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Autonomous Robots: Path Planning

  
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Autonomous Robots: Path Planning

Autonomous Robots: Path Planning
.MP4, AVC, 1920x1080, 30 fps | English, AAC, 2 Ch | 3h 35m | 611 MB
Instructor: Daniel Stang

Use the A* (A-star) search algorithm to find the driving route between any two locations in New York City, just as Google Maps does


Learn

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


About

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:
https://github.com/PacktPublishing/Autonomous-Robots-Path-Planning


Features

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
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