ML for Drone Optimization
Enabling Beyond Visual Line of Sight (BVLOS) operations with drones is key to completely harness the potential of drones A critical aspect of BVLOS drone applications is optimizing the drone path.
A key challenge in the route optimization is that dynamic nature of the wireless channel. Since the channel is impacted by a variety of factors such as weather, user traffic, geography, etc., it is impossible to build a standard channel model that can be used to compute the optimal route.
BIG PICTURE
Beyond Visual Line of Sight (BVLOS) operation is key to enable applications that require large-scale drone deployment
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DELART ML TECHNICAL TEAM
Dr. Vignesh Manohar
Sr. Antenna Engineer
CEO, Delart Canada
CEO, Delart Canada
Dr. Pratheep BV
Engineering Lead
PhD – Wireless Engineering – UT Dallas
PhD – Wireless Engineering – UT Dallas
Dr. Craig Kleski
Lead ML engineer
PhD – Mathematics – Univ of Virginia
PhD – Mathematics – Univ of Virginia
Dr. Riten Gupta
Wireless Engineer
PhD – Wireless Engineering – UMich. Ann Arbor
PhD – Wireless Engineering – UMich. Ann Arbor
How do we do this?
With Reinforcement Learning, we can have the drone ‘learn’ the characteristics of wireless environment
Challenge: RL model needs to be trained offline before it can be implemented
Solution: Generating real-world RF data in a controlled lab/simulation environment that can take into account several factors that impacts a wireless channel.
Methodology
REINFORCEMENT LEARNING ALGORITHM
NEXT STEPS
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