Course Overview

Path planning is a key component required to solve the larger problem of “autonomous robot navigation”. In this course, you will learn about the most used path planning algorithms and you will deploy theory into practice by running coding exercises and simulations in ROS.

You will learn:

You will start the course by learning how to develop allegedly one of the most famous algorithms in Computer Science: Dijkstra’s shortest path algorithm.

We will continue by introducing Greedy Best-First Search, which evolves the fundamental principles set by Dijkastra to include a heuristic function which in some cases can speed up the search process significantly.
As your understanding progresses, you will expand your path planning skills evolving the properties of the algorithm to convert it into the implementation of A* (A -Star).

Then you will turn to learn a method that takes a completely different approach to path planning, namely RRT.

At the end of this course, you will be well aware of various different approaches that have been developed and applied to successfully solve the global path planning problem. Furthermore, you will be able to understand and explain the differences between them as well as the advantages and drawbacks of each other. Last but not least you will have gained solid practical experience by implementing these methods yourself.

Simulation Robots Used

TurtleBot 2