Abstract
In this project we automated navigation of a 3D space with obstacles, and applied an optimal control strategy to track the dynamic path. The path planning and control algorithms were applied to a simulated environment and vehicle. The environment included random strong wind gusts to demonstrate the tracking performance of the controller.
Demonstration
The video below shows the simulation in action. Black lines represent planned paths, red line represents the path the vehicle has taken. Red blocks are unknown to the vehicle, and blue blocks have been sensed by proximity.
Report
The fine details of our implementation can be read in the accompanying report from the Stanford AA203 course.
Download Report
Authors and Contributors
This project was completed as a part of Stanford's AA203 Optimal Control Theory, Spring Quarter 2014
The team members are:
*Kyle Reinke (M.S. Aero/Astro Engineering)
*Manuel Lopez (PhD Candidate Aero/Astro Engineering).
This project utilized a Rapidly-expanding Random Trees algorithm written by Gavin Paul & Matthew Clifton.
Contact
Please refer to the Team Members side bar above to access our LinkedIn profiles. Thank you!
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