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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2018.1
- 수록면
- 88 - 92 (5page)
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초록· 키워드
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Currently drone industry has become one of the fast growing markets and the technology for unmanned aerial vehicles are expected to continue to develop at a rapid rate. Especially small unmanned aerial vehicle systems have been designed and utilized for the various field with their own specific purposes. In these fields the path planning problem to find the shortest path between two oriented points is important. In this paper we introduce a path planning strategy for an autonomous flight of unmanned aerial vehicles through reinforcement learning with self-positioning technique. We perform Q-learning algorithm, a kind of reinforcement learning algorithm. At the same time, multi sensors of acceleraion sensor, gyro sensor, and magnetic are used to estimate the position. For the functional evaluation, the proposed method was simulated with virtual UAV environment and visualized the results. The flight history was based on a PX4 based drones system equipped with a smartphone.
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참고문헌 (13)
참고문헌 신청
Borenstein, J. / 2013 / Robots and the Internet: Causes for Concern / IEEE Technology and Society Magazine 32(1):60~65
Weis, A. / Smart drones, Project Report, Tallinn University of Technology
Jinbae Kim, J. / 2017 / Obstacle Avoidance Path Planning For UAV Using Reinforcement Learning Under Simulated Environment / Science and Engineering Letters 3:34~36
Roberge, V. / 2017 / Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning / IEEE Transactions on Industrial Informatics 9(1):132~141
Chen, H. / 2013 / UAV path planning with tangent-plus-Lyapunov vector field guidance and obstacle avoidance / IEEE Transactions on Aerospace and Electronic Systems 49(2):840~856
Weis, A. / Smart drones, Project Report, Tallinn University of Technology
Jinbae Kim, J. / 2017 / Obstacle Avoidance Path Planning For UAV Using Reinforcement Learning Under Simulated Environment / Science and Engineering Letters 3:34~36
Roberge, V. / 2017 / Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning / IEEE Transactions on Industrial Informatics 9(1):132~141
Chen, H. / 2013 / UAV path planning with tangent-plus-Lyapunov vector field guidance and obstacle avoidance / IEEE Transactions on Aerospace and Electronic Systems 49(2):840~856
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