Autonomous Robots: Nonholonomic Motion Planning Algorithms

Description

Important Note:

This course is a Part 2 to my free course Introduction to Sampling Based Motion Planning Algorithms.

However, you do not need to finish the free course first, since I cover all of the free course material (plus a whole lot more!) within this course.

With that said, if you already have completed Introduction to Sampling Based Motion Planning Algorithms, then you will have an advantage.

Highlights

• Learn affine transformations and vector rotation matrices.

• Derivation of the Dubins Path to generate smooth trajectories given two points on a map.

• Combine Dubins Path with motion planning RRT and RRT* algorithms.

• 4 interactive assignments.

Description

• Motion planning is a field of engineering dealing with calculating a path from a start to a goal location whilst avoiding obstacles, for example using Google or Apple Maps.

• The vast majority of vehicles in today's world i.e (cars, boats, planes) are non holonomic, meaning they have limited degrees of freedom of movement with respect to the available space.

• For instance, think of shifting a car between adjacent parking spots. The car cannot 'slide' sideways into the second parking spot. It has to reverse and turn in to the spot, or go around in a circular path.

• Given two arbitrary positions and headings on a map, the task is to find a smooth trajectory which satisfies the speed constraints of the vehicle. This is accomplished by using the Dubins Path.

• In this course, you will learn how to derive the Dubins Path from first principles and also implement and test the method via an assignment. Next, you will learn how to combine this theory with sampling based motion planning algorithms. The next two assignments will involve finding a path using RRT and RRT* with Dubins Path.

• Lastly, the final assignment will involve incremental RRT with Dubins Path for a realistic roadmap scenario, where the vehicle has limited information.

  
  

Requirements

• Python, Numpy and Matplotlib

• Basic understanding of scalars and vectors

• You could alternatively complete the course using another programming language, however in this case you will have to transcribe the assignments on your own, in that specific language.

  
  

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