An Introduction to Sampling based Motion Planning Algorithms

Motion planning or path planning is an engineering field which 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 are 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 which cover the details of setting up Python on your computer. I look forward to seeing you in this course!