# An Introduction to Sampling based Motion Planning Algorithms

Interested in self driving cars and robotics? Then check out this course!
Free tutorial
Rating: 4.7 out of 5 (54 ratings)
4,215 students
1hr 35min of on-demand video
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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!

## Who this course is for:

• Anyone with an interest in programming, robotics and autonomous vehicles

## Instructor

• 4.4 Instructor Rating
• 75 Reviews
• 4,360 Students
• 3 Courses

I have a masters degree in Mechanical Engineering in Control Theory and Simulation with 3 conference publications in the field of Model Predictive Control. My Bachelor's degree was also in Mechanical Engineering. I currently work as a Development Engineer at a defense contractor in Canada, building simulations and model based design architecture in Matlab, Simulink and C++. I have over 3 years of experience with modeling and simulation of mechanical and aerospace systems.

I have always had a strong passion for teaching ever since I started my YouTube Channel in 2016. My mission statement is to deliver high quality training material on complex engineering topics.