UAV-Enabled Inspection System With No-Fly Zones: DRL-Based Joint Mobile Nest Scheduling and UAV Trajectory Design
Unmanned aerial vehicle (UAV)-enabled inspection is regarded as a promising technology in the electricity system. This paper investigates a UAV-enabled inspection system in an urban environment with no-fly zones (NFZs), where the UAV flies to inspection points to capture images while constrained by...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10839376/ |
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| author | Jin Dai Yunfei Gao Chao Cai Wei Xiong Manjia Liu |
| author_facet | Jin Dai Yunfei Gao Chao Cai Wei Xiong Manjia Liu |
| author_sort | Jin Dai |
| collection | DOAJ |
| description | Unmanned aerial vehicle (UAV)-enabled inspection is regarded as a promising technology in the electricity system. This paper investigates a UAV-enabled inspection system in an urban environment with no-fly zones (NFZs), where the UAV flies to inspection points to capture images while constrained by limited onboard energy. The aim of this paper is to minimize the whole inspection time via joint optimization of the mobile nest’s scheduling and UAV trajectory while satisfying constraints related to energy maximization and avoiding NFZs. The formulated problem is a multivariate mixed non-convex optimization problem (due to the presence of NFZs), which makes it challenging to address with conventional techniques such as successive convex approximation and graph theory. To solve these issues, we first model the proposed problem as a Markov decision process (MDP). Then we propose an efficient two-step optimization method that involves optimizing the UAV trajectory with a modified multi-step dueling DDQN (MSD-DDQN) algorithm and planning the mobile nest’s path using the A* algorithm. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments. In particular, the proposed method not only accelerates algorithm convergence but also reduces inspection time by 35 % compared to baseline methods. |
| format | Article |
| id | doaj-art-93db9eb9cb1c490ea090c7f3cc566e19 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-93db9eb9cb1c490ea090c7f3cc566e192025-01-21T00:00:54ZengIEEEIEEE Access2169-35362025-01-0113108441085610.1109/ACCESS.2025.352908510839376UAV-Enabled Inspection System With No-Fly Zones: DRL-Based Joint Mobile Nest Scheduling and UAV Trajectory DesignJin Dai0Yunfei Gao1https://orcid.org/0000-0001-8869-5625Chao Cai2Wei Xiong3Manjia Liu4State Grid Hubei Electric Power Company Ltd., Wuhan, ChinaSchool of Electronic Information, Wuhan University, Wuhan, ChinaState Grid Hubei Electric Power Company Ltd., Wuhan, ChinaState Grid Hubei Electric Power Company Ltd., Wuhan, ChinaState Grid Hubei Electric Power Research Institute, Wuhan, ChinaUnmanned aerial vehicle (UAV)-enabled inspection is regarded as a promising technology in the electricity system. This paper investigates a UAV-enabled inspection system in an urban environment with no-fly zones (NFZs), where the UAV flies to inspection points to capture images while constrained by limited onboard energy. The aim of this paper is to minimize the whole inspection time via joint optimization of the mobile nest’s scheduling and UAV trajectory while satisfying constraints related to energy maximization and avoiding NFZs. The formulated problem is a multivariate mixed non-convex optimization problem (due to the presence of NFZs), which makes it challenging to address with conventional techniques such as successive convex approximation and graph theory. To solve these issues, we first model the proposed problem as a Markov decision process (MDP). Then we propose an efficient two-step optimization method that involves optimizing the UAV trajectory with a modified multi-step dueling DDQN (MSD-DDQN) algorithm and planning the mobile nest’s path using the A* algorithm. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments. In particular, the proposed method not only accelerates algorithm convergence but also reduces inspection time by 35 % compared to baseline methods.https://ieeexplore.ieee.org/document/10839376/UAV inspectionUAV trajectory designmobile nest path planningdeep reinforcement learning |
| spellingShingle | Jin Dai Yunfei Gao Chao Cai Wei Xiong Manjia Liu UAV-Enabled Inspection System With No-Fly Zones: DRL-Based Joint Mobile Nest Scheduling and UAV Trajectory Design IEEE Access UAV inspection UAV trajectory design mobile nest path planning deep reinforcement learning |
| title | UAV-Enabled Inspection System With No-Fly Zones: DRL-Based Joint Mobile Nest Scheduling and UAV Trajectory Design |
| title_full | UAV-Enabled Inspection System With No-Fly Zones: DRL-Based Joint Mobile Nest Scheduling and UAV Trajectory Design |
| title_fullStr | UAV-Enabled Inspection System With No-Fly Zones: DRL-Based Joint Mobile Nest Scheduling and UAV Trajectory Design |
| title_full_unstemmed | UAV-Enabled Inspection System With No-Fly Zones: DRL-Based Joint Mobile Nest Scheduling and UAV Trajectory Design |
| title_short | UAV-Enabled Inspection System With No-Fly Zones: DRL-Based Joint Mobile Nest Scheduling and UAV Trajectory Design |
| title_sort | uav enabled inspection system with no fly zones drl based joint mobile nest scheduling and uav trajectory design |
| topic | UAV inspection UAV trajectory design mobile nest path planning deep reinforcement learning |
| url | https://ieeexplore.ieee.org/document/10839376/ |
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