These courses can be taken individually via edX.org. Learners who pay for and complete all four courses are eligible for a MicroMasters certificate. Those who apply to certain Master's degree programs at Penn Engineering may use the MicroMasters certificate to accelerate their time to degree completion. Learn more about the MicroMasters path to an MSE.
These courses can be taken individually via ed.org. Learners who pay for and complete all four courses will receive a Professional Certificate.
Learn how to select, apply, and analyze the most appropriate data representations in your code and design high quality software that is easy to understand and modify.
This course begins with an introduction to the mechanics of flight and the design of quadrotor flying robots. The instructors will help you develop dynamic models, derive controllers, and synthesize planners for operating in three-dimensional environments. You will be exposed to the challenges of noisy sensors and maneuvering in complex environments. Finally, you see real world examples of possible applications in the rapidly-growing drone industry.
Robotic systems typically include three components: a mechanism which is capable of exerting forces and torques on the environment, a perception system for sensing the world, and a decision and control system which modulates the robot's behavior to achieve the desired ends. In this course we will consider the problem of how a robot decides what to do to achieve its goals. You will learn some common approaches to this problem including graph-based methods, randomized planners and artificial potential fields.
How can robots use their motors and sensors to move around in an unstructured environment? You will understand how to design robot bodies and behaviors that recruit limbs and more general appendages to apply physical forces that confer reliable mobility in a complex and dynamic world. We develop an approach to composing simple dynamical abstractions that partially automate the generation of complicated sensorimotor programs.
How can robots perceive the world and their own movements so that they accomplish navigation and manipulation tasks? In this module, we will study how images and videos acquired by cameras mounted on robots are transformed into representations like features and optical flow. You will come to understand how grasping objects is facilitated by the computation of 3D posing of objects and navigation can be accomplished by visual odometry and landmark-based localization.
How can robots determine their state and properties of the surrounding environment from noisy sensor measurements in time? In this course you will learn how to get robots to incorporate uncertainty into estimating and learning from a dynamic and changing world. Specific topics that will be covered include probabilistic generative models, Bayesian filtering for localization and mapping.
In our six-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.