An Introduction to Sampling based Motion Planning Algorithms

An Introduction to Sampling based Motion Planning Algorithms, available at Free, has an average rating of 4.45, with 16 lectures, based on 52 reviews, and has 4166 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone with an interest in programming, robotics and autonomous vehicles It is particularly useful for Anyone with an interest in programming, robotics and autonomous vehicles.

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Summary

Title: An Introduction to Sampling based Motion Planning Algorithms

Price: Free

Average Rating: 4.45

Number of Lectures: 16

Number of Published Lectures: 16

Number of Curriculum Items: 16

Number of Published Curriculum Objects: 16

Original Price: Free

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Anyone with an interest in programming, robotics and autonomous vehicles

Target Audiences

Anyone with an interest in programming, robotics and autonomous vehicles

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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Setting up Python and Downloading the Git Repo

Lecture 3: Data Structures: Introduction to Trees

Lecture 4: Assignment 0

Chapter 2: The Rapidly Exploring Random Trees (RRT) Algorithm

Lecture 1: RRT Algorithm Explanation

Lecture 2: Assignment 1 – Intro

Lecture 3: Assignment 1 – Solution

Chapter 3: The Rapidly Exploring Random Trees Star (RRT*) Algorithm

Lecture 1: RRT Star Algorithm Explanation

Lecture 2: Assignment 2 – Intro

Lecture 3: Assignment 2 – Solution

Chapter 4: The Informed RRT Star Algorithm

Lecture 1: Informed RRT Star Algorithm Explanation

Lecture 2: Assignment 3 – Intro

Lecture 3: Assignment 3 – Solution

Chapter 5: Conclusion

Lecture 1: Conclusion

Lecture 2: Announcement: New Course (Nonholonomic Motion Planning)

Lecture 3: Announcement: New Course (Flight Simulation and Control)

Instructors

Vinayak Deshpande
Engineer

Rating Distribution

1 stars: 2 votes
2 stars: 1 votes
3 stars: 4 votes
4 stars: 12 votes
5 stars: 33 votes

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