The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Reinforcement Learning for UAV Attitude Control
Open accessAbstract
Autopilot systems are typically composed of an “inner loop” providing stability and control, whereas an “outer loop” is responsible for mission-level objectives, such as way-point navigation. Autopilot systems for unmanned aerial vehicles are predominately implemented using Proportional-Integral-Derivative (PID) control systems, which have demonstrated exceptional performance in stable environments. However, more sophisticated control is required to operate in unpredictable and harsh environments. Intelligent flight control systems is an active area of research addressing limitations of PID control most recently through the use of reinforcement learning (RL), which has had success in other applications, such as robotics. Yet previous work has focused primarily on using RL at the mission-level controller. In this work, we investigate the performance and accuracy of the inner control loop providing attitude control when using intelligent flight control systems trained with state-of-the-art RL algorithms—Deep Deterministic Policy Gradient, Trust Region Policy Optimization, and Proximal Policy Optimization. To investigate these unknowns, we first developed an open source high-fidelity simulation environment to train a flight controller attitude control of a quadrotor through RL. We then used our environment to compare their performance to that of a PID controller to identify if using RL is appropriate in high-precision, time-critical flight control.
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Cites
Reinforcement Learning: An Introduction
(2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 24,496
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782
Proximal Policy Optimization Algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017) · arXiv · doi:10.48550/arxiv.1707.06347 · Cited by 10,625
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471
Optimum Settings for Automatic Controllers
Ziegler, J. G., & Nichols, N. B. (1942) · Journal of Fluids Engineering · doi:10.1115/1.4019264 · Cited by 5,212
Design and use paradigms for gazebo, an open-source multi-robot simulator
Koenig, N., & Howard, A. (2005) · 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566) · doi:10.1109/iros.2004.1389727 · Cited by 3,638
Optimum Settings for Automatic Controllers
Ziegler, J. G., & Nichols, N. B. (1993) · Journal of Dynamic Systems Measurement and Control · doi:10.1115/1.2899060 · Cited by 2,460
Survey of gain-scheduling analysis and design
Leith, D. J., & Leithead, W. E. (2000) · International Journal of Control · doi:10.1080/002071700411304 · Cited by 939
Feedback linearization vs. adaptive sliding mode control for a quadrotor helicopter
Lee, D., Jin Kim, H., & Sastry, S. (2009) · International Journal of Control, Automation and Systems · doi:10.1007/s12555-009-0311-8 · Cited by 678
Noise and the reality gap: The use of simulation in evolutionary robotics
Jakobi, N., Husbands, P., & Harvey, I. (1995) · Lecture Notes in Computer Science · doi:10.1007/3-540-59496-5_337 · Cited by 631
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Drone Deep Reinforcement Learning: A Review
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Motion Planning for Mobile Robots—Focusing on Deep Reinforcement Learning: A Systematic Review
Sun, H., Zhang, W., Yu, R., & Zhang, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3076530 · Cited by 143
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Employing Intelligent Aerial Data Aggregators for the Internet of Things: Challenges and Solutions
Li, K., Ni, W., Noor, A., & Guizani, M. (2022) · IEEE Internet of Things Magazine · doi:10.1109/iotm.001.2100161 · Cited by 11
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“Additionally, streamlining standardization and certification processes will expedite the adoption of BVLOS technologies, fostering innovation and ensuring regulatory compliance.”
Metadata
- Authors
- Koch, W., Mancuso, R., West, R., & Bestavros, A.
- Venue
- ACM Transactions on Cyber-Physical Systems
- Published
- 2019-02-13
- Volume
- 3
- Issue
- 2
- Pages
- 1-21
- ISSN
- 2378-962X (ISSN-L)
- 2378-9638
- OpenAlex
- W2797028887
- Cited by
- 499
- References
- 15
- FWCI
- 26.36
- Type
- journal article
- Language
- en
- Open access
- bronze
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