Accuracy Improvement of Autonomous Straight Take-off, Flying Forward, and Landing of a Drone with Deep Reinforcement Learning
| Title: | Accuracy Improvement of Autonomous Straight Take-off, Flying Forward, and Landing of a Drone with Deep Reinforcement Learning |
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| Authors: | Che-Cheng Chang, Jichiang Tsai, Peng-Chen Lu, Chuan-An Lai |
| Source: | International Journal of Computational Intelligence Systems, Vol 13, Iss 1 (2020) |
| Publisher Information: | Springer, 2020. |
| Publication Year: | 2020 |
| Collection: | LCC:Electronic computers. Computer science |
| Subject Terms: | Drones, Deep reinforcement learning, Q-learning, Autonomous flight, Electronic computers. Computer science, QA75.5-76.95 |
| More Details: | 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1875-6883 14746328 |
| Relation: | https://www.atlantis-press.com/article/125941515/view; https://doaj.org/toc/1875-6883 |
| DOI: | 10.2991/ijcis.d.200615.002 |
| Access URL: | https://doaj.org/article/b02903beb14746328ff0216dc2ce43d4 |
| Accession Number: | edsdoj.b02903beb14746328ff0216dc2ce43d4 |
| Database: | Directory of Open Access Journals |
| ISSN: | 18756883 14746328 |
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| DOI: | 10.2991/ijcis.d.200615.002 |
| Published in: | International Journal of Computational Intelligence Systems |
| Language: | English |