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arXiv · 2406.15225

Deep UAV Path Planning with Assured Connectivity in Dense Urban Setting

Jiyong Oh·Syed M. Raza·Lusungu J. Mwasinga·Moonseong Kim·Hyunseung Choo
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Abstract

Unmanned Ariel Vehicle (UAV) services with 5G connectivity is an emerging field with numerous applications. Operator-controlled UAV flights and manual static flight configurations are major limitations for the wide adoption of scalability of UAV services. Several services depend on excellent UAV connectivity with a cellular network and maintaining it is challenging in predetermined flight paths. This paper addresses these limitations by proposing a Deep Reinforcement Learning (DRL) framework for UAV path planning with assured connectivity (DUPAC). During UAV flight, DUPAC determines the best route from a defined source to the destination in terms of distance and signal quality. The viability and performance of DUPAC are evaluated under simulated real-world urban scenarios using the Unity framework. The results confirm that DUPAC achieves an autonomous UAV flight path similar to base method with only 2% increment while maintaining an average 9% better connection quality throughout the flight.

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Jiyong Oh, Syed M. Raza, Lusungu J. Mwasinga, Moonseong Kim, Hyunseung Choo. 2024-06-21. Deep UAV Path Planning with Assured Connectivity in Dense Urban Setting. https://arxiv.org/abs/2406.15225

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