CVaR-based Flight Energy Risk Assessment for Multirotor UAVs using a Deep Energy Model

Choudhry, Arnav; Moon, Brady; Patrikar, Jay; Samaras, Constantine; Scherer, Sebastian

doi:10.1109/icra48506.2021.9561658

Cited by 21 publications

(12 citation statements)

References 25 publications

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“…It was observed that the LR-TCN approach proposed in this research outperformed other DNN methods that were tested, as well as earlier studies with similar objectives. 14,20,21 Furthermore, the study yielded a collection of findings that may be concisely described as follows.…”

Section: Discussionmentioning

confidence: 99%

“…The rapid increase in the use of multi‐rotor unmanned aerial vehicles (UAVs) in industry, entertainment, defense industry and transportation has caused researchers to conduct extensive researches on this topic. When these studies in the UAV literature were examined, it was observed that they studies were gathered under three main topics; battery life prediction, 1,2 anomaly detection 3‐14 and instantaneous power consumption prediction 15‐21 …”

Section: Introductionmentioning

confidence: 99%

“…When these studies in the UAV literature were examined, it was observed that they studies were gathered under three main topics; battery life prediction, 1,2 anomaly detection 3-14 and instantaneous power consumption prediction. [15][16][17][18][19][20][21] The battery life prediction using machine learning is of great importance for the UAVs to fulfill their duties as determined by the moderator and to avoid any problems during the flight. In 2017, Mansouri et al examined flights under different conditions and performed the prediction of the remaining useful life (RUL) of Li-on batteries.…”

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confidence: 99%

“…In recent years, researchers have conducted numerous studies on this topic for this reason. [15][16][17][18][19][20][21] In 2018, the effects of payload, communication, environmental parameters (wind, air density, etc. ), and movement parameters (direction, speed, angle, etc.)…”

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confidence: 99%

“…17 As a result of experimental studies using the TCN network, it has been shown that the proposed method is successful for power consumption prediction compared to recent studies. 20 After the 2020s, researchers focused on developing algorithms and smart power supply systems to ensure energy consumption efficiency. 18,19 Recently, the use of TCN for UAV power consumption prediction has increased rapidly due to its high prediction performance, as seen in the above studies.…”

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confidence: 99%

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UAV instantaneous power consumption prediction using LR‐TCN with simple moving average

Dudukcu,

Taskiran,

Kahraman

2023

Concurrency and Computation

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SummaryThe usage of multi‐rotor unmanned aerial vehicles (UAVs) has rapidly increased in a variety of industries recently, which has caused a quick rise in the quantity of research works on the topic. Remaining battery life prediction, anomaly detection and instantaneous power consumption prediction are among the topics that attract the most attention of researchers. This article presents the development and utilization of a modified temporal convolutional network (TCN) model, a commonly employed approach for anomaly detection and instantaneous power consumption prediction in unmanned aerial vehicles (UAVs). The first modification to the TCN model was to use the Leaky ReLu activation function in place of the rectified linear unit (ReLu) activation function from the original TCN model. In the next step, instantaneous power consumption prediction for UAVs was performed by using the data obtained from both sensors and simple moving average (SMA) algorithm. As a result of the tests performed with the created simulation setup, it has been clearly shown that the proposed method gives better results compared to other deep learning models used for comparison with the lowest RMSE of 0.0496.

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Section: Discussionmentioning

confidence: 99%