This work was supported in part by a research grant from The Murata Science Foundation. The authors would like to acknowledge Smart Robotics Co., Ltd., Tokyo, Japan, for assisting in the development of the drone platforms and programming and Assist Technology Co., Ltd., Osaka, Japan, for assisting with the design of the electronic circuits. The authors would also like to thank Dr. Shigeru Matsuyama (Graduate School of Life and Environmental Sciences, University of Tsukuba) for providing purified bombykol; Mr. Takuya Nakajo (RCAST, The University of Tokyo) for support for silkmoth breeding; and Mr. Yusuke Notomi (Graduate School of Science and Technology, Tokyo University of Science) for supporting the acquisition of silkmoth images.

1. Insects
NOTE: Eggs of silkmoths (Bombyx mori) were purchased from a domestic company. The silkmoths were used within 10 days after they emerged from cocoons. Prepare three adult silkmoths for the experiments (six antennae); however, this number can be changed depending on the experimental requirements.
<ol>
	<li>Incubate silkmoth eggs at 15 &#176;C for 24 h and move them to an incubator at 25 &#176;C. 
	NOTE: The silkworms hatch approximately 10-13 days later.</li>
	<li>Place the silkworms on sliced artificial diets in a plastic dish.</li>
	<li>After 20-25 days of silkworm raising, observe the formation and pupation of the silkworms within cocoons. 
	&#8203;NOTE: The cultivation procedure includes feeding, removal, and sanitization in an environment at 25 &#176;C. The silkmoths emerge from the cocoons after 10-15 days.</li>
</ol>
2. Odorants and odor source preparation
NOTE: The principal component of the female silkmoth sex pheromone, bombykol ((E,Z)-10,12-hexadecadien-1-ol), was used as an odor source to perform stimulation. A male silkmoth (Figure 1A) can identify and discriminate bombykol27, and isolated silkmoth antennae have been used to act as a biosensor on mobile robots20,21,22. Store purified bombykol dissolved in hexane (10 mg/mL) in a high-sealed storage bottle in a refrigerator at &#8722;30 &#176;C.
<ol>
	<li>Insert a syringe into the high-sealed storage bottle and withdraw and inject 2 mL of 2000 ng/&#181;L bombykol into a 10 mL vial. Then, add 8 mL of hexane to the same vial.</li>
	<li>Dilute 400 ng/&#181;L of bombykol to 2 ng/&#181;L of bombykol with hexane in a 1 mL vial.</li>
	<li>Cut filter paper into 10 mm &#215; 10 mm pieces, roll them into a cylindrical shape, and place them in a glass tube (internal diameter [ID]: 5 mm; outer diameter [OD]: 7 mm; length [L]: 100 mm).</li>
	<li>Drop a diluted sample (100 ng bombykol dissolved in 50 &#181;L of hexane) onto a portion of the filter paper in the glass tube.</li>
	<li>Close both ends of the glass tube with the filter paper using poly-droppers cut in the middle.</li>
</ol>
3. EAG experiments on a fixed desk surface
NOTE: The mountable EAG device, which functions as a portable biosensor on a small drone, is shown in Figure 1B. The device included high-pass (0.1 Hz) and low-pass (300 Hz) filters. The detailed information of the electrical circuit is described in Terutsuki et al.26
<ol>
	<li>Perform data acquisition and analysis on a personal computer (PC) after the EAG device has sent the measurement data.</li>
	<li>To generate purified air, pass the airflow generated by a compact air pump with a cooling fan through cotton, activated carbon granules, and distilled water. Then, pass the purified air through a glass tube for stimulation. 
	NOTE: A photograph of the odor stimulation system is shown in Figure 1C. The airflow path is indicated by black arrows. The airflow path of the exhaust port of the solenoid valve is indicated by the dashed black arrow.</li>
	<li>Set the flow rate as 5 L min-1 using a flowmeter for odor stimulation in the fixed experimental setup. Set a higher flow rate for generation, assuming odor stimulations of several meters for the drone experiments. 
	NOTE: That the flow rate (5 L min-1) did not affect the signal detection of the EAG device had been previously confirmed26. The maximum airflow velocity at the EAG device position during stimulation was measured as 3.9 m s-1 using an anemometer.</li>
	<li>Use a solenoid valve with a microcontroller to stimulate the EAG device and conduct the stimulations automatically.</li>
	<li>Set the stimulation time to 0.5 s using the solenoid valve.</li>
	<li>Use electrically conductive gel to attach a silkmoth antenna to the electrode. 
	NOTE: This procedure does not require the insertion of micrometer-scale wires to both ends of a silkmoth antenna to attach it to the EAG device.
	<ol>
		<li>Isolate silkmoth antennae using postmortem scissors (Figure 2A,B) without anesthesia. See Figure 2C for an enlarged view of the antenna.</li>
		<li>Cut both sides of the isolated silkmoth antenna and attach it to the Ag/AgCl-coated electrodes of the sensing part of the EAG device (Figure 3A) using electrically conductive gel.</li>
	</ol>
	</li>
	<li>Connect the glass tube containing bombykol to the odor stimulation system (ensure that the pump is already switched on).</li>
	<li>Fix the glass tube such that its tip is 10 mm from the silkmoth antenna on the EAG device (Figure 3B).</li>
	<li>Set the exhaust port (diameter of 60 mm) at 30 mm behind the EAG device to stabilize the airflow and prevent pheromone stagnation (Figure 3B).</li>
	<li>Switch on the EAG device. Connect the PC to the Wi-Fi access point.</li>
	<li>Run the data acquisition program on the PC. See Figure 3C for the graphical user interface (GUI) on the PC for the experiments.</li>
	<li>After pressing the Ground button in the Log menu to decide the experimental state, press the Log start button for data acquisition. Five seconds after pressing the Log start button, initiate odor stimulations.</li>
	<li>Press the Log stop button on the GUI to stop recording.</li>
</ol>
4. Drone
NOTE: A commercial drone flight platform (98 mm x 93 mm x 41 mm; weight 87 g; maximum flight time 13 min) was used in this study. The payload of the drone was approximately 30 g based on the experiments. The drone was equipped with a vision positioning system (VPS) consisting of a camera and an infrared sensor under its body, which allowed for stable hovering without an external positioning system.
<ol>
	<li>Remove the top cover of the drone and add a custom carbon fiber-reinforced plastic (CFRP) board using a three-dimensional (3D)-printed mount to attach the EAG device. See Figure 4A for an image of the bio-hybrid drone. 
	NOTE: The drone developer offers a software development kit (SDK) and sample Python programs (see the Table of Materials); therefore, the drone control program for flight experiments was based on these.</li>
	<li>Send flight commands through the PC to control the drone. 
	&#8203;NOTE: For safety, cut-resistant gloves are required for stopping (catching) the drone in an emergency abort. The GUI is equipped with an emergency stop button to immediately stop the rotation of the propellers of the drone (Figure 3C).</li>
</ol>
5. Flight experimental area preparation
<ol>
	<li>Prepare an experimental flight area (5.0 m x 3.2 m x 3.0 m) and equip it with a commercial surveillance camera on the ceiling.</li>
	<li>Set the flow rate of the odor stimulation system as 5 L min-1 and the stimulation time to 0.5 s using the solenoid valve.</li>
</ol>
6. EAG experiments on the drone
<ol>
	<li>Isolate silkmoth antennae using postmortem scissors and cut both sides of the antenna.</li>
	<li>Attach the isolated antennae to the Ag/AgCl-coated electrodes of the sensing part of the EAG device using electrically conductive gel.</li>
	<li>Connect the glass tube containing bombykol (50,000 ng in 250 &#181;L of hexane/filter paper) to the odor stimulation system (with the pump already switched on).</li>
	<li>Set the glass tube so that the tube and its tip are parallel to and directly above the edge of the desk, respectively.</li>
	<li>Set the circulator so that the most protruding part (the center of the fan) is 15 cm from the edge of the desk.</li>
	<li>Set the wind speed of the circulator to 1 (minimum power) by pushing the button on the console.</li>
	<li>Mount the EAG device on the drone. Connect the PC to the Wi-Fi access point. Switch on the EAG device and the drone. 
	NOTE: The switch of the EAG device is in the processing part.</li>
	<li>Run the drone control program on the PC.
	<ol>
		<li>After the light on the drone blinks yellow, press the appropriate button in the Command menu on the GUI (Figure 3C) of the PC to execute the command. 
		NOTE: After the drone is connected to the PC, the light on the drone will turn green.</li>
		<li>Press the Take off button on the GUI to hover the drone above the ground.</li>
		<li>After pressing the Flight button in the Log menu to decide the experimental state, press the Log start button for data acquisition. 
		NOTE: Odor stimulation will be initiated 5 s after pressing the Log start button.</li>
		<li>Press the Log stop button on the GUI to stop recording.</li>
		<li>Send the Stop command in intervals of 5 s after the lift-off of the drone to maintain the hovering state, as the drone automatically lands if not operated for approximately 15 s.</li>
	</ol>
	</li>
</ol>
7. Sensor enclosure
<ol>
	<li>Develop a sensor enclosure (L: 40 mm; ID: 20 mm; OD: 22 mm) based on a carbon fiber tube to enhance the sensor directivity. See Figure 4B,C for an image of the bio-hybrid drone with its sensor enclosure and configuration.</li>
	<li>Cover the sensing part with a heat-shrink insulation tube and fix it to the inner wall of the enclosure using double-sided tape.</li>
	<li>Insert the sensing part of the EAG device into the sensor enclosure.</li>
	<li>Set the distance between the tip of the electrodes and the tip of the enclosure as 10 mm.</li>
</ol>
8. Odor tracing demonstration using the bio-hybrid drone
<ol>
	<li>Isolate silkmoth antennae using postmortem scissors and cut both sides of the antenna.</li>
	<li>Attach the isolated antenna to the Ag/AgCl-coated electrodes of the sensing part of the EAG device using electrically conductive gel.</li>
	<li>Mount the EAG device with the sensor enclosure on the drone.</li>
	<li>Hover the drone so that it begins an approximately 90&#176; pivoting motion to the left and right.</li>
	<li>Stimulate the EAG device on the drone using poly-droppers containing bombykol during these movements.</li>
	<li>Conduct four cycles of step 8.5. 
	&#8203;NOTE: After step 8.6, the drone will rotate clockwise. When conducting the stimulation during this movement, the drone will perform one counterclockwise rotation and land.</li>
</ol>
9. Odor source localization using the bio-hybrid drone
<ol>
	<li>Connect the glass tube containing bombykol (50,000 ng in 250 &#181;L of hexane/filter paper) to the pump that is already switched on.</li>
	<li>Fix the glass tube such that its tip is 150 mm from the circulator.</li>
	<li>Define the direction toward the odor source as 0&#176;, and set the drone at an angle of clockwise 270&#176; from the odor source at the starting point.</li>
	<li>Connect the PC to the Wi-Fi access point, and switch on the EAG device and the drone.</li>
	<li>Run the drone control program on the PC.
	<ol>
		<li>After the light on the drone blinks yellow, press the appropriate button in the Command menu on the GUI of the PC (Figure 3C) to execute the command. 
		NOTE: After the drone is connected to the PC,&#160;the light on the drone will turn green.</li>
		<li>Press the Take off button on the GUI to hover the drone above the ground.</li>
		<li>After pressing the Search button in the Log menu to decide the experimental state, press the Log start button for data acquisition. Then, press the Search start button in the Command menu to initiate odor source localization using the spiral-surge algorithm and cyclic odor stimulations (odor: 0.5 s; interval: 2.0 s) of the odor source.</li>
		<li>After landing the drone, press the Log stop button on the GUI to stop recording.</li>
	</ol>
	</li>
</ol>

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The authors have no conflicts of interest to disclose.

Mobile robots with EAG devices were first developed 25 years ago20. Since then, there have been significant advancements in robotic technologies, including drones. Considering these technological advancements, we developed an autonomous bio-hybrid drone with an EAG device based on a silkmoth antenna for odor detection and localization in air26. This study demonstrates the operation of the developed bio-hybrid drone and the tracing of manual stimulation of odors in a room using the drone.
In this study, as silkmoth antennae were attached to electrodes using electrically conductive gel, we verified that both ends of each antenna made contact with the electrodes securely before beginning EAG experiments on the desk or the drone. If signals from the EAG device were suddenly lost during the experiment, a researcher would first check the connection of the antenna with the electrodes. It is possible that this problem occurred with a higher probability in the EAG experiments on the drone. While the lifespan of isolated silkmoth antennae is more than an hour, because the gel dried out in a dozen to dozens of minutes in this study, the addition of gel to the connecting points of the antennae and the electrodes may help recover signal intensities.
The drone in this study was equipped with the VPS comprising a camera and an infrared sensor for flight stabilization. We found that the drone drifted during hovering on a smooth floor, which may have caused the instability of an infrared sensor under the body of the drone. The same problem sometimes arose when experiments were conducted using this drone in a room with a smooth floor such as tile. Therefore, we covered the floor with raised carpets (we used four-color carpets of 45 cm &#215; 45 cm area) and reduced the drift of the drone. This process was found to be useful for flight stabilization of the EAG experiments on the drone.
The significance of the bio-hybrid drone in this study lies in its ability to recognize odor concentration and its sensor directivity toward odor sources. The drone identified real-time odorant-concentration differences outside a wind tunnel and localized the source using the spiral-surge algorithm (Figure 8). The spiral-surge algorithm29,30 does not require plume-location information during plume reacquisition and exhibits its relatively high reliability, compared to that of the casting algorithm, in a low-speed laminar flow30. This algorithm was previously installed on a ground mobile robot30; however, a wind direction sensor was required to recognize the upwind direction. Odor information was binarized, and concentration was ignored.
For the insect antenna-based drone, mounting additional sensors, such as wind sensors, is a trade-off between payload and battery consumption. In addition, odor information detected by the EAG on the drone was still assessed to determine whether it exceeded a threshold25. The bio-hybrid drone design used in this study enhanced the directivity of the EAG device itself and did not require a wind direction sensor. The sensor directivity enabled the drone to utilize odor concentration information during spiral movements in a room environment that was more complex than a wind tunnel. A cylindrical enclosure was used in this study; however, a more elaborate and lightweight enclosure should be developed in the future.
However, the bio-hybrid drone examined in this study has some limitations. For example, the distance of odor source localization was still limited. Owing to their high mobility, drones should be capable of searching for odors over long distances in the order of several tens of meters. However, the distance achieved by the insect antenna-based bio-hybrid drone was limited to 2 m26, and odor source localization tests were conducted in a wind tunnel with limited space25. Extending the searching distance is essential for the development of a practical odor-detecting flight platform.
For long-distance searches (over 10 m), a high sensor directivity and an efficient odor source localization algorithm are required, given that dilution of the odor concentration and complex distribution of the odor plume are expected. Stereo sensing using two antennae of the same insect can increase directionality23. Most odor source localization experiments using small drones with commercial gas sensors were conducted using a single sensor, and an EAG device array on drones was not conducted. Therefore, an EAG device array must be developed for small drones to increase their odor-sensing-application potential. The EAG device array would also facilitate the development of an efficient odor source localization algorithm as it allows for more precise localization of an odor plume.
Insect antenna-based bio-hybrid odor-detecting drones contribute to both fundamental and applied research. From the perspective of fundamental research, such drones can be used as test platforms to develop odor source localization algorithms. Various algorithms have been previously proposed31; however, test platforms using a mobile robot that conducted two-dimensional odor searches or commercial gas sensors have exhibited limited performance. In these setups, it is difficult for proposed algorithms to demonstrate their performance. The bio-hybrid drone in this study demonstrated odor concentration recognition ability as well as sensor directivity, sensitivity, and selectivity. Therefore, it shows great promise for installation in more advanced or three-dimensional odor source localization algorithms.
In terms of applications, bio-hybrid drones can be deployed on missions that living animals may have difficulty approaching, such as detecting toxic chemical/biological leaks, explosive materials, and search-and-rescue operations. To apply such drones to these missions, the insect antennae need to detect odorant molecules included in target odor sources. Silkmoth antennae can be genetically modified32 to have the potential to detect odorant molecules other than the female silkmoth sex pheromone; thus, these applications are now becoming reality.

With recent advances, small drones with chemical sensing devices have become highly applicable in environmental and security monitoring and gas-leak detection1. Small drones (with a diameter approximately &#60; 20 cm) with commercial metal-oxide-semiconductor (MOX)&#160;gas sensors have been recently applied for performing odor mapping or odor source localization2,3,4. When searching for odor sources, a drone must trace odor plumes; however, odor source localization using small drones presents significant challenges. In an open environment, odor-plume structures are subjected to continual changes due to environmental factors such as the wind or landscape. Hence, drones should be capable of identifying odorant-concentration differences and directions varying over time; however, the odor detection performance of commercial MOX sensors is still inadequate for real-time sensing because of their slow recovery time5.
Bio-hybrid systems formed by the merging of biological and artificial systems are a recent trend in robotics and sensor technologies6, showing great potential for surpassing the capabilities of existing approaches. For instance, a bio-robotic sensor network has been developed based on cockroaches for application in disaster situations7. Experiments have been conducted in which cyborg rats with computationally enhanced intelligence were tasked with solving mazes8. The possibility of social integration of biomimetic robots into groups of real zebra fishes&#160;have been investigated9.
Naturally, this trend has been applied to develop odorant sensors10. For example, biosensors based on insect olfactory systems have relatively high sensitivity and selectivity with respect to various odorant molecules compared to existing MOX sensors11. Along these lines, we had previously developed bio-hybrid odorant biosensor systems based on a combination of insect cells expressing insect odorant receptors and a microscope or electronic devices12,13,14,15,16. Moreover, insect antennae can be independently used as portable odorant sensing parts with high sensitivity, selectivity, reproducibility, and quick response/recovery time, using the electroantennography (EAG) technique17,18,19. Several ground-mobile odor-sensing robots with EAG techniques based on insect antennae20,21,22,23 or small drones with EAG devices24,25 have been developed for odor detection and odor source localization. These robots displayed sensor sensitivity and real-time sensing ability. However, the mobility of ground mobile robots is significantly influenced by land features or obstacles. In addition, the flight performance and odor source localization algorithms of existing EAG-based bio-hybrid drones remain limited because experimental conditions have been limited to tethered flight24 or to being conducted in a small wind tunnel25.
This study presents experimental protocols for odor detection in the air and odor source localization using a recently developed bio-hybrid drone based on silkmoth (Bombyx mori) antennae26. We developed a mountable-sized and lightweight EAG device with a wireless communication function to detect the odor responses of silkmoth antennae. The EAG device was mounted on a small drone, installed in a simple sensor enclosure to enhance the sensor directivity for odorant molecules and reduce noise. The bio-hybrid drone reproducibly detected airborne odorant molecules and identified the maximum odorant concentration during spiral movements. Moreover, the drone localized the odor source using the spiral-surge algorithm without wind-direction information.

<table><tbody><tr><td>Anemometer</td><td>MK Scientific, Kanagawa, Japan</td><td>DT-8880</td><td></td></tr><tr><td>Circulator</td><td>IRIS OHYAMA Inc., Miyagi, Japan</td><td>PCF-SC15T</td><td></td></tr><tr><td>Compact air pump</td><td>AS ONE Corporation, Osaka, Japan</td><td>NUP-1</td><td></td></tr><tr><td>Drone</td><td>Shenzhen Ryze Tech Co., Ltd.</td><td>Tello EDU</td><td>Ryze Tech opens Tello EDU SDK. Our source code is based on SDK 2.0 Use Guide.&lt;br/&gt; https://dl-cdn.ryzerobotics.com/downloads/Tello/Tello%20SDK%202.0%20User%20Guide.pdf&lt;br/&gt; You can download python code (Tello3.py.) and develop flight programs.</td></tr><tr><td>EAG device</td><td>Custom made</td><td></td><td>The EAG device has custom software to measure signals and communicate with the PC.</td></tr><tr><td>Electrically conductive gel</td><td>Parker Laboratories, NJ, USA</td><td>Spectra 360</td><td></td></tr><tr><td>Ethanol</td><td>FUJIFILM Wako Pure Chemical Corporation, Ltd., Osaka, Japan</td><td>057-00456</td><td></td></tr><tr><td>Flowmeter</td><td>KOFLOC, Kyoto, Japan</td><td>RK1600R-12-B-Air-20</td><td></td></tr><tr><td>Gas sensor</td><td>Sensirion AG, St&amp;auml;fa, Switzerland</td><td>SGP30</td><td>SGP30 breakout board can be used.&lt;br/&gt; You can refer the Adafruit_SGP30 github library.&lt;br/&gt; https://github.com/adafruit/Adafruit_SGP30</td></tr><tr><td>High-sealed storage bottle</td><td>FUJIFILM Wako Pure Chemical Corporation, Ltd., Osaka, Japan</td><td>290-35731</td><td></td></tr><tr><td>Microcontroller</td><td>M5Stack, Shenzhen, China</td><td>M5StickC</td><td></td></tr><tr><td>Purebred silkworm diet</td><td>Nosan Corporation Life Tech Department, Kanagawa, Japan</td><td>Sausage type</td><td></td></tr><tr><td>Silkmoth</td><td>Ueda-sansyu, Nagano, Japan</td><td>a hybrid strain of Kinshu &amp;times; Showa</td><td></td></tr><tr><td>Solenoid valve</td><td>Takasago Electric, Inc., Nagoya, Japan</td><td>YDV-3-1/8</td><td></td></tr><tr><td>Wi-Fi access point</td><td>Yamaha Corporation, Shizuoka, Japan</td><td>WLX313</td><td></td></tr></tbody></table>

electroantennography-based bio-hybrid odor-detecting drone using silkmoth antennae for odor source localization

Small drones with chemical or biosensor devices that can detect airborne odorant molecules have attracted considerable attention owing to their applicability in environmental and security monitoring and search-and-rescue operations. Small drones with commercial metal-oxide-semiconductor (MOX) gas sensors have been developed for odor source localization; however, their real-time-odor-detection performance has proven inadequate. However, biosensing technologies based on insect olfactory systems exhibit relatively high sensitivity, selectivity, and real-time response with respect to odorant molecules compared to commercial MOX gas sensors. In such devices, excised insect antennae function as portable odorant biosensor elements and have been found to deliver excellent sensing performance. This study presents experimental protocols for odorant-molecule detection in the air using a small autonomous bio-hybrid drone based on a mountable electroantennography (EAG) device incorporating silkmoth antennae.
We developed a mountable EAG device including sensing/processing parts with a Wi-Fi module. The device was equipped with a simple sensor enclosure to enhance the sensor directivity. Thus, odor source localization was conducted using the spiral-surge algorithm, which does not assume an upwind direction. The experimental bio-hybrid odor-detecting drone identified real-time odorant-concentration differences in a pseudo-open environment (outside a wind tunnel) and localized the source. The developed drone and associated system can serve as an efficient odorant molecule-detection tool and a suitable flight platform for developing odor source localization algorithms owing to its high programmability.

This paper describes the protocols for signal measurements using the proposed EAG device mounted on a desk and drone. First, we evaluated the performance of the EAG device on a desk. A silkmoth antenna on the EAG device was stimulated by bombykol. Twenty-five continuous stimulations were conducted using 100 ng of bombykol dissolved in 50 &#181;L of hexane with intervals of 5 s, as controlled by a microcontroller. The results indicated that the proposed EAG device reproducibly responded to the stimulations (Figure 5).
The odor detection performance of the EAG device was subsequently evaluated on the drone. The drone equipped with the EAG device hovered at the height of 95 cm from the floor and at a distance of 90 cm from the odor source (Figure 6A). By following the procedure described in section 6, the signals of the EAG device on the drone were measured relative to bombykol (50,000 ng in 250 &#181;L of hexane/filter paper). The sensor performance of a commercial gas sensor on a drone was evaluated for comparison. A digital multi-pixel gas sensor28 was used to detect ethanol vapors. This sensor can be used for the detection of total volatile organic compounds (TVOCs).
According to the datasheet, the TVOC signal range of the sensor was 0-60,000 ppb. The drone with the gas sensor breakout board hovered under the same conditions as the EAG device. Moreover, 500 &#181;L of ethanol (99.5% purity) was used as the odor source instead of bombykol. The typical signals of the EAG device and gas sensor on the drone are shown in Figure 6B. As the odorant molecules and sensor devices differed in this comparison, quantitative comparisons could not be performed. However, the experimental results suggest that it may be difficult for a drone with a commercial gas sensor to detect odorant molecules with a rapid response/recovery speed. In particular, the recovery time of the gas sensor in this study was significantly higher than that of the EAG device with silkmoth antennae.
We also evaluated the sensor directivity of the EAG device on the drone. In this study, the direction toward the odor source was defined as 0&#176;, and the drone was rotated clockwise by 60&#176; intervals to evaluate signal intensities at each angle. For the drone without a sensor enclosure, the signal intensity at 180&#176;, while the drone faced in the opposite direction from the odor source, was occasionally higher than that at 0&#176; (Figure 6C). However, for the drone equipped with the enclosure, the signal intensity of the EAG at 0&#176; became higher than that at 180&#176; (Figure 6D). Consequently, the sensor enclosure enhanced the sensor directivity of the EAG device on the drone.
An odor-tracing demonstration was conducted using the bio-hybrid drone with the sensor enclosure. The results indicated that the drone detected bombykol in the air outside a wind tunnel and identified the direction of the odor plume by pivoting movements&#160;(Figure 7, Supplemental Video S1). Finally, odor source localization was conducted based on the spiral-surge algorithm using the bio-hybrid drone (Figure 8A). The drone was set at 270&#176; from the odor source at the starting point. After hovering, the drone started searching for the maximum value of the signal intensity during clockwise or counterclockwise spiral movements. Then, the drone moved forward in the direction of the maximum value of the signal intensity. After repeating the odor-searching spiral and surge movements six times, the drone landed on the ground. The flowchart of the spiral-surge algorithm is described in Terutsuki et al.26
The trajectory, yaw angles, and EAG signals during the odor source localization are presented in Figure 8B-D. Figure 8D shows that the detection time, including response and recover times of the EAG device on the drone, was approximately 1 s. The drone autonomously modified its movement by searching for the maximum odor concentration during the spiral movements. Readers can view videos of the odor source localization by the bio-hybrid drone described by Terutsuki et al.26.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/62895/62895fig01.jpg" /> 
Figure 1: The silkmoth, EAG device, and odor stimulation system. (A) Image of a male silkmoth. (B) Image of the mountable EAG device for a small drone. (C) Image of the odor stimulation system with airflow directions. Abbreviation: EAG = electroantennography. <a href="https://www.jove.com/files/ftp_upload/62895/62895fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/62895/62895fig02.jpg" /> 
Figure 2: Isolation of silkmoth antenna. (A) Isolation of a silkmoth antenna using postmortem scissors. (B) Typical isolated silkmoth antenna. (C) Enlarged view of an isolated silkmoth antenna; scale bar = 0.5 mm. <a href="https://www.jove.com/files/ftp_upload/62895/62895fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/62895/62895fig03.jpg" /> 
Figure 3: EAG device set up and GUI. (A) Installation of an isolated silkmoth antenna on the electrodes of the EAG device using gel. (B) Setup for odor stimulation using the EAG device on the desk. (C) The GUI for the experiments. Abbreviations: EAG = electroantennography; GUI = graphical user interface. <a href="https://www.jove.com/files/ftp_upload/62895/62895fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/62895/62895fig04.jpg" /> 
Figure 4: Bio-hybrid drone. (A) Bio-hybrid drone based on a silkmoth antenna. (B) Bio-hybrid drone with the sensor enclosure. (C) Configuration of the bio-hybrid drone. Scale bars (A, B) = 50 mm. Abbreviation: CFRP = carbon fiber-reinforced plastic. <a href="https://www.jove.com/files/ftp_upload/62895/62895fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/62895/62895fig05.jpg" /> 
Figure 5: Typical continuous response profile of the EAG device on the desk stimulated by bombykol. Abbreviation: EAG = electroantennography. <a href="https://www.jove.com/files/ftp_upload/62895/62895fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/62895/62895fig06.jpg" /> 
Figure 6: Experimental environment of the bio-hybrid drone and signal intensity of the EAG device. (A) Image of the experimental environment with the bio-hybrid drone, which autonomously hovered 95 cm above the ground at a distance of 90 cm from the odor source. (B) Comparison between the typical signals of the EAG device and commercial gas sensor on the drone. (C) Typical signal intensity of the EAG device without equipping the sensor enclosure on the drone at each angle (N = 1). (D) Average signal intensity of the EAG device with the enclosure on the drone at each angle (N = 3; individual tests). The unit of the signal intensities is V. C and D have been modified from Terutsuki et al.26. Abbreviations: EAG = electroantennography; TVOC = total volatile organic compounds. <a href="https://www.jove.com/files/ftp_upload/62895/62895fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/62895/62895fig07.jpg" /> 
Figure 7: Manual odor stimulation to demonstrate detection and tracing of odor in a room by the bio-hybrid drone. <a href="https://www.jove.com/files/ftp_upload/62895/62895fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/62895/62895fig08.jpg" /> 
Figure 8: Odor source localization by the bio-hybrid drone. (A) Viewpoint from the ceiling camera of the flight area of the bio-hybrid drone. (B) Typical flight trajectory, (C) yaw angles, and (D) EAG signal intensities during odor source localization using the spiral-surge algorithm. These figures are representative results (N=1). A-D have been modified from Terutsuki et al.26. <a href="https://www.jove.com/files/ftp_upload/62895/62895fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplemental Video S1: Demonstration of manual odor stimulation using the bio-hybrid drone. <a href="https://www.jove.com/files/ftp_upload/62895/Movie.mp4" target="_blank">Please click here to download this Video.</a>

Watch this Scientific Journal Video about Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization at JoVE.com

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

This study introduces experimental protocols for a bio-hybrid odor-detecting drone based on silkmoth antennae. The operation of an experimental electroantennogram device with silkmoth antennae is presented, in addition to the structure of a bio-hybrid drone designed for odor source localization using the spiral-surge algorithm.

Small drones with chemical or biosensor devices that can detect airborne odorant molecules have attracted considerable attention owing to their ...

electroantennography-based-bio-hybrid-odor-detecting-drone-using

Research

JoVE Journal

Engineering

5.2K Views.  The University of Tokyo. De biohybride drone met de silkmoth-antennes kan werken met een efficiënte geurmolecuuldetectietool en een geschikt vluchtplatform voor het ontwikkelen van lokalisatiealgoritmen voor geurbronnen. Het grote voordeel is dat de biohybride drone een sensorgeleiding heeft op geurbronnen, vanwege het uitrusten van de sensorbehuizing. Begin met het isoleren van zijdemotantennes met behulp van een postmortale schaar zonder anesthesie.Snijd beide zijden van de geïsoleerde zijdemotantennes en ...

Video: Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Electrophysiological Measurements from a Moth Olfactory System

Insect olfactory systems provide unique opportunities for recording odorant-induced responses in the forms of electroantennograms (EAG) and single sensillum recordings (SSR), which are summed responses from all odorant receptor neurons (ORNs) located on the antenna and from those housed in individual sensilla, respectively. These approaches have been exploited for getting a better understanding of insect chemical communication. The identified stimuli can then be used as either attractants or repellents in management strategies for insect pests.

Insect olfactory systems provide unique opportunities for recording odorant-induced responses in the forms of electroantennograms (EAG) and single sensillum recordings (SSR), which are summed responses from all odorant receptor neurons (ORNs) located on the antenna and from those housed in individual sensilla, respectively.

Insect olfactory systems provide unique opportunities for recording odorant-induced responses in the forms of electroantennograms (EAG) and single ...

Neuroscience

A Wind Tunnel for Odor Mediated Insect Behavioural Assays

Olfaction is the most important sensory mechanism by which many insects interact with their environment and a wind tunnel is an excellent tool to study insect chemical ecology. Insects can locate point sources in a three-dimensional environment through the sensory interaction and sophisticated behavior. The quantification of this behavior is a key element in the development of new tools for pest control and decision support. A wind tunnel with a suitable flight section with laminar air flow, visual cues for in-flight feedback and a variety of options for the application of odors can be used to measure complex behaviour which subsequently may allow the identification of attractive or repellent odors, insect flight characteristics, visual-odor interactions and interactions between attractants and odors lingering as background odors in the environment. A wind tunnel holds the advantage of studying the odor mediated behavioural repertoire of an insect in a laboratory setting. Behavioural measures in a controlled setting provide the link between the insect physiology and field application. A wind tunnel must be a flexible tool and should easily support the changes to setup and hardware to fit different research questions. The major disadvantage to the wind tunnel setup described here, is the clean odor background which necessitates special attention when developing a synthetic volatile blend for field application.

Here, we describe the construction and use of a wind tunnel for odor mediated behavioural assays with insects. The wind tunnel design facilitates the release of odor sources by several methods, with and without visual stimuli. Wind tunnel experiments are important methods to identify behaviorally active volatile chemicals.

Olfaction is the most important sensory mechanism by which many insects interact with their environment and a wind tunnel is an excellent tool to study ...

Behavior

Identification of Olfactory Volatiles using Gas Chromatography-Multi-unit Recordings (GCMR) in the Insect Antennal Lobe

All organisms inhabit a world full of sensory stimuli that determine their behavioral and physiological response to their environment. Olfaction is especially important in insects, which use their olfactory systems to respond to, and discriminate amongst, complex odor stimuli. These odors elicit behaviors that mediate processes such as reproduction and habitat selection1-3. Additionally, chemical sensing by insects mediates behaviors that are highly significant for agriculture and human health, including pollination4-6, herbivory of food crops7, and transmission of disease8,9. Identification of olfactory signals and their role in insect behavior is thus important for understanding both ecological processes and human food resources and well-being.
	To date, the identification of volatiles that drive insect behavior has been difficult and often tedious. Current techniques include gas chromatography-coupled electroantennogram recording (GC-EAG), and gas chromatography-coupled single sensillum recordings (GC-SSR)10-12. These techniques proved to be vital in the identification of bioactive compounds. We have developed a method that uses gas chromatography coupled to multi-channel electrophysiological recordings (termed 'GCMR') from neurons in the antennal lobe (AL; the insect's primary olfactory center)13,14. This state-of-the-art technique allows us to probe how odor information is represented in the insect brain. Moreover, because neural responses to odors at this level of olfactory processing are highly sensitive owing to the degree of convergence of the antenna's receptor neurons into AL neurons, AL recordings will allow the detection of active constituents of natural odors efficiently and with high sensitivity. Here we describe GCMR and give an example of its use.
	Several general steps are involved in the detection of bioactive volatiles and insect response. Volatiles first need to be collected from sources of interest (in this example we use flowers from the genus Mimulus (Phyrmaceae)) and characterized as needed using standard GC-MS techniques14-16. Insects are prepared for study using minimal dissection, after which a recording electrode is inserted into the antennal lobe and multi-channel neural recording begins. Post-processing of the neural data then reveals which particular odorants cause significant neural responses by the insect nervous system.
	Although the example we present here is specific to pollination studies, GCMR can be expanded to a wide range of study organisms and volatile sources. For instance, this method can be used in the identification of odorants attracting or repelling vector insects and crop pests. Moreover, GCMR can also be used to identify attractants for beneficial insects, such as pollinators. The technique may be expanded to non-insect subjects as well.

Identification of Olfactory Volatiles using Gas Chromatography-Multi-unit Recordings GCMR in the Insect Antennal Lobe

Olfactory cues mediate many different behaviors in insects, and are often complex mixtures comprised of tens to hundreds of volatile compounds. Using gas chromatography with multi-channel recording in the insect antennal lobe, we describe a method for the identification of bioactive compounds.

All  organisms inhabit a world full of sensory stimuli that determine their  behavioral and physiological response to their environment. Olfaction is ...

Multi-unit Recording Methods to Characterize Neural Activity in the Locust (Schistocerca Americana) Olfactory Circuits

Detection and interpretation of olfactory cues are critical for the survival of many organisms. Remarkably, species across phyla have strikingly similar olfactory systems suggesting that the biological approach to chemical sensing has been optimized over evolutionary time1. In the insect olfactory system, odorants are transduced by olfactory receptor neurons (ORN) in the antenna, which convert chemical stimuli into trains of action potentials. Sensory input from the ORNs is then relayed to the antennal lobe (AL; a structure analogous to the vertebrate olfactory bulb). In the AL, neural representations for odors take the form of spatiotemporal firing patterns distributed across ensembles of principal neurons (PNs; also referred to as projection neurons)2,3. The AL output is subsequently processed by Kenyon cells (KCs) in the downstream mushroom body (MB), a structure associated with olfactory memory and learning4,5. Here, we present electrophysiological recording techniques to monitor odor-evoked neural responses in these olfactory circuits.
First, we present a single sensillum recording method to study odor-evoked responses at the level of populations of ORNs6,7. We discuss the use of saline filled sharpened glass pipettes as electrodes to extracellularly monitor ORN responses. Next, we present a method to extracellularly monitor PN responses using a commercial 16-channel electrode3. A similar approach using a custom-made 8-channel twisted wire tetrode is demonstrated for Kenyon cell recordings8. We provide details of our experimental setup and present representative recording traces for each of these techniques.

Multi-unit Recording Methods to Characterize Neural Activity in the Locust Schistocerca Americana Olfactory Circuits

We demonstrate variations of the extracellular multi-unit recording technique to characterize odor-evoked responses in the first three stages of the invertebrate olfactory pathway. These techniques can easily be adapted to examine ensemble activity in other neural systems as well.

Detection and interpretation of olfactory cues are critical for the survival  of many organisms. Remarkably, species across phyla have strikingly similar ...

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Robots designed to track chemical leaks in hazardous industrial facilities1 or explosive traces in landmine fields2 face the same problem as insects foraging for food or searching for mates3: the olfactory search is constrained by the physics of turbulent transport4. The concentration landscape of wind borne odors is discontinuous and consists of sporadically located patches. A pre-requisite to olfactory search is that intermittent odor patches are detected. Because of its high speed and sensitivity5-6, the olfactory organ of insects provides a unique opportunity for detection. Insect antennae have been used in the past to detect not only sex pheromones7 but also chemicals that are relevant to humans, e.g., volatile compounds emanating from cancer cells8 or toxic and illicit substances9-11. We describe here a protocol for using insect antennae on autonomous robots and present a proof of concept for tracking odor plumes to their source. The global response of olfactory neurons is recorded in situ in the form of electroantennograms (EAGs). Our experimental design, based on a whole insect preparation, allows stable recordings within a working day. In comparison, EAGs on excised antennae have a lifetime of 2 hr. A custom hardware/software interface was developed between the EAG electrodes and a robot. The measurement system resolves individual odor patches up to 10 Hz, which exceeds the time scale of artificial chemical sensors12. The efficiency of EAG sensors for olfactory searches is further demonstrated in driving the robot toward a source of pheromone. By using identical olfactory stimuli and sensors as in real animals, our robotic platform provides a direct means for testing biological hypotheses about olfactory coding and search strategies13. It may also prove beneficial for detecting other odorants of interests by combining EAGs from different insect species in a bioelectronic nose configuration14 or using nanostructured gas sensors that mimic insect antennae15.

We describe a protocol for using insect antennae in the form of electroantennograms (EAGs) on autonomous robots. Our experimental design allows stable recordings within a day and resolves individual odor patches up to 10 Hz. The efficiency of EAG sensors for olfactory searches is demonstrated in driving a robot toward an odor source.

Robots designed to track chemical leaks in hazardous industrial facilities1 or explosive traces in landmine fields2 face the same problem as insects ...

Using Single Sensillum Recording to Detect Olfactory Neuron Responses of Bed Bugs to Semiochemicals

The insect olfactory system plays an important role in detecting semiochemicals in the environment. In particular, the antennal sensilla which house single or multiple neurons inside, are considered to make the major contribution in responding to the chemical stimuli. By directly recording action potential in the olfactory sensillum after exposure to stimuli, single sensillum recording (SSR) technique provides a powerful approach for investigating the neural responses of insects to chemical stimuli. For the bed bug, which is a notorious human parasite, multiple types of olfactory sensillum have been characterized. In this study, we demonstrated neural responses of bed bug olfactory sensilla to two chemical stimuli and the dose-dependent responses to one of them using the SSR method. This approach enables researchers to conduct early screening for individual chemical stimuli on the bed bug olfactory sensilla, which would provide valuable information for the development of new bed bug attractants or repellents and benefits the bed bug control efforts.

Bed bugs rely on olfactory receptor neurons housed in their antennal olfactory sensilla to detect semiochemicals in the environment. Utilizing single sensillum recording, we demonstrate a method to evaluate bed bug response to semiochemicals and explore the coding process involved.

The insect olfactory system plays an important role in detecting semiochemicals in the environment. In particular, the antennal sensilla which house ...

Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect

Robotic odor source localization has been a challenging area and one to which biological knowledge has been expected to contribute, as finding odor sources is an essential task for organism survival. Insects are well-studied organisms with regard to odor tracking, and their behavioral strategies have been applied to mobile robots for evaluation. This "bottom-up" approach is a fundamental way to develop biomimetic robots; however, the biological analyses and the modeling of behavioral mechanisms are still ongoing. Therefore, it is still unknown how such a biological system actually works as the controller of a robotic platform. To answer this question, we have developed an insect-controlled robot in which a male adult silkmoth (Bombyx mori) drives a robot car in response to odor stimuli; this can be regarded as a prototype of a future insect-mimetic robot. In the cockpit of the robot, a tethered silkmoth walked on an air-supported ball and an optical sensor measured the ball rotations. These rotations were translated into the movement of the two-wheeled robot. The advantage of this "hybrid" approach is that experimenters can manipulate any parameter of the robot, which enables the evaluation of the odor-tracking capability of insects and provides useful suggestions for robotic odor-tracking. Furthermore, these manipulations are non-invasive ways to alter the sensory-motor relationship of a pilot insect and will be a useful technique for understanding adaptive behaviors.

The capability to localize an odor source is necessary for insect survival and is expected to be applicable to artificial odor-tracking. The insect-controlled robot is driven by an actual silkmoth and enables us to evaluate the odor-tracking capability of insects through a robotic platform.

Robotic odor source localization has been a challenging area and one to which biological knowledge has been expected to contribute, as finding odor ...

Localization of Odorant Receptor Genes in Locust Antennae by RNA In Situ Hybridization

Insects have evolved sophisticated olfactory reception systems to sense exogenous chemical signals. These chemical signals are transduced by Olfactory Receptor Neurons (ORNs) housed in hair-like structures, called chemosensilla, of the antennae. On the ORNs&#39; membranes, Odorant Receptors (ORs) are believed to be involved in odor coding. Thus, being able to identify genes localized to the ORNs is necessary to recognize OR genes, and provides a fundamental basis for further functional in situ studies. The RNA expression levels of specific ORs in insect antennae are very low, and preserving insect tissue for histology is challenging. Thus, it is difficult to localize an OR to a specific type of sensilla using RNA in situ hybridization. In this paper, a detailed and highly effective RNA in situ hybridization protocol particularly for lowly expressed OR genes of insects, is introduced. In addition, a specific OR gene was identified by conducting double-color fluorescent in situ hybridization experiments using a co-expressing receptor gene, Orco, as a marker.

Localization of Odorant Receptor Genes in Locust Antennae by RNA In Situ Hybridization

This paper describes a detailed and highly effective RNA in situ hybridization protocol particularly for low-level expressed Odorant Receptor (OR) genes, as well as other genes, in insect antennae using digoxigenin (DIG)-labeled or biotin-labeled probes.

Insects have evolved sophisticated olfactory reception systems to sense exogenous chemical signals. These chemical signals are transduced by Olfactory ...

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Many scientifically and agriculturally important insects use antennae to detect the presence of volatile chemical compounds and extend their proboscis during feeding. The ability to rapidly obtain high-resolution measurements of natural antenna and proboscis movements and assess how they change in response to chemical, developmental, and genetic manipulations can aid the understanding of insect behavior. By extending our previous work on assessing aggregate insect swarm or animal group movements from natural and laboratory videos using the video analysis software SwarmSight, we developed a novel, free, and open-source software module, SwarmSight Appendage Tracking (SwarmSight.org) for frame-by-frame tracking of insect antenna and proboscis positions from conventional web camera videos using conventional computers. The software processes frames about 120 times faster than humans, performs at better than human accuracy, and, using 30 frames per second (fps) videos, can capture antennal dynamics up to 15 Hz. The software was used to track the antennal response of honey bees to two odors and found significant mean antennal retractions away from the odor source about 1 s after odor presentation. We observed antenna position density heat map cluster formation and cluster and mean angle dependence on odor concentration.

This protocol describes steps for using the novel software, SwarmSight, for frame-by-frame tracking of insect antenna and proboscis positions from conventional web camera videos using conventional computers. The free, open-source software processes frames about 120 times faster than humans and performs at better than human accuracy.

Many scientifically and agriculturally important insects use antennae to detect the presence of volatile chemical compounds and extend their proboscis ...

Electroantennographic Bioassay as a Screening Tool for Host Plant Volatiles

Plant volatiles play an important role in plant-insect interactions. Herbivorous insects use plant volatiles, known as kairomones, to locate their host plant.1,2 When a host plant is an important agronomic commodity feeding damage by insect pests can inflict serious economic losses to growers. Accordingly, kairomones can be used as attractants to lure or confuse these insects and, thus, offer an environmentally friendly alternative to pesticides for insect control.3 Unfortunately, plants can emit a vast number volatiles with varying compositions and ratios of emissions dependent upon the phenology of the commodity or the time of day. This makes identification of biologically active components or blends of volatile components an arduous process. To help identify the bioactive components of host plant volatile emissions we employ the laboratory-based screening bioassay electroantennography (EAG). EAG is an effective tool to evaluate and record electrophysiologically the olfactory responses of an insect via their antennal receptors. The EAG screening process can help reduce the number of volatiles tested to identify promising bioactive components. However, EAG bioassays only provide information about activation of receptors. It does not provide information about the type of insect behavior the compound elicits; which could be as an attractant, repellent or other type of behavioral response. Volatiles eliciting a significant response by EAG, relative to an appropriate positive control, are typically taken on to further testing of behavioral responses of the insect pest. The experimental design presented will detail the methodology employed to screen almond-based host plant volatiles4,5 by measurement of the electrophysiological antennal responses of an adult insect pest navel orangeworm (Amyelois transitella) to single components and simple blends of components via EAG bioassay. The method utilizes two excised antennae placed across a "fork" electrode holder. The protocol demonstrated here presents a rapid, high-throughput standardized method for screening volatiles. Each volatile is at a set, constant amount as to standardize the stimulus level and thus allow antennal responses to be indicative of the relative chemoreceptivity. The negative control helps eliminate the electrophysiological response to both residual solvent and mechanical force of the puff. The positive control (in this instance acetophenone) is a single compound that has elicited a consistent response from male and female navel orangeworm (NOW) moth. An additional semiochemical standard that provides consistent response and is used for bioassay studies with the male NOW moth is (Z,Z)-11,13-hexdecadienal, an aldehyde component from the female-produced sex pheromone.6-8

A method to rapidly screen host plant volatiles by measurement of the electrophysiological response of adult navel orangeworm (Amyelois transitella) antennae to single components and blends via electroantennographic analysis is demonstrated.

Plant volatiles play an important role in plant-insect interactions. Herbivorous insects use plant volatiles, known as kairomones, to locate their host ...

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

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