Online Course – Certified Professional Internship in Robotics from the University of Pennsylvania

Robotics: Aerial Robotics

• Course 1 • 18 hours • 4.5 (3,070 ratings)

Course Details

What will you learn?

• How do we create small, flexible air vehicles that can operate autonomously in busy environments, both inside buildings and outdoors?
• You will learn about the mechanics of flight and the design of quadcopter flying robots.
• You will be able to develop dynamic models, derive variables, and synthesize action plans in 3D environments.
• You will be exposed to the challenges of using noisy sensors for positioning and maneuvering in complex 3D environments.
• At the end, you will see real-world examples of possible applications and challenges in the rapidly evolving drone industry.

Mathematical requirements

• Student expectations for this course include familiarity with linear algebra, differential calculus with one variable, and differential equations.

Programming requirements

• It is recommended to have programming experience with MATLAB or Octave (we will use MATLAB in this course).
• A 64-bit computer is required.

Skills you will develop

• Category: Traffic Planning
• Category: Robotics
• Category: Drone
• Category: MATLAB

Robotics: Computational Motion Planning

• Course 2 • 11 hours • 4.3 (1,034 ratings)

Course Details

What will you learn?

• Robotic systems typically include three components: a mechanism that can exert forces and torques on the environment, a sensing system to sense the world, and a decision-making and behavior control system for the robot.
• In this course, we will examine the problem of how a robot decides what to do to achieve its goals.
• This problem is sometimes called traffic planning and is formulated in different ways to model different situations.
• You will learn several common approaches to solving this problem, including graph-based methods, random planners, and artificial potential fields.
• During the course, we will talk about the aspects of the problem that make planning a challenge.

Skills you will develop

• Category: Python Programming
• Category: Robotics
• Category: Raspberry Pi
• Category: MATLAB

Robotics: Mobility

• Course 3 • 19 hours • 3.9 (603 ratings)

Course Details

What will you learn?

• How can robots use their motors and sensors to navigate in an unstructured environment?
• Understand how to design robot bodies and behaviors to utilize physical forms to exert physical forces that ensure reliable mobility in a complex and dynamic world.
• We open an approach to assembling simple dynamic instances that perform partial automation to create complex sensor-motor programs.
• Specific topics that will be covered include: mobility in animals and robots, kinematics and dynamics of legged machines, and designing dynamic behavior using energy landscapes.

Skills you will develop

• Category: Particulate Filter
• Category: Evaluation
• Category: Mapping

Robotics: Sensing

• Course 4 • 33 hours • 4.3 (653 ratings)

Course Details

What will you learn?

• How can robots sense the world and their movements so that they can perform navigation and manipulation tasks?
• In this module, we will explore how images and videos captured by cameras mounted on robots are converted into representations such as features and optical flow.
• These 2D representations allow us to extract 3D information about the camera position and the direction of the robot’s movement.
• Understand how object perception is facilitated by calculating the 3D alignment of objects and navigation can be performed using visual odometry and symbol-based detection.

Skills you will develop

• Category: Computer Vision
• Category: Evaluation
• Category: Random Sampling Analysis (RANSAC)
• Category: Geometry

Robotics: Assessment and Learning

• Course 5 • 15 hours • 4.3 (504 ratings)

Course Details

What will you learn?

• How can robots determine their state and the properties of the environment around them based on noisy sensor measurements?
• In this module, you will learn how to make robots incorporate uncertainty into evaluation and learn from a dynamic and changing world.
• Specific topics that will be covered include probabilistic generative models, Bayesian filtering for location detection, and mapping.

Skills you will develop

• Category: Traffic Planning
• Category: Planning and Automation
• Category: A* algorithm
• Category: MATLAB

Robotics: Final Project

• Course 6 • 26 hours • 4.6 (114 ratings)

Course Details

What will you learn?

• In our robotics final project, we will give you the opportunity to implement a solution to a practical problem based on the content you learned in your robotics specialization courses.
• It will also give you the opportunity to use mathematical and programming methods that researchers use in robotics labs.
• Choose from two routes:
• In the simulation track, you will use MATLAB to simulate an inverted mobile pendulum. The required material for this final track is based on courses in mobility, aerial robotics, and evaluation.
• In the hardware track, you will need to purchase and assemble a robot kit, Raspberry Pi, Pi camera, and IMU to allow your robot to navigate autonomously in your environment.
• Hands-on programming experience will show that you have acquired the fundamentals of robot movement, design, and sensing, and that you are able to translate them into a variety of practical uses in real-world problems.
• Completing the project will better prepare you to enter the field of robotics, as well as a growing variety of other career paths where robots are changing the face of every industry.

Please refer to the curriculum below for a weekly breakdown of each track.

Week 1

• introduction
• MIP Track: Using MATLAB for Dynamic Simulations
• AR route: Purchase the Dijkstra kit
• Test: A1.2 Integration of ODEs with MATLAB
• Programming Assignment: B1.3 Dijkstra’s Algorithm in Python

Week 2

• MIP Track: PD Control for Second-Order Systems
• AR Track: Rover Train
• Test: A2.2 PD Tracking
• Test: B2.10 Completed Rover View

Week 3

• MIP trajectory: Using EKF to obtain scalar heading from IMU
• AR Track: Calibration
• Test: A3.2 EKF for Scalar Position Estimation
• Test: B3.8 Calibration

Week 4

• MIP track: Mobile Pendulum Models (MIP)
• AR Track: Rover Controller Design
• Test: A4.2 Dynamic MIP Simulation
• Peer Assessment Assignment: B4.2 Programming a Label Tracking Algorithm

Week 5

• MIP Path: Local MIP Linearization and Linear Control
• AR path: Extended Kalman Filter for situational assessment
• Test: A5.2 MIP Balance Control
• Peer Review Assignment: B5.2 Extended Kalman Filtering for Situational Assessment

Week 6

• MIP Track: Course Planning with Feedback for the MIP
• AR Track: Integration
• Test: A6.2 Noise-Resistant Control and Design for the MIP
• Peer Assessment Assignment: B6.2 Completing Your Autonomous Rover

Skills you will develop

• Category: Serial Line Internet Protocol (SLIP)
• Category: Robotics
• Category: Robot
• Category: MATLAB