Identification and Classification of Drop Zones and Helicopter Landing Zones in Images Obtained by Small Size Remotely Piloted Aircraft Systems

Lacerda, M. G.; Paulino, Angelo de Carvalho; Shiguemori, Elcio Hideiti; Damião, Álvaro José; Guimarães, Lamartine Nogueira Frutuoso; Anjos, Camila Souza dos

doi:10.1109/igarss.2018.8517753

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…In recent years, data fusion has led to techniques that combine both camera and laser scanner data to provide a more accurate representation of a landing area [Natividade et al, 2017,Singh et al, 2016, Lacerda et al, 2018. For instance, [Johnson and Ivanov, 2011] uses flash lidar in combination with known terrain data derived from imagery to estimate position within 90 meters for lunar landings.…”

Section: Landing Zone Selection and Verificationmentioning

confidence: 99%

Autonomous, Long-Range, Sensor Emplacement Using Unmanned Aircraft Systems

Plowcha¹,

Bradley²,

Hogberg³

et al. 2022

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Automated, in-ground sensor emplacement can significantly improve remote, terrestrial, data collection capabilities. Utilizing a multicopter, unmanned aircraft system (UAS) for this purpose allows sensor insertion with minimal disturbance to the target site or surrounding area. However, developing an emplacement mechanism for a small multicopter, autonomy to manage the target selection and implantation process, as well as long-range deployment are challenging to address. We have developed an autonomous, multicopter UAS that can implant subsurface sensor devices. We enhanced the UAS autopilot with autonomy for target and landing zone selection, as well as ensuring the sensor is implanted properly in the ground. The multicopter UAS, limited by onboard energy, can be carried by a transport aircraft to within 1 km of the desired sensor location site and deployed by a novel parachuting-canister system. Through a comprehensive set of field trials and testing, we assess the effectiveness of each subsystem. We evaluate our system on missions covering distances up to 25 km away in mountains 1 km above the takeoff location.

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Section: Landing Zone Selection and Verificationmentioning

confidence: 99%

Autonomous, Long-Range, Sensor Emplacement Using Unmanned Aircraft Systems

Plowcha¹,

Bradley²,

Hogberg³

et al. 2022

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“…In addition, the use of these types of algorithms as predictors is well extended and validated in the research literature [13] - [16].…”

Section: Incmentioning

confidence: 99%

A Framework to Predict Failures for Ground Tests on Aircrafts

Monedero

Biscarri

Larios

et al. 2019

IEEE Aerosp. Electron. Syst. Mag.

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“…Regarding the civilian scope of aerospace robotics employment, there can be mentioned topographic surveys, mapping of areas of interest, precision agriculture, surveillance, and access control, remote sensing for geological and climatic research and various utilities [2], [3], [8], [12]. State-of-the-art applications include detection and monitoring of sea wildlife [13], thermographic inspection of photovoltaic plants [15], radiation detection [14] and identification of helicopter landing zones and airdrop zones in calamity situations [16].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Adaptive Computational Intelligence-based Multisensor Data Fusion applied to real-time UAV autonomous navigation

Paulino

Guimarães

Shiguemori

2019

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Nowadays, there is a remarkable world trend in employing UAVs and drones for diverse applications. The main reasons are that they may cost fractions of manned aircraft and avoid the exposure of human lives to risks. Nevertheless, they depend on positioning systems that may be vulnerable. Therefore, it is necessary to ensure that these systems are as accurate as possible, aiming to improve navigation. In pursuit of this end, conventional Data Fusion techniques can be employed. However, its computational cost may be prohibitive due to the low payload of some UAVs. This paper proposes a low-cost Multisensor Data Fusion application based on Hybrid Adaptive Computational Intelligence (HACI) -the cascaded use of Fuzzy C-Means Clustering (FCM) and Adaptive-Network-Based Fuzzy Inference System (ANFIS) algorithms -that have been shown able to improve considerably the accuracy of current positioning estimation systems for real-time UAV autonomous navigation, reducing the error in approximately 300cm 2 . In addition, the proposed methodology outperformed two other Computational Intelligence methodologies -Artificial Neural Networks and Regression Models, with 18 different tested approaches -in estimating an UAV position considering the Root-Mean-Square Error against the real trajectory. The generated Fuzzy Inference System has proved to be effective in providing an improved positioning estimation with a low computational burden, about 600 times faster than the fastest embedded sensor refresh rate.

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Identification and Classification of Drop Zones and Helicopter Landing Zones in Images Obtained by Small Size Remotely Piloted Aircraft Systems

Cited by 5 publications

References 11 publications

Autonomous, Long-Range, Sensor Emplacement Using Unmanned Aircraft Systems

Autonomous, Long-Range, Sensor Emplacement Using Unmanned Aircraft Systems

A Framework to Predict Failures for Ground Tests on Aircrafts

Hybrid Adaptive Computational Intelligence-based Multisensor Data Fusion applied to real-time UAV autonomous navigation

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