Autonomous Robots: Path Planning [Video]

More Information
  • Become well-versed with path planning
  • Understand the concept of robotics
  • Get to grips with advanced heuristics

Path planning involves finding an optimal and viable path from the current location to the goal location. This is crucial for any robot that must move something in the real world, whether it's a robotic arm or a self-driving car.

This course will get you up to speed with the A* algorithm that is one of the most fundamental robotics algorithms. A fun fact is that this algorithm was even used on the first general-purpose mobile robot – Shakey the Robot. You’ll go on to understand how to use Robotics to create a viable path from your start to end location. Next, you'll start the search on a small grid between two lanes, and then gradually scale up to navigate between any two locations in New York City. As you progress, you will build on your knowledge of robotics and get hands-on with path planning. A dedicated section will also guide you through advanced heuristics.

By the end of this course, you will be well-versed with path planning and have the skills you need to use the A* algorithm to find the shortest driving path between two locations.

All the code and supporting files for this course are available here:

  • Get to grips with breadth-first search (BFS) and depth-first search (DFS) implementation
  • Understand the difference between BFS and DFS implementation
  • Get up to speed with A searches in New York City
Course Length 3 hours 35 minutes
ISBN 9781800567290
Date Of Publication 30 Apr 2020


Daniel Stang

Daniel Stang is a robotics software engineer who holds a master’s degree in mechanical engineering, which he earned for his research in control system design for automotive applications. In his first job out of school, he was responsible for designing motion controllers and stabilization systems for military tank turrets. Daniel has previously written robotic software for a startup based out of Toronto, Canada, and currently writes software for autonomous vehicles in California.