100% OFF- An Introduction to Sampling based Motion Planning Algorithms

An Introduction to Sampling based Motion Planning Algorithms , Interested in self driving cars and robotics? Then check out this course!

Course Description

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. Be sure to leave a rating when you finish. I look forward to seeing you in this course!