An Introduction to Sampling based Motion Planning Algorithms


An Introduction to Sampling-based Motion Planning Algorithms, If you are interested in self-driving cars and robotics, then check out this course!

Course Description

Motion planning or path planning is an engineering field that deals with developing computational algorithms to calculate a path or a trajectory for a robot or any other autonomous vehicle. In this course, you will learn the well-known Rapidly Exploring Random Trees (RRT) and RRT* algorithms. These are sampling-based algorithms unlike search-based algorithms (A*) and are used to plan a path from a start to an end location whilst avoiding obstacles. You will be implementing these algorithms in Python. If you do not have any background in programming that is okay as I will teach everything from scratch. There will be 3 interactive assignments in which you will get to test your algorithms. By the end of this course, you will have a fundamental understanding of RRT-based algorithms. The objective of these algorithms is to generate a path consisting of waypoints from a start to an end location. It will be required to have Python 3.7 along with Numpy and Matplotlib installed to complete the assignments in this course. I will briefly go over installing Python as well, however, there are numerous resources that cover the details of setting up Python on your computer. I look forward to seeing you in this course!

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