Deep UAV Path Planning with Assured Connectivity in Dense Urban Setting
This work addresses connectivity challenges for UAV services in urban environments, but it is incremental as it builds on existing methods with modest improvements.
The paper tackled the problem of autonomous UAV path planning with assured connectivity in dense urban settings, proposing a DRL framework that achieved a 2% increase in path length compared to a base method while maintaining 9% better average connection quality.
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.