Al-Radaideh, A., Selje, R. A., Coraspe, D., Camci, E., Dutta, R., Sun, L., Jayavelu, S., & Li, X. (2023). Tethered Multicopter Guidance in GPS-Denied Environments Through Reinforcement Learning. AIAA SCITECH 2023 Forum. https://doi.org/10.2514/6.2023-0507
Abstract:
This paper presents a novel reinforcement learning (RL) approach for a tethered drone to follow a predefined three-dimensional trajectory in a GPS-denied environment. The adopted Q-learning strategy determines high-level actions using raw observations from the onboard accelerometers, gyros, and altimeter, which facilitates a low-level proportional-integral-derivative (PID) controller to drive the drone through the desired waypoints on a reference trajectory. The effectiveness of the proposed approach is demonstrated in a simulated environment.
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Publisher Copyright
Funding Info:
There was no specific funding for the research done