Avoiding contingent incidents by quadrotors due to one or two propellers failure

Altınuç, Kemal Orçun; Khan, Muhammad Umer; Iqbal, Jamshed

doi:10.1371/journal.pone.0282055

Cited by 2 publications

(3 citation statements)

References 29 publications

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“…The higher the number of propellors are, the more stable the flight it takes. A quite recent and notable work upon the stability of the rotor-based UAV was carried by [9], in which a control architecture was designed and implemented for a quadrotor losing its propellors and still being able to maneuver itself within the stable region.…”

Section: Environmental Monitoring Through Uavsmentioning

confidence: 99%

“…This is opposed to the maximum yaw thrust published in the algorithm, which is up to 180˚, both in positive and negative value. This opens another problem to either optimize the generated path for this specific simulator or introduce 10,6], [9,7], [8,8], [8,9], [8,10], [7,11], [7,12], [7,13], [7,14], [8,15]]…”

Section: The Reasoning Behind Failed Scenariosmentioning

confidence: 99%

“…[ [9,7], [8,8], [8,9], [8,10], [7,11], [7,12], [7,13], [7,14], [8,15]] 15], [4,15], [5,15], [6,15], [7,14], [8,15]]…”

Section: The Reasoning Behind Failed Scenariosunclassified

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Gas concentration mapping and source localization for environmental monitoring through unmanned aerial systems using model-free reinforcement learning agents

Husnain,

Mokhtar,

Mohamed Shah

et al. 2024

PLoS ONE

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There are three primary objectives of this work; first: to establish a gas concentration map; second: to estimate the point of emission of the gas; and third: to generate a path from any location to the point of emission for UAVs or UGVs. A mountable array of MOX sensors was developed so that the angles and distances among the sensors, alongside sensors data, were utilized to identify the influx of gas plumes. Gas dispersion experiments under indoor conditions were conducted to train machine learning algorithms to collect data at numerous locations and angles. Taguchi’s orthogonal arrays for experiment design were used to identify the gas dispersion locations. For the second objective, the data collected after pre-processing was used to train an off-policy, model-free reinforcement learning agent with a Q-learning policy. After finishing the training from the training data set, Q-learning produces a table called the Q-table. The Q-table contains state-action pairs that generate an autonomous path from any point to the source from the testing dataset. The entire process is carried out in an obstacle-free environment, and the whole scheme is designed to be conducted in three modes: search, track, and localize. The hyperparameter combinations of the RL agent were evaluated through trial-and-error technique and it was found that ε = 0.9, γ = 0.9 and α = 0.9 was the fastest path generating combination that took 1258.88 seconds for training and 6.2 milliseconds for path generation. Out of 31 unseen scenarios, the trained RL agent generated successful paths for all the 31 scenarios, however, the UAV was able to reach successfully on the gas source in 23 scenarios, producing a success rate of 74.19%. The results paved the way for using reinforcement learning techniques to be used as autonomous path generation of unmanned systems alongside the need to explore and improve the accuracy of the reported results as future works.

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Section: Environmental Monitoring Through Uavsmentioning

confidence: 99%

Section: The Reasoning Behind Failed Scenariosmentioning

confidence: 99%

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Gas concentration mapping and source localization for environmental monitoring through unmanned aerial systems using model-free reinforcement learning agents

Husnain,

Mokhtar,

Mohamed Shah

et al. 2024

PLoS ONE

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An Overview of Drone Applications in the Construction Industry

Choi

Kim

et al. 2023

Drones

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The integration of drones in the construction industry has ushered in a new era of efficiency, accuracy, and safety throughout the various phases of construction projects. This paper presents a comprehensive overview of the applications of drones in the construction industry, focusing on their utilization in the design, construction, and maintenance phases. The differences between the three different types of drones are discussed at the beginning of the paper where the overview of the drone applications in construction industry is then described. Overall, the integration of drones in the construction industry has yielded transformative advancements across all phases of construction projects. As technology continues to advance, drones are expected to play an increasingly critical role in shaping the future of the construction industry.

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Avoiding contingent incidents by quadrotors due to one or two propellers failure

Cited by 2 publications

References 29 publications

Gas concentration mapping and source localization for environmental monitoring through unmanned aerial systems using model-free reinforcement learning agents

Gas concentration mapping and source localization for environmental monitoring through unmanned aerial systems using model-free reinforcement learning agents

An Overview of Drone Applications in the Construction Industry

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