Deep UAV Path Planning with Assured Connectivity in Dense Urban Setting
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910497675149312 |
|---|---|
| author | Oh, Jiyong Raza, Syed M. Mwasinga, Lusungu J. Kim, Moonseong Choo, Hyunseung |
| author_facet | Oh, Jiyong Raza, Syed M. Mwasinga, Lusungu J. Kim, Moonseong Choo, Hyunseung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_15225 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep UAV Path Planning with Assured Connectivity in Dense Urban Setting Oh, Jiyong Raza, Syed M. Mwasinga, Lusungu J. Kim, Moonseong Choo, Hyunseung Artificial Intelligence Robotics Signal Processing 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. |
| title | Deep UAV Path Planning with Assured Connectivity in Dense Urban Setting |
| topic | Artificial Intelligence Robotics Signal Processing |
| url | https://arxiv.org/abs/2406.15225 |