Power electrical drive Power-Hardware-in-the-Loop system : 2018 X International Conference on Electrical Power Drive Systems (ICEPDS)

Mudrov, Mikhail; Ziuzev, Anatolii; Нестеров, К. Е.; Valtchev, Stanimir

doi:10.1109/icepds.2018.8571801

Cited by 4 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, there is a limitation that the UAV cannot maneuver normally in situations beyond the established rules [3][4][5]. Therefore, recent autonomous UAV flight research has used reinforcement learning based on artificial neural networks for robust flights in external environments [6][7][8]. In the case of autonomous UAV flight using reinforcement learning, real aerodynamics based on simulated environments can be implemented, and the UAV can fly based on decision-making processes similar to those made by a pilot.…”

Section: Introductionmentioning

confidence: 99%

Modular Reinforcement Learning for Autonomous UAV Flight Control

Choi

Kim

Hwang

et al. 2023

Drones

View full text Add to dashboard Cite

Recently, research on unmanned aerial vehicles (UAVs) has increased significantly. UAVs do not require pilots for operation, and UAVs must possess autonomous flight capabilities to ensure that they can be controlled without a human pilot on the ground. Previous studies have mainly focused on rule-based methods, which require specialized personnel to create rules. Reinforcement learning has been applied to research on UAV autonomous flight; however, it does not include six-degree-of-freedom (6-DOF) environments and lacks realistic application, resulting in difficulties in performing complex tasks. This study proposes a method of efficient learning by connecting two different maneuvering methods using modular learning for autonomous UAV flights. The proposed method divides complex tasks into simpler tasks, learns them individually, and then connects them in order to achieve faster learning by transferring information from one module to another. Additionally, the curriculum learning concept was applied, and the difficulty level of individual tasks was gradually increased, which strengthened the learning stability. In conclusion, modular learning and curriculum learning methods were used to demonstrate that UAVs can effectively perform complex tasks in a realistic, 6-DOF environment.

show abstract