Download Udemy – Autonomous Robots: Nonholonomic Motion Planning Algorithms 2023-4

Autonomous Robots_ Nonholonomic Motion Planning Algorithms


Autonomous Robots: Nonholonomic Motion Planning Algorithms, the course for training autonomous robots and learning to calculate smooth paths with sampling-based motion planning has been published by Udemy Academy. Motion planning is an engineering field that deals with calculating a path from a start to a destination while avoiding obstacles, for example using Google or Apple Maps. The vast majority of vehicles in today’s world, such as cars, boats, and planes, are non-holonomic, which means that they have limited degrees of freedom of movement due to the available space. For example, consider moving a car between adjacent parking lots. The car cannot go to the second parking lot from the side. It should reverse and turn towards the point or move in a circular path. Given two arbitrary positions and headings on the map, the task is to find a smooth path that overcomes the vehicle’s speed limits.

This is done using the Dubin path. In this lesson, you will learn how to derive Dubin’s path from the basics and also implement and test this method through a tutorial. In the following, you will learn how to combine this theory with motion planning algorithms based on sampling. The next two missions will involve finding a route using RRT and RRT * with Dubin’s route. Finally, the final mission will include incremental RRT with Dubin’s trajectory for a realistic roadmap scenario where the vehicle has limited information.

What you will learn

  • Learn the Dubins Curve Algorithm to generate a smooth path between two points on a map.
  • Compute a path using Rapid Random Tree Search (RRT) and RRT* algorithms combined with Dubins Curve.
  • Learn the basics of incremental path planning for real-time applications.
  • Determine a path using incremental RRT with Dubins Curve and analyze your results quantitatively.

Who is this course suitable for?

  • Anyone interested in automation, robotics, algorithms and path planning.

Autonomous Robots: Nonholonomic Motion Planning Algorithms course specifications

  • Publisher: Udemy
  • teacher : Vinayak Deshpande
  • English language
  • Education level: introductory
  • Number of courses: 23
  • Training duration: 3 hours and 21 minutes

Chapters of Autonomous Robots: Nonholonomic Motion Planning Algorithms course

Course prerequisites

  • No programming experience needed. I will teach you everything from scratch.
  • It is recommended that students have Python 3.9.x, Numpy 1.23.x and Matplotlib 3.x ready for the assignments.


Autonomous Robots_ Nonholonomic Motion Planning Algorithms

Sample video

Installation guide

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download link

Download part 1 – 1 GB

Download part 2 – 1 GB

Download part 3 – 291 MB

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2.28 GB

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