[Free] An Introduction To Sampling Based Motion Planning Algorithms
If you are interested in self driving cars and robotics, then check out this course! – Free Course
What you’ll learn
- Introduction to Python and the Tree Data Structure
- Motion Planning Basics
- Calculate a path using The Rapidly Exploring Random Trees (RRT) algorithm
- Calculate a path using The RRT Star and Informed RRT Star algorithms
Requirements
- No programming experience needed, I will teach you everything from scratch.
- It is preferred to already have Python 3.7.4 installed along with packages Numpy (1.16.x), Matplotlib (3.1.x)
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!
Author(s): Vinayak Deshpande
