Joint optimization of UAV communication connectivity and obstacle avoidance in urban environments using a double-map approach
Abstract Cellular-connected unmanned aerial vehicles (UAVs), which have the potential to extend cellular services from the ground into the airspace, represent a promising technological advancement. However, the presence of communication coverage black holes among base stations and various obstacles...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-03-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13634-024-01130-6 |
| _version_ | 1797258913393934336 |
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| author | Weizhi Zhong Xin Wang Xiang Liu Zhipeng Lin Farman Ali |
| author_facet | Weizhi Zhong Xin Wang Xiang Liu Zhipeng Lin Farman Ali |
| author_sort | Weizhi Zhong |
| collection | DOAJ |
| description | Abstract Cellular-connected unmanned aerial vehicles (UAVs), which have the potential to extend cellular services from the ground into the airspace, represent a promising technological advancement. However, the presence of communication coverage black holes among base stations and various obstacles within the aerial domain pose significant challenges to ensuring the safe operation of UAVs. This paper introduces a novel trajectory planning scheme, namely the double-map assisted UAV approach, which leverages deep reinforcement learning to address these challenges. The mission execution time, wireless connectivity, and obstacle avoidance are comprehensively modeled and analyzed in this approach, leading to the derivation of a novel joint optimization function. By utilizing an advanced technique known as dueling double deep Q network (D3QN), the objective function is optimized, while employing a mechanism of prioritized experience replay strengthens the training of effective samples. Furthermore, the connectivity and obstacle information collected by the UAV during flight are utilized to generate a map of radio and environmental data for simulating the flying process, thereby significantly reducing operational costs. The numerical results demonstrate that the proposed method effectively circumvents obstacles and areas with weak connections during flight, while also considering mission completion time. |
| first_indexed | 2024-04-24T23:01:05Z |
| format | Article |
| id | doaj.art-021ff8ca3c874643b13570674e4766c8 |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-04-24T23:01:05Z |
| publishDate | 2024-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-021ff8ca3c874643b13570674e4766c82024-03-17T12:41:58ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802024-03-012024112610.1186/s13634-024-01130-6Joint optimization of UAV communication connectivity and obstacle avoidance in urban environments using a double-map approachWeizhi Zhong0Xin Wang1Xiang Liu2Zhipeng Lin3Farman Ali4Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and AstronauticsKey Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and AstronauticsKey Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and AstronauticsKey Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and AstronauticsDepartment of Electrical Engineering, Qurtuba University of Science and ITAbstract Cellular-connected unmanned aerial vehicles (UAVs), which have the potential to extend cellular services from the ground into the airspace, represent a promising technological advancement. However, the presence of communication coverage black holes among base stations and various obstacles within the aerial domain pose significant challenges to ensuring the safe operation of UAVs. This paper introduces a novel trajectory planning scheme, namely the double-map assisted UAV approach, which leverages deep reinforcement learning to address these challenges. The mission execution time, wireless connectivity, and obstacle avoidance are comprehensively modeled and analyzed in this approach, leading to the derivation of a novel joint optimization function. By utilizing an advanced technique known as dueling double deep Q network (D3QN), the objective function is optimized, while employing a mechanism of prioritized experience replay strengthens the training of effective samples. Furthermore, the connectivity and obstacle information collected by the UAV during flight are utilized to generate a map of radio and environmental data for simulating the flying process, thereby significantly reducing operational costs. The numerical results demonstrate that the proposed method effectively circumvents obstacles and areas with weak connections during flight, while also considering mission completion time.https://doi.org/10.1186/s13634-024-01130-6Cellular-connected UAVTrajectory planningRadio mapDRLEnvironment characteristic |
| spellingShingle | Weizhi Zhong Xin Wang Xiang Liu Zhipeng Lin Farman Ali Joint optimization of UAV communication connectivity and obstacle avoidance in urban environments using a double-map approach EURASIP Journal on Advances in Signal Processing Cellular-connected UAV Trajectory planning Radio map DRL Environment characteristic |
| title | Joint optimization of UAV communication connectivity and obstacle avoidance in urban environments using a double-map approach |
| title_full | Joint optimization of UAV communication connectivity and obstacle avoidance in urban environments using a double-map approach |
| title_fullStr | Joint optimization of UAV communication connectivity and obstacle avoidance in urban environments using a double-map approach |
| title_full_unstemmed | Joint optimization of UAV communication connectivity and obstacle avoidance in urban environments using a double-map approach |
| title_short | Joint optimization of UAV communication connectivity and obstacle avoidance in urban environments using a double-map approach |
| title_sort | joint optimization of uav communication connectivity and obstacle avoidance in urban environments using a double map approach |
| topic | Cellular-connected UAV Trajectory planning Radio map DRL Environment characteristic |
| url | https://doi.org/10.1186/s13634-024-01130-6 |
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