Path Planning Basics Course - Python

Learn the theory behind the most used path planning algorithms.

Path Planning Basics course

Course Summary

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.

What 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.

Course Overview

Introduction to the Course

A brief introduction to the course contents. It includes a practical demonstration.

Dijkstra algorithm

In this unit, we will get into Dijkstra’s super-famous path planning algorithm and apply it to the motion planning of a mobile robot.

A* search algorithm

This unit focuses on the A* (pronounced "A-star"). A* is one of the most popular choices for pathfinding, because it is only a step up from Dijkstra's, but can often find an optimal path much faster.

Rapidly-Exploring Random Tree (RRT)

This unit covers the fundamentals of the Rapidly-Exploring Random Tree (RRT) algorithm applied to the robotic path planning problem.

Artificial Potential Fields

This unit covers the fundamentals of the artificial potential fields (also known as APF) method applied to the mobile robotic path planning problem.

Final Project: Roadmap Based Path Planning

This last unit is meant to be a bit of an extension lesson about path planning as well as a review of some of the core concepts that you learned throughtout this course. Specifically, you will be applying your knowledge about Dijkstra's shortest path algorithm, only this time, for a robot that is constrained by a road network.


Roberto Zegers

PMP, B.Sc in Business Management. He loves all things robotics and is constantly exploring technology advancements evolving and shaping up the future of business.

Roberto Zegers

Robots used

Turtlebot robot

Turtlebot robot

Learning Path

Basic Robotics Theory

Basic Robotics Theory


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