Reinforcement Learning-Enabled UAV Itinerary Planning for Remote Sensing Applications in Smart Farming
UAV path planning for remote sensing aims to find the best-fitted routes to complete a data collection mission. UAVs plan the routes and move through them to remotely collect environmental data from particular target zones by using sensory devices such as cameras. Route planning may utilize machine...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Telecom |
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| Online Access: | https://www.mdpi.com/2673-4001/2/3/17 |
| _version_ | 1797411131369717760 |
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| author | Saeid Pourroostaei Ardakani Ali Cheshmehzangi |
| author_facet | Saeid Pourroostaei Ardakani Ali Cheshmehzangi |
| author_sort | Saeid Pourroostaei Ardakani |
| collection | DOAJ |
| description | UAV path planning for remote sensing aims to find the best-fitted routes to complete a data collection mission. UAVs plan the routes and move through them to remotely collect environmental data from particular target zones by using sensory devices such as cameras. Route planning may utilize machine learning techniques to autonomously find/select cost-effective and/or best-fitted routes and achieve optimized results including: minimized data collection delay, reduced UAV power consumption, decreased flight traversed distance and maximized number of collected data samples. This paper utilizes a reinforcement learning technique (location and energy-aware Q-learning) to plan UAV routes for remote sensing in smart farms. Through this, the UAV avoids heuristically or blindly moving throughout a farm, but this takes the benefits of environment exploration–exploitation to explore the farm and find the shortest and most cost-effective paths into target locations with interesting data samples to collect. According to the simulation results, utilizing the Q-learning technique increases data collection robustness and reduces UAV resource consumption (e.g., power), traversed paths, and remote sensing latency as compared to two well-known benchmarks, IEMF and TBID, especially if the target locations are dense and crowded in a farm. |
| first_indexed | 2024-03-09T04:40:34Z |
| format | Article |
| id | doaj.art-fbd9227e1b2e4f66a3af2236687b3fdd |
| institution | Directory Open Access Journal |
| issn | 2673-4001 |
| language | English |
| last_indexed | 2024-03-09T04:40:34Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Telecom |
| spelling | doaj.art-fbd9227e1b2e4f66a3af2236687b3fdd2023-12-03T13:21:36ZengMDPI AGTelecom2673-40012021-07-012325527010.3390/telecom2030017Reinforcement Learning-Enabled UAV Itinerary Planning for Remote Sensing Applications in Smart FarmingSaeid Pourroostaei Ardakani0Ali Cheshmehzangi1School of Computer Science, University of Nottingham Ningbo, Ningbo 315100, ChinaDepartment of Architecture and Built Environment, University of Nottingham Ningbo, Ningbo 315100, ChinaUAV path planning for remote sensing aims to find the best-fitted routes to complete a data collection mission. UAVs plan the routes and move through them to remotely collect environmental data from particular target zones by using sensory devices such as cameras. Route planning may utilize machine learning techniques to autonomously find/select cost-effective and/or best-fitted routes and achieve optimized results including: minimized data collection delay, reduced UAV power consumption, decreased flight traversed distance and maximized number of collected data samples. This paper utilizes a reinforcement learning technique (location and energy-aware Q-learning) to plan UAV routes for remote sensing in smart farms. Through this, the UAV avoids heuristically or blindly moving throughout a farm, but this takes the benefits of environment exploration–exploitation to explore the farm and find the shortest and most cost-effective paths into target locations with interesting data samples to collect. According to the simulation results, utilizing the Q-learning technique increases data collection robustness and reduces UAV resource consumption (e.g., power), traversed paths, and remote sensing latency as compared to two well-known benchmarks, IEMF and TBID, especially if the target locations are dense and crowded in a farm.https://www.mdpi.com/2673-4001/2/3/17UAVreinforcement learningQ-learningpath planningremote sensing |
| spellingShingle | Saeid Pourroostaei Ardakani Ali Cheshmehzangi Reinforcement Learning-Enabled UAV Itinerary Planning for Remote Sensing Applications in Smart Farming Telecom UAV reinforcement learning Q-learning path planning remote sensing |
| title | Reinforcement Learning-Enabled UAV Itinerary Planning for Remote Sensing Applications in Smart Farming |
| title_full | Reinforcement Learning-Enabled UAV Itinerary Planning for Remote Sensing Applications in Smart Farming |
| title_fullStr | Reinforcement Learning-Enabled UAV Itinerary Planning for Remote Sensing Applications in Smart Farming |
| title_full_unstemmed | Reinforcement Learning-Enabled UAV Itinerary Planning for Remote Sensing Applications in Smart Farming |
| title_short | Reinforcement Learning-Enabled UAV Itinerary Planning for Remote Sensing Applications in Smart Farming |
| title_sort | reinforcement learning enabled uav itinerary planning for remote sensing applications in smart farming |
| topic | UAV reinforcement learning Q-learning path planning remote sensing |
| url | https://www.mdpi.com/2673-4001/2/3/17 |
| work_keys_str_mv | AT saeidpourroostaeiardakani reinforcementlearningenableduavitineraryplanningforremotesensingapplicationsinsmartfarming AT alicheshmehzangi reinforcementlearningenableduavitineraryplanningforremotesensingapplicationsinsmartfarming |