An Aero-olfactory-Effect Elimination Algorithm for Rotor UAV based Gas Distribution Mapping

Jing, Tao; Wang, Jiaying; Meng, Qing-Hao

doi:10.23919/ccc50068.2020.9188643

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…This prevented each robot from transiting above or below another agent and improved the flying stability of the whole system. The work reported in [38] proposed an algorithm to account for the sidewash and downwash effects that the propellers of a quadrotor have on a gas plume dispersion. The paper presented an algorithm that used a wake model to estimate the real position of the detected gas particles and eliminated the effect of the sidewash and downwash, accordingly.…”

Section: Robotic Gas Sensing and Propeller Interferencementioning

confidence: 99%

3D Gas Sensing with Multiple Nano Aerial Vehicles: Interference Analysis, Algorithms and Experimental Validation

Ercolani,

Jin,

Martinoli

2023

Sensors

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Within the scope of the ongoing efforts to fight climate change, the application of multi-robot systems to environmental mapping and monitoring missions is a prominent approach aimed at increasing exploration efficiency. However, the application of such systems to gas sensing missions has yet to be extensively explored and presents some unique challenges, mainly due to the hard-to-sense and expensive-to-model nature of gas dispersion. For this paper, we explored the application of a multi-robot system composed of rotary-winged nano aerial vehicles to a gas sensing mission. We qualitatively and quantitatively analyzed the interference between different robots and the effect on their sensing performance. We then assessed this effect, by deploying several algorithms for 3D gas sensing with increasing levels of coordination in a state-of-the-art wind tunnel facility. The results show that multi-robot gas sensing missions can be robust against documented interference and degradation in their sensing performance. We additionally highlight the competitiveness of multi-robot strategies in gas source location performance with tight mission time constraints.

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Section: Robotic Gas Sensing and Propeller Interferencementioning

confidence: 99%

3D Gas Sensing with Multiple Nano Aerial Vehicles: Interference Analysis, Algorithms and Experimental Validation

Ercolani,

Jin,

Martinoli

2023

Sensors

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“…The optimal location of the sensors requires a study of the airflows produced by the rotors and the analysis of the quad-rotor aerodynamics [17,41] and the hexa-rotor aerodynamics [40]. Such studies, performed using simulations [17,[42][43][44][45][46] and experiments in the real environment [17,40,41,[45][46][47][48][49], showed that the calmest place, with the weakest airflow, is the middle part of the UAV. This applies to both small, several-hundred-gram UAVs [17], to medium-sized ones [41,42,45,47,48] and large, several-kilogram ones [40] as well.…”

Section: Location and Calibration Of The Gas Sensormentioning

confidence: 99%

The Prototype Monitoring System for Pollution Sensing and Online Visualization with the Use of a UAV and a WebRTC-Based Platform

Chodorek

Yastrebov

2022

Sensors

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Nowadays, we observe a great interest in air pollution, including exhaust fumes. This interest is manifested in both the development of technologies enabling the limiting of the emission of harmful gases and the development of measures to detect excessive emissions. The latter includes IoT systems, the spread of which has become possible thanks to the use of low-cost sensors. This paper presents the development and field testing of a prototype pollution monitoring system, allowing for both online and off-line analyses of environmental parameters. The system was built on a UAV and WebRTC-based platform, which was the subject of our previous paper. The platform was retrofitted with a set of low-cost environmental sensors, including a gas sensor able to measure the concentration of exhaust fumes. Data coming from sensors, video metadata captured from 4K camera, and spatiotemporal metadata are put in one situational context, which is transmitted to the ground. Data and metadata are received by the ground station, processed (if needed), and visualized on a dashboard retrieving situational context. Field studies carried out in a parking lot show that our system provides the monitoring operator with sufficient situational awareness to easily detect exhaust emissions online, and delivers enough information to enable easy detection during offline analyses as well.

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Effects of Rotor-Rotor Interaction on the Wake Structure and Thrust Generation of a Quadrotor Unmanned Aerial Vehicle

et al. 2021

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An Aero-olfactory-Effect Elimination Algorithm for Rotor UAV based Gas Distribution Mapping

Cited by 3 publications

References 17 publications

3D Gas Sensing with Multiple Nano Aerial Vehicles: Interference Analysis, Algorithms and Experimental Validation

3D Gas Sensing with Multiple Nano Aerial Vehicles: Interference Analysis, Algorithms and Experimental Validation

The Prototype Monitoring System for Pollution Sensing and Online Visualization with the Use of a UAV and a WebRTC-Based Platform

Effects of Rotor-Rotor Interaction on the Wake Structure and Thrust Generation of a Quadrotor Unmanned Aerial Vehicle

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