Deep UAV Path Planning with Assured Connectivity in Dense Urban Setting

• URL: http://arxiv.org/abs/2406.15225v1
• Date: Fri, 21 Jun 2024 15:10:25 GMT
• Title: Deep UAV Path Planning with Assured Connectivity in Dense Urban Setting
• Abstract summary: This paper proposes a 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 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.
• Score: 2.4156037371075088
• License: http://creativecommons.org/licenses/by/4.0/
• 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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