Design and Experimental Evaluation of an Odor Sensing Method for a Pocket-Sized Quadcopter

Shigaki, Shunsuke; Fikri, Muhamad Rausyan; Kurabayashi, Daisuke

doi:10.3390/s18113720

Cited by 41 publications

(33 citation statements)

References 55 publications

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“…1 The authors are with the Distributed Intelligent Systems and Algorithms Laboratory, School of Architecture, Civil and Environmental Engineering,École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland effect on the plume of the gas, making its detection a challenging task [4], [5], [6].In order to avoid the effect of the wake, Neumann et al [7] proposed a robotic platform for aerial sensing that uses remote gas sensors. However, the start-stop strategy for gas detection proposed in this paper does not allow continuous detection and drains the battery quickly.…”

Section: Introductionmentioning

confidence: 99%

3D Odor Source Localization using a Micro Aerial Vehicle: System Design and Performance Evaluation

Ercolani

Martinoli

2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Finding chemical compounds in the air has applications when situations such as gas leaks, environmental emergencies and toxic chemical dispersion occur. Enabling robots to undertake this task would provide a powerful tool to prevent dangerous situations and assist humans when emergencies arise. While the dispersion of chemical compounds in the air is intrinsically a three-dimensional (3D) phenomenon, the scientific community tackled primarily two-dimensional (2D) scenarios so far. This is mainly due to the challenges of developing a platform able to successfully provide chemical compounds samples of a 3D space. In this paper, a 3D bioinspired algorithm for odor source localization, previously validated in a controlled physical environment leveraging a robotic manipulator, is adapted for deployment on a micro aerial vehicle equipped with an odor sensor. Given the effect that the propellers have on a gas distribution, the algorithmic adaptation focused on enhancing the sensing strategy of the platform. Additionally, two sensor placement configurations are assessed to determine which one yields best sensing results. A performance evaluation in different environmental scenarios is carried out to test the robustness of the implementation. Two different localization systems are used for the performance evaluation experiments to quantify the impact of localization accuracy on the algorithm outcome.

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Section: Introductionmentioning

confidence: 99%

3D Odor Source Localization using a Micro Aerial Vehicle: System Design and Performance Evaluation

Ercolani

Martinoli

2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…The future research that will be based on the developed framework could assist development of better navigating strategies for autonomous drones or underwater vehicles, e.g. [31] or analysing sophisticated navigation strategies such as infotaxis [28], [27].…”

Section: Summary and Discussionmentioning

confidence: 99%

“…In order to evaluate the feasibility, accuracy and readiness of the current odor-based navigational models, there is a need for a framework that will enable an assessment of the variability of the proposed models. A unified framework and a computer simulation for quantitative comparison of the above mentioned models and others, similar to those proposed in autonomous navigation studies [1, 29–31] is required.…”

Section: Introductionmentioning

confidence: 99%

Open source computational simulation for a moth-inspired navigation algorithm

Benelli

Gurka

Golov

et al. 2019

Preprint

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Olfactory navigation in insects, for instance when males search for mates, is a navigational problem of a self-propelled agent with limited sensor capabilities in a scalar field (odor) convected and diffused by turbulent wind. There are numerous navigation strategies proposed to explain the navigation paths of insects to food (flowers) or mating partners (females). In a search for a mate, the males use airborne pheromone puffs in turbulent environments around trees and vegetation. It is difficult to compare the various strategies because of a lack of a single simulation framework that can change a single parameter in time and test all the strategies against a controlled environment. This work aims at closing this gap, suggesting an open source, freely accessible simulation framework, abbreviated MothPy. We implement the simulation framework using another open source package ("pompy") that recreates a state-of-the-art puff-based odor plume model of Farrell et al. [1]. We add four different navigation strategies to the simulation framework based on and extending the previously published models [2, 3], and compare their performance with different wind and odor spread parameters. We test a sensitivity analysis of the navigation strategies to the plume meandering and to increased turbulence levels that are effectively expressed as the elevated puff spread rates. The simulations are compared statistically and provide an interesting view on the robustness and effectiveness of various strategies. This benchmarking-ready simulation framework could be useful for the biology-oriented, as well as engineering-oriented studies, assisting to deduce the evolutionary efficient strategies and improving self-propelled autonomous systems in complex environments. PLOS1/13 using chemo-receptors on their antennae [8-10] for chemical sensing [11, 12] whilst using 7 visual optometry for the spatial orientation, using so-called optomotor anemotaxis. The 8 navigational paths of moths were mostly observed to perform relative narrow 9 zigzagging [13-15] motion and wider side-slips, sometimes called "casting" or "sweeping" 10 motion, respectively. A large variety of models were proposed to explain their navigation 11 strategy, using an internal counter [16][17][18][19] or a different set of assumptions [13,[20][21][22][23][24]. 12In addition, there are probabilistic types of navigation models that use the olfactory 13 signal with or without prior information or memory assumptions, e.g. [2, 6, 23,[25][26][27][28][29][30]. In 14 order to evaluate the feasibility, accuracy and readiness of the current odor-based 15 navigational models, there is a need for a framework that will enable an assessment of 16 the variability of the proposed models. A unified framework and a computer simulation 17 for quantitative comparison of the above mentioned models and others, similar to those 18 proposed in autonomous navigation studies [1,[29][30][31] is required. 19 Recently, Macedo et al. [32] reported about a simulator and comparison of several 20 bio-inspi...

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“…The advantage of using an insect antenna for robotic odour detection is its high temporal resolution owing to its short recovery time, along with its high sensitivity [17]. To overcome the slow recovery time of the semiconductor sensors, an algorithm using a system modelling approach of the sensor [18] or the derivative of the sensor output to decompose odour bouts has been developed [19] and implemented in small-sized drones for odour searching tasks [20,21]. Recent studies reported that some semiconductor gas sensors can resolve 2–5 Hz of odour pulses [21,22]; however, these values are still lower than the temporal resolution of insect antennae (more than 100 Hz [23]).…”

Section: Introductionmentioning

confidence: 99%

“…To overcome the slow recovery time of the semiconductor sensors, an algorithm using a system modelling approach of the sensor [18] or the derivative of the sensor output to decompose odour bouts has been developed [19] and implemented in small-sized drones for odour searching tasks [20,21]. Recent studies reported that some semiconductor gas sensors can resolve 2–5 Hz of odour pulses [21,22]; however, these values are still lower than the temporal resolution of insect antennae (more than 100 Hz [23]). The problem of using the EAG for robotic odour searching is that the measurement is strongly influenced by electrical and mechanical noises caused by robot movement, and the attenuation of odour signals because the antennal tissue dries and dies if the antenna is isolated.…”

Section: Introductionmentioning

confidence: 99%

Dropping Counter: A Detection Algorithm for Identifying Odour-Evoked Responses from Noisy Electroantennograms Measured by a Flying Robot

Lan

Kanzaki

Ando

2019

Sensors

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The electroantennogram (EAG) is a technique used for measuring electrical signals from the antenna of an insect. Its rapid response time, quick recovery speed, and high sensitivity make it suitable for odour-tracking tasks employing mobile robots. However, its application to flying robots has not been extensively studied owing to the electrical and mechanical noises generated. In this study, we investigated the characteristics of the EAG mounted on a tethered flying quadcopter and developed a special counter-based algorithm for detecting the odour-generated responses. As the EAG response is negative, the algorithm creates a window and compares the values inside it. Once a value is smaller than the first one, the counter will increase by one and finally turns the whole signal into a clearer odour stimulated result. By experimental evaluation, the new algorithm gives a higher cross-correlation coefficient when compared with the fixed-threshold method. The result shows that the accuracy of this novel algorithm for recognising odour-evoked EAG signals from noise exceeds that of the traditional method; furthermore, the use of insect antennae as odour sensors for flying robots is demonstrated to be feasible.

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Design and Experimental Evaluation of an Odor Sensing Method for a Pocket-Sized Quadcopter

Cited by 41 publications

References 55 publications

3D Odor Source Localization using a Micro Aerial Vehicle: System Design and Performance Evaluation

3D Odor Source Localization using a Micro Aerial Vehicle: System Design and Performance Evaluation

Open source computational simulation for a moth-inspired navigation algorithm

Dropping Counter: A Detection Algorithm for Identifying Odour-Evoked Responses from Noisy Electroantennograms Measured by a Flying Robot

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