Accuracy Improvement of Autonomous Straight Take-off, Flying Forward, and Landing of a Drone with Deep Reinforcement Learning
Nowadays, drones are expected to be used in several engineering and safety applications both indoors and outdoors, e.g., exploration, rescue, sport, entertainment, and convenience. Among those applications, it is important to make a drone capable of flying autonomously to carry out an inspection pat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2020-06-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/125941515/view |
| _version_ | 1818247292838215680 |
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| author | Che-Cheng Chang Jichiang Tsai Peng-Chen Lu Chuan-An Lai |
| author_facet | Che-Cheng Chang Jichiang Tsai Peng-Chen Lu Chuan-An Lai |
| author_sort | Che-Cheng Chang |
| collection | DOAJ |
| description | Nowadays, drones are expected to be used in several engineering and safety applications both indoors and outdoors, e.g., exploration, rescue, sport, entertainment, and convenience. Among those applications, it is important to make a drone capable of flying autonomously to carry out an inspection patrol. In this paper, we present a novel method that uses ArUco markers as a reference to improve the accuracy of a drone on autonomous straight take-off, flying forward, and landing based on Deep Reinforcement Learning (DRL). More specifically, the drone first detects a specific marker with one of its onboard cameras. Then it calculates the position and orientation relative to the marker so as to adjust its actions for achieving better accuracy with a DRL method. We perform several simulation experiments with different settings, i.e., different sets of states, different sets of actions and even different DRL methods, by using the Robot Operating System (ROS) and its Gazebo simulator. Simulation results show that our proposed methods can efficiently improve the accuracy of the considered actions. |
| first_indexed | 2024-12-12T15:02:24Z |
| format | Article |
| id | doaj.art-b02903beb14746328ff0216dc2ce43d4 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-12-12T15:02:24Z |
| publishDate | 2020-06-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-b02903beb14746328ff0216dc2ce43d42022-12-22T00:20:46ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832020-06-0113110.2991/ijcis.d.200615.002Accuracy Improvement of Autonomous Straight Take-off, Flying Forward, and Landing of a Drone with Deep Reinforcement LearningChe-Cheng ChangJichiang TsaiPeng-Chen LuChuan-An LaiNowadays, drones are expected to be used in several engineering and safety applications both indoors and outdoors, e.g., exploration, rescue, sport, entertainment, and convenience. Among those applications, it is important to make a drone capable of flying autonomously to carry out an inspection patrol. In this paper, we present a novel method that uses ArUco markers as a reference to improve the accuracy of a drone on autonomous straight take-off, flying forward, and landing based on Deep Reinforcement Learning (DRL). More specifically, the drone first detects a specific marker with one of its onboard cameras. Then it calculates the position and orientation relative to the marker so as to adjust its actions for achieving better accuracy with a DRL method. We perform several simulation experiments with different settings, i.e., different sets of states, different sets of actions and even different DRL methods, by using the Robot Operating System (ROS) and its Gazebo simulator. Simulation results show that our proposed methods can efficiently improve the accuracy of the considered actions.https://www.atlantis-press.com/article/125941515/viewDronesDeep reinforcement learningQ-learningAutonomous flight |
| spellingShingle | Che-Cheng Chang Jichiang Tsai Peng-Chen Lu Chuan-An Lai Accuracy Improvement of Autonomous Straight Take-off, Flying Forward, and Landing of a Drone with Deep Reinforcement Learning International Journal of Computational Intelligence Systems Drones Deep reinforcement learning Q-learning Autonomous flight |
| title | Accuracy Improvement of Autonomous Straight Take-off, Flying Forward, and Landing of a Drone with Deep Reinforcement Learning |
| title_full | Accuracy Improvement of Autonomous Straight Take-off, Flying Forward, and Landing of a Drone with Deep Reinforcement Learning |
| title_fullStr | Accuracy Improvement of Autonomous Straight Take-off, Flying Forward, and Landing of a Drone with Deep Reinforcement Learning |
| title_full_unstemmed | Accuracy Improvement of Autonomous Straight Take-off, Flying Forward, and Landing of a Drone with Deep Reinforcement Learning |
| title_short | Accuracy Improvement of Autonomous Straight Take-off, Flying Forward, and Landing of a Drone with Deep Reinforcement Learning |
| title_sort | accuracy improvement of autonomous straight take off flying forward and landing of a drone with deep reinforcement learning |
| topic | Drones Deep reinforcement learning Q-learning Autonomous flight |
| url | https://www.atlantis-press.com/article/125941515/view |
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