Efficient UAV path planning using coverage map‐based value iteration
Abstract This letter presents an efficient coverage map‐based unmanned aerial vehicle (UAV) navigation framework in cellular communication systems. Unlike previous research that focused on viewing UAV navigation as a Markov decision process in unknown continuous state space and leveraged various mod...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-07-01
|
| Series: | Electronics Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ell2.12867 |
| _version_ | 1797784178488508416 |
|---|---|
| author | Zhaozhou Wu Xingqi Zhang |
| author_facet | Zhaozhou Wu Xingqi Zhang |
| author_sort | Zhaozhou Wu |
| collection | DOAJ |
| description | Abstract This letter presents an efficient coverage map‐based unmanned aerial vehicle (UAV) navigation framework in cellular communication systems. Unlike previous research that focused on viewing UAV navigation as a Markov decision process in unknown continuous state space and leveraged various model‐free and deep neural network‐based reinforcement learning algorithms, a more straightforward and efficient model‐based value iteration algorithm is proposed. The algorithm leverages prior knowledge obtained through empirical channel models to develop a sampled coverage map that can be used in value iteration. A deep neural network is subsequently trained with supervised learning to approximate the optimal Q function in continuous state space. Finally, the trained neural network is applied to obtain a UAV trajectory that optimizes the objective function. |
| first_indexed | 2024-03-13T00:36:16Z |
| format | Article |
| id | doaj.art-e0d1ff30497949269402440f32924bf8 |
| institution | Directory Open Access Journal |
| issn | 0013-5194 1350-911X |
| language | English |
| last_indexed | 2024-03-13T00:36:16Z |
| publishDate | 2023-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Electronics Letters |
| spelling | doaj.art-e0d1ff30497949269402440f32924bf82023-07-10T05:18:29ZengWileyElectronics Letters0013-51941350-911X2023-07-015913n/an/a10.1049/ell2.12867Efficient UAV path planning using coverage map‐based value iterationZhaozhou Wu0Xingqi Zhang1School of Electrical and Electronic Engineering University College Dublin DublinIrelandSchool of Electrical and Electronic Engineering University College Dublin DublinIrelandAbstract This letter presents an efficient coverage map‐based unmanned aerial vehicle (UAV) navigation framework in cellular communication systems. Unlike previous research that focused on viewing UAV navigation as a Markov decision process in unknown continuous state space and leveraged various model‐free and deep neural network‐based reinforcement learning algorithms, a more straightforward and efficient model‐based value iteration algorithm is proposed. The algorithm leverages prior knowledge obtained through empirical channel models to develop a sampled coverage map that can be used in value iteration. A deep neural network is subsequently trained with supervised learning to approximate the optimal Q function in continuous state space. Finally, the trained neural network is applied to obtain a UAV trajectory that optimizes the objective function.https://doi.org/10.1049/ell2.12867autonomous aerial vehiclespath planningnavigation |
| spellingShingle | Zhaozhou Wu Xingqi Zhang Efficient UAV path planning using coverage map‐based value iteration Electronics Letters autonomous aerial vehicles path planning navigation |
| title | Efficient UAV path planning using coverage map‐based value iteration |
| title_full | Efficient UAV path planning using coverage map‐based value iteration |
| title_fullStr | Efficient UAV path planning using coverage map‐based value iteration |
| title_full_unstemmed | Efficient UAV path planning using coverage map‐based value iteration |
| title_short | Efficient UAV path planning using coverage map‐based value iteration |
| title_sort | efficient uav path planning using coverage map based value iteration |
| topic | autonomous aerial vehicles path planning navigation |
| url | https://doi.org/10.1049/ell2.12867 |
| work_keys_str_mv | AT zhaozhouwu efficientuavpathplanningusingcoveragemapbasedvalueiteration AT xingqizhang efficientuavpathplanningusingcoveragemapbasedvalueiteration |