Making a Drone Smarter With Motion Planning

This fully autonomous drone has an onboard computer ‘brain’, camera ‘eyes’, and an algorithm that generates the fastest path around unknown obstacles as they’re detected mid-flight. Everything is computed onboard with no need for a radio connection to the ground, making it completely immune to jamming.

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GPS-denied, vision-based autonomy is a very popular topic in robotics right now. Most importantly: once you know where you are, how do you most efficiently navigate around the environment without bumping into things? Variations of Dijkstra’s algorithm such as A* or D* Lite can be used to quickly and efficiently calculate the optimal path over ‘nodes’, or potential waypoints. This is the same general algorithm used in google maps to find the fastest route through traffic. Putting all of this together on a flying drone took a bit of specialized hardware, some help from Robot Operating System (ROS), and a whole lot of testing. Yes, there are pre-existing packages for pretty much every feature I implemented, but where's the fun in using someone else's code? If you learned something, I’d greatly appreciate a like on this video and maybe even a subscription to my channel for more projects like this in the future.

00:00 Intro
00:56 How Waypoint Autonomy Works
03:00 Hardware Overview
04:38 Position Control Demo
06:37 Motion Planning 101
07:23 Dijkstra’s algorithm, A*, and D* Lite
09:16 Obstacle Detection
09:47 Complete Demo
12:08 Conclusions

#Drone #MotionPlanning #Autonomy