstone will better prepare you to enter the field of Robotics as well as an expansive and growing number of other career paths where robots are changing the landscape of nearly every industry.
- Please refer to the syllabus below for a week by week breakdown of each track.
- Week 1
- Introduction
- MIP Track: Using MATLAB for Dynamic Simulations
- AR Track: Dijkstra's and Purchasing the Kit
- Quiz: A1.2 Integrating an ODE with MATLAB
- Programming Assignment: B1.3 Dijkstra's Algorithm in Python
- Week 2
- MIP Track: PD Control for Second-Order Systems
- AR Track: Assembling the Rover
- Quiz: A2.2 PD Tracking
- Quiz: B2.10 Demonstrating your Completed Rover
- Week 3
- MIP Track: Using an EKF to get scalar orientation from an IMU
- AR Track: Calibration
- Quiz: A3.2 EKF for Scalar Attitude Estimation
- Quiz: B3.8 Calibration
- Week 4
- MIP Track: Modeling a Mobile Inverted Pendulum (MIP)
- AR Track: Designing a Controller for the Rover
- Quiz: A4.2 Dynamical simulation of a MIP
- Peer Graded Assignment: B4.2 Programming a Tag Following Algorithm
- Week 5
- MIP Track: Local linearization of a MIP and linearized control
- AR Track: An Extended Kalman Filter for State Estimation
- Quiz: A5.2 Balancing Control of a MIP
- Peer Graded Assignment: B5.2 An Extended Kalman Filter for State Estimation
- Week 6
- MIP Track: Feedback motion planning for the MIP
- AR Track: Integration
- Quiz: A6.2 Noise-Robust Control and Planning for the MIP
- Peer Graded Assignment: B6.2 Completing your Autonomous Rover
Robotics: Capstone at Coursera Curriculum
Week 1
Capstone Introduction and Choosing the Capstone Project
Introduction to the Mobile Inverted Pendulum (MIP) Track
Introduction to the Autonomous Rover (AR) Track
A1.1 Using MATLAB for Dynamic Simulations
(Review) Dijkstra's Algorithm
B1.1 Purchasing the Robot Kit
B1.2 The Rover Simulator
A1.2 Integrating an ODE with MATLAB
Week 2: Lesson Choices
(Review) Newton's Laws; Damped and Undamped
(Review) PD Control for a Point Particle in Space
A2.1 PD Control for Second-Order Systems
(Review) Infinitesimal Kinematics; RR Arm
B2.1 Building the Autonomous Rover (AR)
B2.6 Connecting to the Pi
B2.2 Soldering tips
B2.3 Soldering the Motor Hat and IMU
B2.4 Flashing your Raspberry Pi SD Card
B2.5 Assembling the Robot
B2.7 Expanding the SD Card Partition
B2.8 Remote Access to the Pi
B2.9 Controlling the Rover
A2.2 PD Tracking
Week 3: Lesson Choices
(Review) Extended Kalman Filter
A3.1 Using an EKF to get Scalar Orientation from an IMU
B3.1 Calibration
B3.2 Camera Calibration
(Review) Rotations and Translations
B3.4 Camera to body calibration
B3.5 Introduction to Apriltags
B3.3 Motor Calibration
B3.6 Printing your own AprilTags
B3.7 Optional: IMU Accelerometer Calibration
A3.2 EKF for Scalar Attitude Estimation
B3.8 Calibration
Week 4: Lesson Choices
(Review) Lagrangian Dynamics
A4.1 Modeling a Mobile Inverted Pendulum (MIP)
(Review) 2-D Quadrotor Control
B4.1 Designing a Controller for the Rover
A4.2 Dynamical simulation of a MIP
Week 5: Lesson Choices
(Review) Linearization
A5.1 Local Linearization of a MIP and Linearized Control
(Review) Kalman Filter Model
(Review) Extended Kalman Filter Model
B5.1 An Extended Kalman Filter for the Rover
A5.2 Balancing Control of a MIP
Week 6: Lesson Choices
(Review) Motion Planning for Quadrotors
A6.1 Feedback Motion Planning for the MIP
B6.1 Integration
A6.2 Noise-Robust Control and Planning for the MIP
Robotics: Capstone at Coursera Admission Process
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