A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Platform

Jiang, Zhiling; Song, Guanghua

doi:10.1109/icrss57469.2022.00031

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…In [58], three different RL algorithms, DDPG, TD3, and SAC, were applied to study multirotor landing. Using the DDPG method did not result in successful landings.…”

Section: Actor-critic Algorithmsmentioning

confidence: 99%

“…Thus, applications of actor-critic RL as applied to UAV control have been summarized in this subsection. Studies like [58] tested algorithms like DDPG, TD3, and SAC for multirotor landing, with SAC and TD3 performing well. Rubi et al [47,55] examined quadrotor path following, while Wang et al [45] focused on UAV navigation in complex environments, favoring Fast-RDPG.…”

Section: Actor-critic Algorithmsmentioning

confidence: 99%

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A Survey of Offline- and Online-Learning-Based Algorithms for Multirotor Uavs

Sönmez,

Rutherford,

Valavanis

2024

Drones

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Multirotor UAVs are used for a wide spectrum of civilian and public domain applications. Their navigation controllers include onboard sensor suites that facilitate safe, autonomous or semi-autonomous multirotor flight, operation, and functionality under nominal and detrimental conditions and external disturbances, even when flying in uncertain and dynamically changing environments. During the last decade, given the available computational power, different learning-based algorithms have been derived, implemented, and tested to navigate and control, among other systems, multirotor UAVs. Learning algorithms have been and are used to derive data-driven based models, to identify parameters, to track objects, to develop navigation controllers, and to learn the environments in which multirotors operate. Learning algorithms combined with model-based control techniques have proven beneficial when applied to multirotors. This survey summarizes the research published since 2015, dividing algorithms, techniques, and methodologies into offline and online learning categories and then further classifying them into machine learning, deep learning, and reinforcement learning sub-categories. An integral part and focus of this survey is on online learning algorithms as applied to multirotors, with the aim to register the type of learning techniques that are either hard or almost hard real-time implementable, as well as to understand what information is learned, why, how, and how fast. The outcome of the survey offers a clear understanding of the recent state of the art and of the type and kind of learning-based algorithms that may be implemented, tested, and executed in real time.

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“…In [58], three different RL algorithms, DDPG, TD3, and SAC, were applied to study multirotor landing. Using the DDPG method did not result in successful landings.…”

Section: Actor-critic Algorithmsmentioning

confidence: 99%