Jan 2020



Developing Biohybrid Robotic Jellyfish (Aurelia aurita) for Free-swimming Tests in the Laboratory and in the Field

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Biohybrid robotics is a growing field that incorporates both live tissues and engineered materials to build robots that address current limitations in robots, including high power consumption and low damage tolerance. One approach is to use microelectronics to enhance whole organisms, which has previously been achieved to control the locomotion of insects. However, the robotic control of jellyfish swimming offers additional advantages, with the potential to become a new ocean monitoring tool in conjunction with existing technologies. Here, we delineate protocols to build a self-contained swim controller using commercially available microelectronics, embed the device into live jellyfish, and calculate vertical swimming speeds in both laboratory conditions and coastal waters. Using these methods, we previously demonstrated enhanced swimming speeds up to threefold, compared to natural jellyfish swimming, in laboratory and in situ experiments. These results offered insights into both designing low-power robots and probing the structure-function of basal organisms. Future iterations of these biohybrid robotic jellyfish could be used for practical applications in ocean monitoring.

Keywords: Jellyfish (水母), Aurelia aurita (海月水母), Robotics (机器人学), Biohybrid robot (生物合成机器人), Swimming (游泳), Speed (速度), Ocean monitoring (海洋监测)


Despite the importance of the ocean and need to track its changing conditions, over 80% of the ocean remains largely unobserved and unexplored (Kim et al., 2012; Malve, 2016; NOAA, 2020a and 2020b). Further exploration is fundamental to reveal physical and biogeochemical processes in the ocean. This can improve our understanding of climate change, as well as provide new sources of food, medicine, and energy (NOAA, 2020b).

Traditional methods for ocean monitoring and exploration include remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs). ROVs and AUVs have been used to track anthropogenic effects on the ocean, with the potential to monitor changes in the ecosystem (Wynn et al., 2014; Teoh et al., 2018; Plum et al., 2020; Sanchez et al., 2020). These vehicles can commonly reach ranges of depths up to 6 km (Wynn et al., 2014), although examples such as the Nereus have approached 11 km (WHOI, 2014). However, in many cases these technologies are still limited by their power consumption and cannot be used in more environmentally sensitive areas of the ocean, where the vehicles could potentially produce or incur damage (Page et al., 2017). Such areas include small crevices, or even open spaces where natural debris can cause vehicle damage (Teoh et al., 2018; Plum et al., 2020). As an alternative tool, biologically inspired AUVs offer advantages that address such limitations, including high power consumption and disruptive wake signatures (Fish, 2020). These bioinspired vehicles use animal body forms and principles, within the broader frame of biomimicry.

Bioinspired robotics study the biological systems that act as models from which machines are designed. Biological systems are adapted to selective pressure and environmental conditions (Darwin, 1859), and engineers can apply these natural designs to address similar engineering problems (Fish, 2020). Within bioinspired robotics, biohybrid approaches integrate both biological and robotic components (Yang et al., 2018). These biological constructs have the capability both to improve robotics by taking advantage of existing tools in nature, and to use engineering tools to advance our knowledge of the natural world.

At present, there are two approaches to building biohybrid systems. The first is to incorporate live cells or tissues (Nawroth et al., 2012; Morimoto et al., 2018). Ricotti et al. (2017) describes this bottom-up approach for a comprehensive list of devices actuated by living cells, from bacteria and motile cells, cardiomyocytes, skeletal muscles, and insect self-contractile tissues. These approaches offer advantages, such as increased controllability and new potentials in environmental sensing, but require specific media for survival (Webster et al., 2016a and 2016b; Morimoto et al., 2018). Another approach is to integrate electronics into live insects (Sato and Maharbiz, 2010; Dirafzoon et al., 2015; Latif et al., 2016; Li and Sato, 2018; Saha et al., 2020; Tadepalli et al., 2020) and higher order animals (Valero-Sarmiento et al., 2017). The external control of insect locomotion has incited a growing collection of “cyborg” cockroaches (Holzer and Shimoyama, 1997; Bozkurt et al., 2016; Latif and Bozkurt, 2017), beetles (Sato et al., 2009; Cao et al., 2016; Li et al., 2016; Vo Doan et al., 2018; Li et al., 2018; Cao and Sato, 2019), and moths (Bozkurt et al., 2009; Tsang et al., 2010).

The control of aquatic animals has additional potentials for robotics. First, as previously described, biohybrid swimming robots can expand ocean monitoring to areas which traditional AUVs and ROVs cannot reach because of size constraints, damage to the vehicle, or impact on the wildlife (Teoh et al., 2018; Plum et al., 2020). Second, using marine invertebrates offers insights into modes of locomotion that arose early in the evolutionary history of animals (Halanych, 2015).

Animal models for aquatic vehicles
The choice of model organism offers different advantages and disadvantages in design considerations for bioinspired aquatic vehicles. For example, although large fishes and manta rays can travel fast speeds over long distances (Fish, 2020), the integration of robotic control onto these organisms requires extensive training, personnel, and access to these animals, which introduce more ethical considerations. Although aquatic invertebrates may swim slower and be more affected by the weight and buoyancy of self-contained robotic devices, these invertebrates offer advantages in their simplicity. Jellyfish offer additional benefits because of their low cost of transport (COT), one metric of energy efficiency defined as the mass-specific energy input per distance traveled (Gemmell et al., 2013).

Jellyfish as a model organism
Cnidarian jellyfish fossils have revealed the conservation of the medusan body structure since the Middle Cambrian period, despite over 505 million years of evolutionary pressure (Cartwright et al., 2007). Because the jellyfish bell structure and locomotion are linked to multiple behaviors, including feeding and escaping from predators (Arai, 1997), jellyfish swimming provides a unique model for evolutionary and ecological insights.

In addition to the energy efficiency exhibited by natural jellyfish, biohybrid robotic jellyfish offer new advantages to robotics because jellyfish are naturally found in a variety of environments, including thousands of meters below surface (the limit of current ocean exploration depths), which the animals can traverse without a swim bladder for pressure equilibration (NOAA, 2016). Although jellyfish are relatively slow swimmers with speeds on the order of cm/s, some species such as Stomolopus meleagris can exhibit directional swimming, migration, and maintain position against tidal currents, which are useful traits for an underwater vehicle (Shanks and Graham, 1987).

The microelectronic systems described in this protocol can increase swimming speeds up to threefold (Xu and Dabiri, 2020; Xu et al., 2020a). Moreover, because AUVs and ROVs have demonstrated high speeds on field tests (traveling on the order of m/s), both traditional and new biohybrid tools can be used synergistically for future deployments, where AUVs and ROVs can tow biohybrid robotic jellyfish to certain environments before releasing the biohybrid robots into smaller spaces or areas in which the vehicles cannot reach. This can expand our current capabilities in ocean monitoring to provide new insights into the natural world and changing climate.

Furthermore, a major advantage of using jellyfish over animals such as manta rays and fish is their lower taxonomic order and relatively tractable nervous systems. Although further ethical considerations of invertebrate research are merited, jellyfish are invertebrates exempt from evaluation by an Institutional Animal Care and Use Committee. Even among jellyfish clades, scyphomedusae (true jellyfish) possess the most diffuse nervous system organization, comprising eight sensory structures and two nerve nets: the motor nerve net (MNN) and diffuse nerve net (DNN) (Arai, 1997; Satterlie, 2011; Katsuki and Greenspan, 2013). Thus, the moon jellyfish species Aurelia aurita offers advantages as simple model organisms that do not possess a centralized nervous system, brain, or pain receptors; and have potential capabilities that could benefit current underwater technologies.

Biology of Aurelia aurita (moon jellyfish)
Jellyfish are invertebrates with a simple body structure comprising a flexible bell and a muscle monolayer that lines the subumbrellar bell surface. Specifically, the moon jellyfish Aurelia aurita is a species of scyphozoa, the Cnidarian class of true jellyfish with medusae possessing radial symmetry about the oral-aboral body axis (Arai, 1997) and have the ability to exhibit directional travel, useful for underwater vehicles (Hamner et al., 1994).

Jellyfish do not possess a centralized nervous system (CNS) or brain; rather, A. aurita have distributed, non-polarized neuronal networks (Arai, 1997; Satterlie, 2011; Katsuki and Greenspan, 2013; Byrne, 2017). These nervous systems include eight rhopalia, a motor nerve net (MNN), and a diffuse nerve net (DNN). The rhopalia (also known as swim pacemakers or marginal sensory structures) are equally distributed in indentations along the bell margin and directly activate the MNN, which then activates bidirectional waves of muscle contractions. Stimulation of one rhopalium is sufficient to activate the entire MNN (Passano, 1965; Mackie and Meech, 1995).

To swim, jellyfish contract their ring-shaped musculature to decrease the volume of the subumbrellar cavity, thus expelling water and providing the motive force for acceleration (Satterlie, 2002), with additional suction-based propulsion from vortical structures (Gemmell et al., 2015a). Oblate medusae such as Aurelia also rely heavily on the bell margin to create a paddling mode of locomotion (Colin and Costello, 2002; Costello et al., 2020).

Jellyfish primarily move vertically using pulsed propulsion sufficient for survival, i.e., passively catching prey, while their horizontal movement largely depends on ocean currents and waves (Satterlie, 2002). A. aurita also possess radial muscles that potentially contribute to non-symmetric thrust, thereby distorting individual vortex rings to cause turning maneuvers (Gemmell et al., 2015b). Jellyfish are energetically efficient swimmers because of passive energy recapture, in which the animals still propel forward in a relaxed state without added energy input (Gemmell et al., 2013; Gemmell et al., 2018).

Robotic control of live jellyfish swimming
The following protocols describe the steps to build a biohybrid robotic jellyfish, comprising a microelectronic swim controller embedded in live A. aurita medusae, and subsequent laboratory and in situ experiments to calculate vertical swimming speeds. Design considerations include minimizing costs by using inexpensive off-the-shelf components, keeping the robotic system as compact and neutrally buoyant as possible, and using a two-electrode system and symmetric activation pattern to incite unidirectional swimming. See Figure 1 for representative images and schematics of the biohybrid robotic jellyfish and microelectronic system.

Using these methods, we have determined that the user control of jellyfish can increase their swimming speeds up to three times, both in the absence of flow (in the laboratory) and in coastal conditions (Xu and Dabiri, 2020; Xu et al., 2020a). The existence of both faster and potentially more efficient swimming in jellyfish has implications for future studies in the ecology and evolution of basal organisms, beyond A. aurita. The results suggest that robotic systems can be used to uncover latent capabilities in organisms, and organisms can be used in biohybrid capabilities in ocean environments. Furthermore, biohybrid robotic jellyfish consume less external power per mass compared to other swimming robots in literature (Xu and Dabiri, 2020). This suggests that biohybrid designs can be used to improve the energy efficiency of robots to address extant challenges in robotics, which include power consumption and regeneration after damage (Yang et al., 2018).

This work describes the entire process toward building and implementing a biohybrid robotic jellyfish, with a proof of concept that these constructs can be deployed in real ocean environments. This provides the basis for future practical applications in the ocean, with the ultimate goal to track markers of climate change. For comprehensive information on the usage and execution of these protocols, refer to Xu and Dabiri (2020) for the swim controller design and laboratory experiments, and Xu et al. (2020a) for field experiments.

Figure 1. Biohybrid robotic jellyfish. A. Representative image of a biohybrid robotic jellyfish deployed in coastal conditions in the Atlantic Ocean. B. Schematic of the biohybrid robotic jellyfish to compare, with the animal bell labeled in white, swim controller housing in blue, two electrodes in red, and wooden pin in yellow. C. Two microelectronic swim controllers (front one is active, as shown by the red LEDs). Figures adapted from Xu et al. (2020a and 2020b).

Materials and Reagents

  1. TinyLily mini processor (TinyCircuits, catalog number: ASM2101)

  2. TinyLily light-emitting diodes (LED) 0402 (TinyCircuits, catalog number: ASL1001-LR)

  3. 10-mAh ultra-light lithium polymer (LiPo) battery cell (PowerStream Technology, catalog number: PGEB201212)

  4. Weights and buoyant materials (e.g., stainless steel washers and cork)

  5. Toothpicks (Good Old Values Bamboo Toothpicks, Pack of 1000)

  6. Platinum rod, 254.0-μm diameter (A-M Systems, catalog number: 711000)

  7. Polypropylene housing (e.g., 2.11-cm diameter cap of a 15 ml NuncTM conical centrifuge tube, Thermo ScientificTM, catalog number: 339650; and polypropylene plastic sheets cut to a corresponding circular shape)

  8. Perfluoroalkoxy alkane (PFA)-coated silver wire, 76.2-μm diameter bare, 139.7-μm diameter coated (A-M Systems, catalog number: 785500)

  9. Hot glue (e.g., Gorilla hot glue sticks, Gorilla Glue)

  10. Instant Ocean® Sea Salt (Instant Ocean, Spectrum Brands, catalog number: SS15-10, stored at room temperature)


  1. Soldering iron, solder, helping hands tool, and tweezers

  2. Candle or open flame

  3. Hot glue gun (e.g., cordless hot glue gun, Neu Master, catalog number: FQ-009)

  4. Parchment paper (e.g., Quillon parchment paper, Uline, catalog number: S-19145)

  5. Multimeter (e.g., Fluke 115 Field Technicians Digital Multimeter)

  6. Acrylic tank (Envision Acrylics, Inc., custom design with dimensions 1.8 m × 0.9 m × 0.9 m)

  7. Refractometer (e.g., Milwaukee MA871 Digital Brix Refractometer, catalog number: MW-MA871)

  8. Ladder

  9. Net (e.g., a large fish net with a handle, or a custom-built fish net to match the dimensions of the bottom of the tank, with strings and handles to pull upward)

  10. Plastic containers with lids (e.g., 86 oz clear food storage container, Basix)

  11. Insulated Styrofoam container (e.g., Thermo Chill insulated carton with foam shipper, medium, 12” × 10” × 7”, Polar Tech Industries, catalog number: 227C)

  12. Cyber-shot DSC-RX100 (Sony, catalog number: DSCRX100/B)

  13. Sony AX100 camera (Sony, catalog number: FDR-AX100)

  14. Gates AX100 Underwater Housing (Gates Underwater Products, catalog number: AX100)

  15. Background boards (e.g., black foam board, 36 × 48”, Uline, catalog number: S-19381 and white foam board 20 × 30 × 3/16” or 5 mm, Navy Penguin)

  16. Rope tied to a dive weight

  17. Zip ties (e.g., red and yellow nylon zip ties)

  18. SCUBA diving equipment (e.g., mask, snorkel, fins, wetsuit, regulator, compressed air tanks, dive computer)


  1. Arduino (Arduino, https://www.arduino.cc/)

  2. MATLAB (MathWorks, https://www.mathworks.com/)

  3. ImageJ (National Institutes of Health, https://imagej.nih.gov/ij/)


  1. Building microelectronic swim controllers

    1. Program the TinyLily microcontroller to the desired signal frequency, according to the manufacturer’s instructions for uploading the code to the board using Arduino. Figure 2A shows the square pulse wave signal, with an amplitude of 3.7 V, pulse width of 10 ms, and adjustable frequency (f) and period (T).

      Note: Figure 2B shows an example of the Arduino code for a square pulse wave, with pulse width of 10 ms. Replace ‘TIME’ (highlighted in orange) with the desired period (T) in ms, i.e., for a frequency (f) of 0.50 Hz, T = 2,000 ms. For a two-electrode system, choose two stimulation pins, such as pins 2 and 3 shown in the example code in Figure 2B.

      Figure 2. Electrical stimulation of jellyfish. A. A schematic of a square pulse wave to incite muscle contractions in Aurelia aurita moon jellyfish. The signal amplitude is 3.7 V and pulse width is 10 ms. The signal frequency (f) and period (T) can be adjusted, as desired. B. An example code that executes the signal in (A) using two stimulation pins, stimPins 2 and 3, which correspond to the yellow pins in Figure 3B. To adjust the signal frequency, replace ‘TIME’ (highlighted in orange) with the period in ms.

    2. To build one electrode:

      1. Cut two pieces of PFA-coated silver wires to 10 cm in length, or at least half of the diameter of the jellyfish.

        Note: The animals used in prior experiments ranged from 9.8 to 19.0 cm in diameter.

      2. Flame both ends of the silver wires over an open flame to strip the PFA coating and expose the silver tip.

      3. Cut two pieces of platinum rod to 1 cm in length.

      4. Use a soldering iron, helping hand tools, and tweezers to maneuver the platinum rods, silver wires, and one LED into place (see Figure 3A for reference).

        Note: Use higher temperatures, such as 450 °C or above, to improve the soldering process.

        Figure 3. Microelectronic components of the jellyfish swim controller. A. An electrode, comprising the following components: a red TinyLily LED, soldered platinum wire tips bent into a fishhook-like shape, soldered PFA-coated silver wires (connected to the microcontroller via yellow and black pins, as illustrated in B), and hot melt adhesive coating to waterproof. B. TinyLily microcontroller, with two stimulation pins labeled in yellow (pins 2 and 3), two ground pins labeled in black and cyan, and a power pin labeled in red. Photo credit to Tiny Circuits. C. Lithium polymer battery cell (Powerstream), active when connected to the red and blue pins in B.

      5. Solder one end of each platinum rod to the positive and negative terminals of the LED.

      6. Solder one exposed end of each silver wire to the LED terminals.

      7. Use hot melt adhesive to coat the LED to seal the solder connections.

        1. Squeeze a thin strip of hot melt adhesive directly onto the parchment paper and place the LED onto the glue. The parchment paper will not stick to the adhesive.

        2. Press the electrode gently into the adhesive to ensure the surface is covered.

        3. Wait for the adhesive to dry (approximately 1 min), and use the tweezers to pick up the electrode, which is now coated on one side.

        4. Squeeze another thin strip of hot melt adhesive onto the parchment paper.

        5. Press the other side of the electrode into the adhesive to ensure the surface is covered.

        6. Wait for the adhesive to dry, and trim excess glue as needed.

        7. Ensure that no solder is exposed to the air.

          Note: Exposed solder will increase hydrogen bubble production during electrolysis, which can cause tissue damage to the jellyfish mesoglea over prolonged periods.

      8. Use the tweezers to bend the platinum rods into a fishhook-like shape, as shown in Figure 3A.

    3. Repeat Step A2 to build multiple electrodes.

    4. Trim a toothpick, removing at least 1/3 of the total length and keeping the remainder. The length of this remaining piece can be adjusted as desired, according to the depth of tissue in the jellyfish manubrium.

    5. Use hot melt adhesive to attach the blunt end of the toothpick to the cylindrical plastic housing, as shown in Figure 1C for reference.

    6. Solder the exposed silver wire connected to the negative terminal of the electrode onto the TinyLily ground pin (labeled in black in Figure 3B). Multiple wires can be soldered to this ground pin.

    7. Solder the exposed silver wire connected to the positive terminal of the electrode onto the TinyLily stimulation pin (for the two-electrode example, solder one wire onto pin 2 and the other onto pin 3, shown in yellow in Figure 3B).

      Note: Use a multimeter to test the connections.

    8. Solder the positive and negative terminals of the LiPo battery (Figure 3C) onto the corresponding terminals on the Tiny Lily (labeled in red and blue in Figure 3B).

      The LEDs will flash while the signal is on, as a visual confirmation that the swim controller is active. Figure 1C shows an example of an active swim controller with red LED electrodes.

      Note: Take care not to short the battery when soldering. One technique is to apply shrink wrap or other nonconductive materials to the battery leads. Avoid using the same ground terminal as used in Step A6.

    9. Optional: Wrap tape around the microcontroller and battery to secure all the connections in place before attaching to the plastic housing.

    10. Use hot melt adhesive to secure the central electronics (soldered microcontroller and battery unit) into the cylindrical plastic housing (centrifuge cap). Ensure that the electrodes and electrode wires are outside of the housing.

    11. Use hot melt adhesive to secure the flat circular plastic piece onto the cylindrical piece, as shown in Figure 4.

      Note: Ensure that the electrode wires are oriented evenly outward from the central housing before attaching the flat piece. Once the housing is secured with adhesive, removing the electronics will damage the silver wires and potentially break the connection to the electrodes.

      Figure 4. Side view of a two-electrode swim controller to show the flat polypropylene piece (black), with a silver washer to ballast the system. Scale bar, 1 cm.

    12. Offset the weight of the microelectronics accordingly by using washers and/or cork until the entire microelectronic system is neutrally buoyant in the desired saltwater, as shown in Figure 4.

      Note: It is helpful to determine the buoyancy on an inactive swim controller before assembling these devices. Nevertheless, because A. aurita are sensitive to changes in buoyancy, fully assembled devices should be retested in the appropriate water conditions, i.e., salt concentration and temperature of the water to maintain neutral buoyancy. In addition, changing the ballast of the swim controller can determine the orientation for improved unidirectional swimming stability, i.e., the swim controller was designed to be top heavy for swimming downward to the bottom of the tank (Xu and Dabiri, 2020), and bottom heavy for swimming upward to the ocean surface (Xu et al., 2020a).

  2. Inserting the swim controller into the jellyfish

    1. Place a jellyfish subumbrellar surface upward in a container filled with saltwater.

    2. Insert the toothpick through the manubrium so that the plastic housing is placed directly on the stomachs and oral arms. Ensure that the entire wooden pin is embedded securely in the jellyfish.

    3. Use one hand to hold the jellyfish bell securely. Use the other hand to embed one electrode into the jellyfish, and a curved hand motion to secure the fishhook-shaped platinum rod tips into the tissue.

      1. Embedding the electrode should be conducted as swiftly as possible to avoid excessive motion from the animal, which can cause the electrodes to pull on the tissue.

      2. The optimal location to embed is toward the margin, from the underside of the animal. Because the marginal tissue is thin, embedding the electrode at a radial distance between the margin (radius R) and halfway inward (radius R/2) is sufficient.

      3. Proper technique ensures a tight hold in the jellyfish tissue and requires additional effort to remove the device from the animal. If the electrode is not embedded well into the tissue, or if the electrode falls out of the animal, repeat the process again until there is a secure hold. Avoid embedding directly into the same location, which can cause excessive tissue damage that can preclude proper electrode insertion.

    4. Repeat Step B3 for multiple electrodes. Ensure that the electrodes are spaced evenly, i.e., for a two-electrode system, the electrodes should be embedded symmetrically in the animal (see Figure 1A).

  3. Conducting straight free-swimming experiments in the laboratory

    1. Fill the tank with DI water.

    2. Add Instant Ocean salt mix to the desired salinity (35 ppt).

      1. To begin adding the salt, first estimate the amount of salt mix needed, assuming no impurities as a conservative estimate (Arain, 2020).

        Note: Add the salt mix into DI water. Adding DI water into the salt mix can form calcium precipitates, which can result in cloudy water.

      2. Measure the salinity using a refractometer, and add more salt mix accordingly.

      3. Mix the water well, ideally using an automatic pump to recirculate the water.

      4. Let equilibrate for at least a few hours, ideally overnight.

      5. Measure the salinity using a refractometer, and adjust the salt or water content accordingly.

      6. Use towels or other cleaning tools to remove air bubbles from the sides of the tank.

    3. Acclimate the jellyfish (without embedded swim controllers) to the water for at least 4 h, up to overnight. This will ensure that the tissue density is approximately neutral with the surrounding water.

    4. Optional: Place a net on the bottom of the 1.8-m length tank to collect the jellyfish post-experiments.

      Note: Because the animals will be initiated from the top of the tank and swim downward, these experiments are most easily conducted using batches of multiple animals. For successive runs, biohybrid robotic jellyfish from previous trials remain at the bottom of the tank until all animals are tested. Use the net to bring all animals to the top of the tank to remove, then wait at least 30 min for the water to return to static conditions, as described in Steps C10 to C12.

    5. Set up a camera, e.g., 1,920 × 1,080 resolution at 60 fps on the Cyber-shot DSC-RX100, as shown in Figure 5 to record videos of the animals swimming.

      Figure 5. Laboratory setup for vertical swimming experiments. The setup for laboratory experiments to calculate the vertical swimming speed of biohybrid robotic jellyfish includes the following components: a 1.8-m tall acrylic tank filled with saltwater (35 ppt), background board (black or white) attached to the back panel of the tank, biohybrid robotic jellyfish (swimming downward, initiated from the top of the tank), optional net at the bottom of the tank to facilitate removing the jellyfish from the tank post-experiments, and camera to record videos. The yellow box highlights the area of the tank in which the animal is away from the sides, top, and bottom of the tank, which can minimize disturbances from wall effects.

    6. Tape the background boards to the outside of the tank’s back panel, as shown in Figure 5. This will ensure that the background is a uniform color and simplify the data analysis.

      Note: To track the swim controller, use a white board so that the swim controller housing is maximally visible through semi-transparent animal (Figure 8A). To track the whole animal, use a black board with adequate lighting on the animal tissue, which will appear to be an opaque white (Figure 8B). Tracking the entire animal can allow for additional analysis of the geometric parameters, such as the time-dependent diameter of the animal throughout the swimming cycle or time periods of contraction and relaxation.

    7. Use a ladder to access the top of the tank. Insert an active microelectronic swim controller into the jellyfish, as described in Procedure B, while the animal is at the top of the tank.

    8. Hold the animal with both hands at the top of the tank, and wait for the water to become relatively quiescent, approximately 2 min.

    9. Slowly release the animal, taking care to minimally disturb the water. Allow the animal to swim downward to the bottom of the tank.

    10. Wait at least 5 to 15 min between successive trials to allow the water to return to quiescent conditions, depending on the amount of disturbance generated by the biohybrid robotic jellyfish.

    11. Repeat Steps C7 to C10 for additional animals.

    12. Once all animals are at the bottom of the tank, use the net to capture all biohybrid robotic jellyfish to the surface, and remove from the tank.

    13. For additional trials, wait at least 30 min to allow the water to return to quiescent conditions, depending on the amount of disturbance generated by the biohybrid robotic jellyfish and net.

    14. Repeat Steps C7 to C13 until all experimental parameters are tested (e.g., frequencies ranging from 0.25 to 1.00 Hz and control cases with inactive swim controllers at 0 Hz), as described in Xu and Dabiri (2020). After all trials are completed, proceed to Procedure E for post-experimental steps.

  4. Conducting straight free-swimming experiments in situ (coastal conditions)

    1. Prepare for field experiments.

      1. Attach colored zip ties onto the dive rope with alternating colors (red and yellow) spaced 30.5 cm. Trim the excess zip tie material. The remainder attached to the rope serves as background markers for data analysis.

      2. Tie a dive weight (e.g., 2.5 kg) to one end of the rope.

      3. Insert an active microelectronic swim controller into the jellyfish, as described in Procedure B, with each animal in an individual plastic container filled with saltwater that matches the field conditions (see Figure 6C).

      4. Cover the plastic container with its corresponding lid.

        Note: Ensure that the plastic containers are overfilled with saltwater to minimize the presence of air bubbles that could negatively impact the jellyfish.

      5. Place the plastic containers in an insulated box to carry into the field (Figure 6B). Ensure that the containers are secured and do not shift within the box; if the containers move freely, include padding or other filler materials.

        Note: Bring extra swim controllers (embedded in foam for protection), animals, battery-powered soldering irons, scissors, and other useful tools into the field.

      6. Place the camera in the waterproof plastic casing, e.g., Sony AX100 camera in the Gates AX100 Underwater Housing.

    2. Allow two SCUBA divers to prepare their gear and enter the water (labeled ‘Divers 1 and 2’), as shown in Figure 6A.

    3. Allow one person to stay on shore or a dock, with the biohybrid robotic jellyfish and other experimental materials and equipment (labeled ’Person on shore’), as shown in Figure 6A.

      Figure 6. Field experiments conducted in coastal conditions in Woods Hole, MA. A. Swimming experiments in the coastal waters of MA required two SCUBA divers and one person on shore. The person on shore can tie the rope to the dock post and maneuver the biohybrid robotic jellyfish to one diver (illustrated in D), who can release the jellyfish at the bottom of the ocean near the rope as a background feature. The second diver can record videos of the biohybrid robotic jellyfish. B. Biohybrid robotic jellyfish were transported in plastic containers, stored in an insulated Styrofoam box, with extra swim controllers embedded in foam. C. An image of one biohybrid robotic jellyfish in the plastic container. D. A diver holding a container with a biohybrid robotic jellyfish in preparation for vertical swimming experiments. Note that the rope with red and yellow markers is visible, secured to the wooden dock post.

    4. Diver 1: Place the weighted end of the rope on the ocean floor for coastal experiments, as shown in Figure 7.

      Figure 7. Field setup for vertical swimming experiments. The setup for in situ experiments in coastal conditions to calculate the vertical swimming speed of biohybrid robotic jellyfish includes the following components: a rope with alternating red and yellow markers, weighted to the bottom of the ocean and securely fashioned to the surface; one diver with an underwater camera setup to record videos of the trials; a second diver to maneuver the animal into its initial position at the ocean bottom using a container; and the biohybrid robotic jellyfish, swimming upward.

    5. Person on shore: Tie the other end of the rope to the dock or other surface structure, ensuring that the rope is taut to minimize rope motion. The rope should be visible in the background of recorded videos for data analysis (1,920 × 1,080 resolution at 30 fps).

    6. Person on shore: Remove the lid and hand a plastic container with one of the biohybrid robotic jellyfish to Diver 2, as shown in Figure 6D.

    7. Diver 2: Gently submerge the plastic container into the water, taking care to ensure that the microelectronic system is still fully embedded in the jellyfish. Move the container to the bottom of the ocean, near the background rope. Gently release the biohybrid robotic jellyfish and ensure that the animal is oriented properly for forward swimming.

    8. Diver 1: Record a video of the biohybrid robotic jellyfish, taking care to minimize diver and camera motion and keep the rope markers visible in the background. Track the jellyfish from the ocean floor to surface (1.6 m) if possible, with minimal motion in the depth axis of the camera (in and out of the image plane).

    9. Diver 2: Once the animal reaches the ocean surface, capture the biohybrid robotic jellyfish in the plastic container and hand the container back to the person on shore.

    10. Person on shore: To obtain control cases, cut the electrode wires, taking care to ensure that all microelectronics are still strongly embedded into the animal. Hand the biohybrid robotic jellyfish in the plastic container back to Diver 2.

      1. Control cases are defined as trials in which the swim controller is embedded but inactive (0 Hz) to determine each animal’s baseline swimming frequency and speed.

    11. Divers 1 and 2: Repeat Steps D7 to D9.

    12. Repeat Steps D6 to D11 for additional animals. After all trials are completed, proceed to Procedure E for post-experimental steps.

  5. Conducting post-experimental procedures

    1. Remove the electrodes from the animals by gently following the curvature of the fishhook-like platinum rods to minimize tissue damage.

    2. Remove the plastic housing by gently pulling the wooden pin from the manubrium.

    3. Return the animals to their housing conditions.

    4. Feed the animals (such as with Artemia nauplii) to encourage recovery.

Data analysis

  1. Calculate the unidirectional swimming speed and enhancement factor of biohybrid robotic jellyfish using MATLAB

    1. Convert videos to images.

    2. Use ImageJ to obtain the diameter of the animal, swim controller housing, and other relevant geometric parameters (if desired) in pixels. (The diameters of the animal and housing are known quantities in centimeters.)

    3. Find the (x, y) centroids of the jellyfish swim controller (Figure 8A) for all frames, time t, in pixels (capx,t, capy,t).

      Note: If using color images, split the image into its red, green, and blue components and filter for the appropriate size and color of the swim controller housing, i.e., blue in Figure 8A. If using a dark background in laboratory experiments, find the (x, y) centroids of the jellyfish, filtering for bright pixels to obtain the opaque white tissue (Figure 8B).

      Figure 8. Representative images of laboratory conditions. A. A biohybrid robotic jellyfish (circled in red) swimming downward in the tank. Note that the semi-transparent jellyfish tissue blends in with the white background, allowing the swim controller to be visible for tracking. B. A biohybrid robotic jellyfish (circled in red) swimming downward in the tank. Note that the jellyfish tissue is white against the black background, which allows for the animal bell and morphological parameters to be tracked. For clarity, reflections and other aspects of the tank are also visible, including side ports (far right). Scale bars, 10 cm.

    4. Calculate the vertical displacements for laboratory experiments, in which the camera is stationary:

      1. Calculate the vertical displacement (distance between the centroid and the initial starting centroid) for each frame: dt = capy,t – capy,0

      2. Convert the displacements from pixels to centimeters by using a known scale in the image, such as the diameter of the swim controller housing (2.11 cm), or other methods of camera calibration.

      Note: Select video segments in which the animal is in center of the tank, and track vertical displacements only for the middle half of the tank (see yellow box in Figure 5). This will minimize wall effects from the animal swimming too close to the sides or bottom of the tank, and from water disturbances at the top and bottom of the tank from initializing the experiment or other animals pulsing at the bottom of the tank from previous trials.

    5. Calculate the vertical displacements for in situ experiments, in which camera motion is nonzero:

      1. Find the (x, y) centroids of the rope markers in the background, in pixels (markerx,t, markery,t).

        Note: Use the same technique above, filtering for the appropriate sizes and colors of the rope markers, i.e., red and yellow in Figure 9.

        Figure 9. Representative image from in situ experiments. A biohybrid robotic jellyfish in field conditions, with the background markers (red and yellow) on the rope visible.

      2. Determine the (x, y) centroids of the jellyfish relative to the background rope markers (norm_capx,t, norm_capy,t).

        Note: Because MATLAB’s image origin is defined as the top left corner, the equation should be norm_capx,t = capx,t – markerx,t for the x-components and norm_capy,t = markery,t – capy,t for the y components.

      3. Calculate the vertical displacement (distance between the centroid and the initial starting centroid) for each frame: dt = norm_ capy,t norm_capy,0.

      4. Convert the displacements from pixels to centimeters by using a known scale in the image, such as the distance between subsequent rope markers (30.5 cm), or other methods of camera calibration.

        1. Calculate the distance in pixels between subsequent rope markers, if multiple rope markers are visible. If multiple rope markers are not visible, mark the distance as not a number (NaN).

        2. Interpolate between the distances to fill in NaN values.

      Note: Select video segments in which the animal is toward the middle of the ocean depth, avoiding the ocean bottom and surface (see note in the above step 4).

    6. Calculate the differential swimming speeds, using the distance between each subsequent pair of time steps (dt+1 – dt) and multiplying by the camera framerate, e.g., 60 fps in the laboratory experiments and 30 fps in the field experiments.

    7. Calculate the mean swimming speed (v).

    8. Optional: Calculate the normalized swimming speed, or proficiency, by dividing the absolute swimming speed by the diameter of the animal (D): vnorm = v/D .

    9. Calculate the enhancement factor by dividing the mean swimming speed (absolute or normalized, as previously described) by the baseline swimming speed (the swimming speed of the control case, in which the animal is initiated with an inactive swim controller, 0 Hz).

    Note: Additional information is described for laboratory experiments in Xu and Dabiri (2020) and for field experiments in Xu et al. (2020a).


We gratefully acknowledge Cabrillo Marine Aquarium for providing Aurelia aurita medusae, and Angela Fan for illustrating the scientific artwork associated with this manuscript. This work was supported in part by a National Science Foundation Graduate Research Fellowship, Grant Number DGE-1147470, awarded to N.W.X. in 2015. The protocols are appended from the original manuscripts, Xu and Dabiri (2020) and Xu et al. (2020a).

Competing interests

The authors declare no competing interests.


A. aurita are invertebrates that do not possess a brain, central nervous system, pain receptors, or nociceptors, and therefore do not require protocol review or approval from the Institutional Animal Care and Use Committee (IACUC). However, we took great care to ensure the welfare interests of these animals, in accordance to the precautionary and minimization principles to reduce the number of animals used and refine procedures to minimize potential distress. We also monitored the jellyfish and microelectronic systems to ensure that we introduced no additional electronic waste or other material into the ocean. Further information about our ethical views can be found in Xu et al. (2020b).


  1. Arai, M. N. (1997). A functional biology of scyphozoa. London: Chapman & Hall.
  2. Arain, H. M. (2020). Direct salt addition calculator. Hamza’s Reef.
  3. Bozkurt, A., Lal, A. and Gilmour, R. (2009). Aerial and terrestrial locomotion control of lift assisted insect biobots. Annu Int Conf IEEE Eng Med Biol Soc 2009: 2058-2061.
  4. Bozkurt, A., Lobaton, E. and Sichitiu, M. (2016). A biobotic distributed sensor network for under rubble search and rescue. IEEE Computer, Special Issue Emergency Response 49(5): 38-46.
  5. Byrne, J. H., editor. (2017). The Oxford Handbook of Invertebrate Neurobiology. Oxford University Press.
  6. Cao, F., Zhang, C., Choo, H. Y. and Sato, H. (2016). Insect–computer hybrid legged robot with user- adjustable speed, step length and walking gait.J R Soc Interface 13(116): 20160060.
  7. Cao, F. and Sato, H. (2019). Insect–computer hybrid robot achieves a walking gait rarely seen in nature by replacing the anisotropic natural leg spines with isotropic artificial leg spines.IEEE Trans Robot 35(4): 1034-1038.
  8. Cartwright, P., Halgedahl, S. L., Hendricks, J. R., Jarrard, R. D., Marques, A. C., Collins, A. G. and Lieberman, B. S. (2007). Exceptionally preserved jellyfishes from the Middle Cambrian. PLoS ONE 2(10): e1121.
  9. Darwin, C. (1859). On the origin of species by means of natural selection, or preservation of favoured races in the struggle for life. London: John Murray.
  10. Colin, S. P. and Costello, J. H. (2002). Morphology, swimming performance and propulsive mode of six co-occurring hydromedusae. J Exp Biol 205(Pt 3): 427-437.
  11. Costello, J. H., Colin, S. P., Dabiri, J. O., Gemmell, B. J., Lucas, K. N. and Sutherland, K. R. (2020). The Hydrodynamics of Jellyfish Swimming. Ann Rev Mar Sci.
  12. Dirafzoon, A., Bozkurt, A. and Lobaton, E. (2015). Dynamic topological mapping with biobotic swarms. arXiv. 1507.03206v2. https://arxiv.org/abs/1507.03206.
  13. Fish, F. E. (2020). Advantages of aquatic animals as models for bio-inspired drones over present AUV technology. Bioinspir Biomim15(2): 025001.
  14. Gemmell, B. J., Costello, J. H., Colin, S. P., Stewart, C. J., Dabiri, J. O., Tafti, D. and Priya, S. (2013). Passive energy recapture in jellyfish contributes to propulsive advantage over other metazoans. Proc Natl Acad Sci U S A 110(44): 17904-17909.
  15. Gemmell, B. J., Colin, S. P., Costello, J. H. and Dabiri, J. O. (2015a). Suction-based propulsion as a basis for efficient animal swimming. Nat Commun 6: 8790.
  16. Gemmell, B. J., Troolin, D. R., Costello, J. H., Colin, S. P. and Satterlie, R. A. (2015b). Control of vortex rings for manoeuvrability. J R Soc Interface 12(108): 20150389.
  17. Gemmell, B. J., Colin, S. P. and Costello, J. H. (2018). Widespread utilization of passive energy recapture in swimming medusae. J Exp Biol 221(Pt 1).
  18. Halanych, K. M. (2015). The ctenophore lineage is older than sponges? That cannot be right! Or can it? J Exp Biol 218(4): 592-597.
  19. Hamner, W. M., Hamner, P. P. and Strand, S. W. (1994). Sun-compass migration by Aurelia aurita (Scyphozoa): population retention and reproduction in Saanich Inlet, British Columbia. Mar Biol 119(3): 347-356.
  20. Holzer, R. and Shimoyama, I. (1997). Locomotion control of a bio-robotic system via electric stimulation. In: Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS ’97.
  21. Katsuki, T. and Greenspan, R. J. (2013). Jellyfish nervous systems.Curr Biol 23(14): R592-594.
  22. Kim, S. K. and Pallela, R. (2012). Medicinal foods from marine animals. In: Marine Medicinal Foods - Impli- cations and Applications - Animals and Microbes, pages 1-9. Elsevier.
  23. Latif, T. and Bozkurt, A. (2017). Roach biobots: toward reliability and optimization of control. IEEE Pulse 8(5): 27-30.
  24. Latif, T., Whitmire, E., Novak, T. and Bozkurt, A. (2016). Sound localization sensors for search and rescue biobots. IEEE Sens J 16(10): 3444-3453.
  25. Li, Y. and Sato, H. (2018). Insect-computer hybrid robot. Mol Front J 02(01): 30-42.
  26. Li, Y., Wu, J. and Sato, H. (2018). Feedback control-based navigation of a flying insect-machine hybrid robot. Soft Robotics 5(4): 365-374.
  27. Li, Y., Cao, F., Thang Vo Doan, T., and Sato, H. (2016). Controlled banked turns in coleopteran flight measured by a miniature wireless inertial measurement unit.Bioinspir Biomim 11(5): 056018.
  28. Mackie, G. and Meech, R. (1995). Central circuitry in the jellyfish Aglantha. II: The ring giant and carrier systems.J Exp Biol 198(11): 2271-2278.
  29. Malve, H. (2016). Exploring the ocean for new drug developments: Marine pharmacology. J Pharm Bioallied Sci 8(2): 83-91.
  30. Morimoto, Y., Onoe, H., and Takeuchi, S. (2018). Biohybrid robot powered by an antagonistic pair of skeletal muscle tissues. Sci Robot 3(18): eaat4440.
  31. Nawroth, J. C., Lee, H., Feinberg, A. W., Ripplinger, C. M., McCain, M. L., Grosberg, A., Dabiri, J. O. and Parker, K. K. (2012). A tissue-engineered jellyfish with biomimetic propulsion. Nat Biotechnol 30(8): 792-797.
  32. NOAA. (2016). NOAA Ship Okeanos Explorer: 2016 Overview: NOAA Office of Ocean Exploration and Research. National Oceanic, and Atmospheric Administration. US Department of Commerce.
  33. NOAA. (2020a). What is ocean exploration and why is it important? National Oceanic, and Atmospheric Administration.
  34. NOAA. (2020b). Why should we care about the ocean? National Oceanic, and Atmospheric Administration.
  35. Page, B. R., Ziaeefard, S., Pinar, A. J. and Mahmoudian, N. (2017). Highly maneuverable low-cost underwater glider: Design and development. IEEE Robot Auto Let 2(1): 344-349.
  36. Passano, L. M. (1965). Pacemakers And Activity Patterns In Medusae: Homage To Romanes. Am Zool 5: 465-481.
  37. Plum, F., Labisch, S. and Dirks, J. H. (2020). SAUV-A Bio-Inspired Soft-Robotic Autonomous Underwater Vehicle. Front Neurorobot 14: 8.
  38. Ricotti, L., Trimmer, B., Feinberg, A. W., Raman, R., Parker, K. K., Bashir, R., Sitti, M., Martel, S., Dario, P. and Menciassi, A. (2017). Biohybrid actuators for robotics: A review of devices actuated by living cells. Sci Robot 2(12).
  39. Roboroach, The RoboRoach Bundle. https://backyardbrains.com/products/roboroach.
  40. Saha, D., Mehta, D., Atlan, E., Chandak, R., Traner, M., Lo, R., Gupta, P., Singamaneni, S., Chakrabartty, S., and Raman, B. (2020). Explosive sensing with insect-based biorobots. bioRxiv. https://doi.org/10.1101/2020.02.10.940866.
  41. Sanchez, P. J. B., Papaelias, M. and Marquez, F. P. G. (2020). Autonomous underwater vehicles: Instrumentation and measurements. IEEE Instru Meas Mag 23(2): 105-114.
  42. Sato, H., Peeri, Y., Baghoomian, E., Berry, C. W. and Maharbiz, M. M. (2009). Radio-controlled cyborg beetles: A radio- frequency system for insect neural flight control. In: 2009 IEEE 22nd International Conference on Micro Electro Mechanical Systems.
  43. Sato, H. and Maharbiz, M. M. (2010). Recent developments in the remote radio control of insect flight. Front Neurosci 4: 199.
  44. Satterlie, R. A. (2002). Neuronal control of swimming in jellyfish: a comparative story. Can J Zool 80(10): 1654-1669.
  45. Satterlie, R. A. (2011). Do jellyfish have central nervous systems? J Exp Biol 214(Pt8): 1215-1223.
  46. Shanks, A. L. and Graham, W. M. (1987). Orientated swimming in the jellyfish Stomolopus meleagris L. Agassiz (Scyphozoan: Rhizostomida). J Exp Mar Biol Ecol 108(2): 159-169.
  47. Tadepalli, S., Cao, S., Saha, D., Liu, K.-K., Chen, A., Bae, S. hyun, Raman, B., and Singamaneni, S. (2020). Remote-controlled insect navigation using plasmonic nanotattoos. bioRxiv. https://doi.org/10.1101/2020.02.10.942540.
  48. Teoh, Z. E., Phillips, B. T., Becker, K. P., Whittredge, G., Weaver, J. C., Hoberman, C., Gruber, D. F. and Wood, R. J. (2018). Rotary-actuated folding polyhedrons for midwater investigation of delicate marine organisms. Sci Rob 3(20): eaat5276.
  49. Tsang, W. M., Stone, A., Aldworth, Z., Otten, D., Akinwande, A. I., Daniel, T., Hildebrand, J. G., Levine, R. B., and Voldman, J. (2010). Remote control of a cyborg moth using carbon nanotube-enhanced flexible neuroprosthetic probe. In: 2010 IEEE 23rd International Conference on Micro Electro Mechanical Systems.
  50. Valero-Sarmiento, J. M., Reynolds, J., Krystal, A. and Bozkurt, A. (2017). In Vitro Evaluation of an Injectable EEG/ECG Sensor for Wireless Monitoring of Hibernation in Endangered Animal Species. IEEE Sensors J 18(2) 798-808. doi: 10.1109/JSEN.2017.2772844.
  51. Vo Doan, T. T., Tan, M. Y. W., Bui, X. H. and Sato, H. (2018). An ultralightweight and living legged robot. Soft Robotics 5(1): 17-23.
  52. Webster, V. A., Hawley, E. L., Akkus, O., Chiel, H. J. and Quinn, R. D. (2016a). Effect of actuating cell source on locomotion of organic living machines with electrocompacted collagen skeleton. Bioinspir Biomim 11(3): 036012.
  53. Webster, V. A., Chapin, K. J., Hawley, E. L., Patel, J. M., Akkus, O., Chiel, H. J. and Quinn, R. D. (2016b). Aplysia Californica as a novel source of material for biohybrid robots and organic machines. In Biomimetic and Biohybrid Systems 365-374. Springer International Publishing.
  54. WHOI. (2014). HROV Nereus. Woods Hole Oceanographic Institute.
  55. Wynn, R. B., Huvenne, V. A. I., Le Bas, T. P., Murton, B. J., Connelly, D. P., Bett, B. J., Ruhl, H. A., Morris, K. J., Peakall, J., Parsons, D. R., Sumner, E. J., Darby, S. E., Dorrell, R. M. and Hunt, J. E. (2014). Autonomous underwater vehicles (AUVs): Their past, present and future contributions to the advancement of marine geoscience. Mar Geol 352: 451-468.
  56. Xu, N. W. and Dabiri, J. O. (2020). Low-power microelectronics embedded in live jellyfish enhance propulsion. Sci Adv 6(5): eaaz3194.
  57. Xu, N. W., Townsend, J. P., Costello, J. H., Colin, S. P., Gemmell, B. J. and Dabiri, J. O. (2020a). Field testing of biohybrid robotic jellyfish to demonstrate enhanced swimming speeds. Biomimetics 5(4): 64.
  58. Xu, N. W., Lenczewska, O., Wieten, S. E., Federico, C. A. and Dabiri, J. O. (2020b). Ethics of biohybrid robotic jellyfish modification and invertebrate research. Preprint available on Preprints.
  59. Yang, G. Z., Bellingham, J., Dupont, P. E., Fischer, P., Floridi, L., Full, R., Jacobstein, N., Kumar, V., McNutt, M., Merrifield, R., Nelson, B. J., Scassellati, B., Taddeo, M., Taylor, R., Veloso, M., Wang, Z. L. and Wood, R. (2018). The grand challenges of Science Robotics. Sci Robot 3(14).



[背景]尽管海洋很重要并且需要跟踪其不断变化的状况,但仍有80%以上的海洋基本上未被观测和探索(Kim等人,2012; Malve,2016; NOAA,2020a和2020b)。进一步的探索对于揭示海洋中的物理和生物地球化学过程至关重要。这可以增进我们对气候变化的了解,并提供新的食物,药物和能源来源(NOAA,2020b)。

用于海洋监测和探索的传统方法包括遥控飞行器(ROV)和自主水下航行器(AUV)。ROV和AUV已被用来追踪对海洋的人为影响,具有监测生态系统变化的潜力(Wynn等人,2014; Teoh等人,2018; Plum等人,2020; Sanchez等人, 2020)。这些车辆通常可以达到6 km的深度范围(Wynn等,2014),尽管像Nereus这样的例子已经接近11 km(WHOI,2014)。然而,在许多情况下,这些技术仍然受到其功耗的限制,无法在对环境更敏感的海洋地区使用,因为这些地区的车辆可能会产生或造成损害(Page等,2017)。这些区域包括小缝隙,甚至是自然碎屑可能导致车辆损坏的开放空间(Teoh等人,2018; Plum等人,2020)。作为一种替代工具,受生物启发的AUV具有解决此类局限性的优势,包括高功耗和破坏性唤醒信号(Fish,2020年)。在仿生学的更广泛框架内,这些受生物启发的媒介物采用动物的体形和原理。


目前,有两种方法来构建生物杂交系统。首先是整合活细胞或组织(Nawroth等,2012; Morimoto等,2018)。Ricotti等。(2017)描述了这种自下而上的方法,以获取由活细胞激活的设备的全面列表,这些细胞来自细菌和运动细胞,心肌细胞,骨骼肌和昆虫自收缩组织。这些方法具有优势,例如可控性增强和环境传感方面的新潜力,但需要特定的媒介才能生存(Webster等人,2016a和2016b; Morimoto等人,2018)。另一种方法是将电子技术集成到活昆虫中(Sato和Maharbiz,2010; Dirafzoon等人,2015; Latif等人,2016; Li and Sato,2018; Saha等人,2020; Tadepalli等人,2020)和更高阶的动物(Valero-Sarmiento et al 。,2017)。昆虫运动的外部控制引发了越来越多的“半机械人”蟑螂(Holzer和Shimoyama,1997; Bozkurt等,2016; Latif和Bozkurt,2017),甲虫(Sato等,2009; Cao等。,2016; Li等人,2016; Vo Doan等人,2018; Li等人,2018; Cao和Sato,2019)以及飞蛾(Bozkurt等人,2009; Tsang等人,2010)。

对水生动物的控制具有机器人技术的其他潜力。首先,如前所述,生物混合游泳机器人可以将海洋监测扩展到传统的AUV和ROV由于尺寸限制,车辆损坏或对野生生物的影响而无法到达的区域(Teoh等人,2018; Plum等人。,2020年)。其次,使用海洋无脊椎动物可以洞悉动物进化史早期出现的运动模式(Halanych,2015年)。





除了天然水母展现出的能源效率外,生物混合机器人水母还为机器人技术提供了新的优势,因为水母天然存在于多种环境中,包括海平面以下数千米(当前海洋勘探深度的极限),动物可以在没有游泳膀胱的情况下进行横向运动以实现压力平衡(NOAA,2016年)。尽管水母是相对较慢的游泳者,其速度约为厘米/秒,但某些物种(如海豚)可表现出定向游泳,迁移并保持抵御潮流的位置,这对于水下航行器是有用的特征(Shanks和Graham,1987年) )。

该协议中描述的微电子系统可以将游泳速度提高三倍(Xu和Dabiri,2020; Xu等,2020a)。此外,由于AUV和ROV在现场测试中已证明具有很高的速度(以m / s的速度运行),因此传统的和新的生物混合工具都可以协同使用,以用于将来的部署,其中AUV和ROV可以将生物混合机器人水母拖到某些环境中在将生物混合机器人释放到车辆无法到达的较小空间或区域之前。这可以扩展我们当前在海洋监测中的能力,以提供有关自然界和不断变化的气候的新见解。

此外,相对于诸如蝠man和鱼类等动物而言,使用水母的主要优势在于其较低的生物分类顺序和相对易处理的神经系统。尽管值得进行无脊椎动物研究的更多伦理考虑,但水母是无脊椎动物,不受机构动物护理和使用委员会的评估。即使在水母进化枝中,鞘水母(真正的水母)也具有最分散的神经系统组织,包括八个感觉结构和两个神经网:运动神经网(MNN)和弥散神经网(DNN)(Arai,1997; Satterlie ,2011;Ab ,1997)。 Katsuki和Greenspan,2013年)。因此,月水母物种Aurelia aurita具有优势,因为它们是简单的模型生物,没有集中的神经系统,大脑或疼痛感受器。并具有可以使当前水下技术受益的潜在功能。

Aurlia aurita(月亮水母)的生物学

水母是无脊椎动物,具有简​​单的身体结构,包括柔性铃铛和衬在伞下铃铛表面的单层肌肉。具体而言,月水母Aurelia aurita是一种鞘翅目动物,属于真水母,属于水母类,具有水母,在口腔-腹腔轴周围呈放射状对称(Arai,1997年),并具有定向运动的能力,可用于水下航行器( Hamner et al。,1994)。

水母没有中枢神经系统(CNS)或大脑;相反,A。aurita具有分布的,非极化的神经元网络(Arai,1997; Satterlie,2011; Katsuki和Greenspan,2013; Byrne,2017)。这些神经系统包括八个横纹肌肉瘤,一个运动神经网(MNN)和一个弥散神经网(DNN)。横纹肌肉瘤(也称为游泳起搏器或边缘感觉结构)沿钟形边缘均匀分布在凹痕中,并直接激活MNN,MNN随后激活双向的肌肉收缩波。刺激一个纹状体足以激活整个MNN(Passano,1965; Mackie和Meech,1995)。

游泳,水母合同其环状肌肉组织以降低subumbrellar腔的体积,从而排出水和提供用于加速的动力(Satterlie,2002),与从涡结构附加基于抽吸的推进(Gemmell等人, 2015a)。扁圆的水母,例如奥雷利亚(Aurelia),也严重依赖钟形边缘来形成运动的划桨模式(Colin和Costello,2002; Costello等,2020)。

水母主要利用足以维持生存的脉冲推进垂直移动,即被动捕获猎物,而其水平移动很大程度上取决于洋流和海浪(Satterlie,2002)。金黄色葡萄球菌还具有可能有助于非对称推力的radial肌,从而使单个涡流环扭曲以引起转向动作(Gemmell等人,2015 b)。由于被动能量捕获,水母是高效的游泳者,在这种情况下,动物仍以放松状态向前推进而无需增加能量输入(Gemmell等人,2013; Gemmell等人,2018)。



使用这些方法,我们已经确定,无论是在没有人流的情况下(在实验室中)还是在沿海条件下,用户控制水母的游泳速度都可以使游泳速度提高三倍(Xu和Dabiri,2020; Xu等, 2020a)。水母中更快和潜在更有效的游泳方式的存在,对日光藻以外的基础生物的生态学和进化的未来研究产生了影响。结果表明,机器人系统可用于揭示生物体的潜在能力,而生物体可用于海洋环境中的生物杂交能力。此外,与文献中的其他游泳机器人相比,生物混合机器人水母每质量消耗的外部动力更少(Xu和Dabiri,2020年)。这表明生物混合设计可用于提高机器人的能效,以解决机器人技术中的现存挑战,包括功耗和损坏后的再生(Yang等人,2018)。

这项工作描述了构建和实施生物混合型机器人水母的整个过程,并证明了这些构建体可以部署在真实的海洋环境中。这为将来在海洋中的实际应用提供了基础,其最终目标是跟踪气候变化的标志。有关这些协议的使用和执行的全面信息,请参考Xu和Dabiri (2020 )的游泳控制器设计和实验室实验,以及Xu等。(2020a )进行现场实验。


关键字:水母, 海月水母, 机器人学, 生物合成机器人, 游泳, 速度, 海洋监测


1. TinyLily迷你处理器(TinyCircuits,目录号:ASM2101)     

2. TinyLily发光二极管(LED)0402(TinyCircuits,目录号:ASL1001-LR)     

3. 10 mAh超轻型锂聚合物(LiPo)电池(PowerStream Technology,目录号:PGEB201212)     



6.直径254.0μm的铂金棒(AM Systems,目录号:711000)     

7.聚丙烯外壳(例如,直径15毫升的Nunc TM锥形离心管的帽盖为2.11厘米,Thermo Scientific TM ,目录号:339650;以及切成相应圆形的聚丙烯塑料片)     

8.涂有全氟烷氧基烷烃(PFA)的银线,裸线直径为76.2-μm,涂层直径为139.7-μm(AM Systems,目录号:785500)     




热胶枪(例如,无绳热胶枪,Neu Master,目录号:FQ-009)
万用表(例如Fluke 115 Field Technicians Digital Multimeter)
亚克力水箱(Envision Acrylics,Inc.,定制设计,尺寸为1.8 m × 0.9 m × 0.9 m)
隔热的聚苯乙烯泡沫塑料容器(例如,带泡沫托运器的Thermo Chill隔热纸箱,中号,12英寸× 10英寸× 7英寸,Polar Tech Industries,目录号:227C)
Cyber-shot DSC-RX100(Sony,目录号:DSCRX100 / B)
背景板(例如,黑色泡沫板,36 × 48”,Uline,目录号:S-19381和白色泡沫板20 × 30 × 3/16”或5 mm,海军企鹅)


Arduino(Arduino,https://www.arduino.cc/ )
MATLAB(MathWorks,https ://www.mathworks.com/ )


根据制造商的说明,使用Arduino将TinyLily微控制器编程为所需的信号频率,以将代码上传到板上。图2A显示了方波信号,振幅为3.7 V,脉冲宽度为10 ms,并且频率(f )和周期(T )可调节。
注意:图2B显示了一个方波波形的Arduino代码示例,脉冲宽度为10 ms。将“ TIME”(以橙色突出显示)替换为所需的以毫秒为单位的周期(T),即,对于0.50 Hz的频率(f),T = 2,000 ms。对于双电极系统,请选择两个刺激针,例如图2B中示例代码所示的针2和3。

图2。电刺激的水母。A.刺激Aurelia aurita月水母中肌肉收缩的方波示意图。信号放大e为3.7 V,脉冲宽度为10 ms。可以根据需要调整信号频率(f)和周期(T)。B.使用两个刺激针,刺激针2和3(与图3B中的黄色针相对应)执行(A)中信号的示例代码。要调整信号频率,请将“ TIME”(橙色突出显示)替换为以毫秒为单位的周期。


注意:使用较高的温度(例如450 °C或更高)以改善焊接过程。

图3.水母游泳控制器的微电子组件。A.一种电极,包括以下组件:红色TinyLily LED,弯曲成鱼钩状的焊接铂丝头,焊接PFA涂层的银线(如B所示,通过黄色和黑色引脚连接到微控制器),和热熔胶涂层以防水。B. TinyLily微控制器,带有两个标记为黄色的激励引脚(引脚2和3),两个接地引脚分别为黑色和青色以及一个电源引脚为红色。照片来源:Tiny Circuits。C.锂聚合物电池(Powerstream),当连接到B中的红色和蓝色引脚时有效。



将LiPo电池的正极和负极(图3C)焊接到Tiny Lily的相应端子上(在图3B中标记为红色和蓝色)。





嵌入的最佳位置是从动物的底部朝向边缘。因为边缘组织很薄,所以将电极嵌入边缘(半径R)和中途(半径R / 2)之间的径向距离就足够了。

将速溶海盐混合物添加至所需的盐度(35 ppt)。


设置的照相机,例如。,1920 ×在对60帧1080分辨率的Cyber-shot DSC-RX100,如在图5中示出以记录动物游泳视频。

图5.垂直游泳实验的实验室设置。用于计算生物混合机器人水母的垂直游泳速度的实验室实验设置包括以下组件:一个1.8米高的丙烯酸水箱,里面装有盐水(35 ppt),背板(黑色或白色)贴在水箱的后面板上,生物混合机器人水母(向下游动,从水箱顶部开始),水箱底部的可选网(便于从实验后移走水母)和摄像头来录制视频。黄色框突出显示了水箱中动物远离水箱侧面,顶部和底部的区域,可以最大程度地减少壁效应的干扰。


重复步骤C7至C13,直到测试所有实验参数(例如,频率从0.25到1.00 Hz以及在0 Hz时没有活动游泳控制器的控制案例),如Xu和Dabiri (2020)所述。完成所有试验后,继续进行程序E的实验后步骤。



将摄像机放在防水塑料外壳中,例如,将Gates AX100水下机壳中的Sony AX100摄像机。
如图6A所示,让两名SCUBA潜水员准备好装备并入水(标记为“ Divers 1 and 2”)。




上岸人员:将绳索的另一端系到船坞或其他表面结构上,确保绳索拉紧以最大程度地减少绳索的运动。绳索应该在录制的视频的背景中可见以进行数据分析(30 fps时为1,920 × 1,080分辨率)。
上岸人员:取下盖子,将装有生物混合机器人水母之一的塑料容器交给Diver 2,如图6D所示。
潜水员1:录制生物混合机器人水母的视频,注意尽量减少潜水员和照相机的运动,并使绳索标记在背景中可见。尽可能跟踪海底到海面(1.6 m)的水母,并在相机的深度轴上(图像平面内外)以最小的运动。
岸上人士:要获得控制箱,请切断电极丝,并注意确保所有微电子器件仍牢固地嵌入动物体内。将生物混合型机器人水母放入塑料容器中,送回Diver 2。
对照案例被定义为试验,其中嵌入了游泳控制器,但是无效(0 Hz)来确定每只动物的基线游泳频率和速度。
对其他动物重复步骤D6至D11 。完成所有试验后,继续进行程序E的实验后步骤。



找到所有帧(时间t)的水母游泳控制器(图8A)的(x,y)重心,以像素为单位:(cap x,t ,cap y,t )


计算每帧的垂直位移(质心与初始起始质心之间的距离):d t = cap y,t – cap y,0

在背景中找到绳索标记的(x,y)重心,以像素为单位:(标记x,t ,标记y,t )。


确定水母相对于背景绳索标记的(x,y)重心:(norm_cap x,t ,norm_cap y,t )。
注意:由于MATLAB的图像原点定义为左上角,因此方程式应为norm_cap x,t = cap x,t – x分量的标记x,t和norm_cap y,t =标记y,t – cap y ,t为y分量。

计算每帧的垂直位移(质心与初始起始质心之间的距离):d t = norm_ cap y,t – norm_ cap y,0 。

使用每对后续时间步长之间的距离(d t + 1 – d t )乘以相机帧速率(例如,在实验室实验中为60 fps,在野外实验中为30 fps ),计算出不同的游泳速度。
计算平均游泳速度(v )。
可选:计算归一化的游泳速度,或熟练程度,通过由动物(的直径除以绝对游泳速度d ):v范数= V / d 。
通过将平均游泳速度(如前所述,用绝对或标准化的速度)除以基线游泳速度(对照例的游泳速度,以不活动的游泳控制器启动动物为0 Hz)来计算增强因子。
注意:Xu和Dabiri (2020 )中的实验室实验以及Xu等人的现场实验描述了其他信息。(2020a )。


我们非常感谢Cabrillo海洋水族馆提供的Aurelia aurita medusae,感谢Angela Fan展示了与该手稿有关的科学艺术品。这项工作得到了美国国家自然科学基金会研究生研究奖学金的部分支持,该研究项目的奖学金编号为DGE-1147470,该奖项于2015年授予NWX。方案附有原始手稿Xu和Dabiri(2020)以及Xu等人的补充。(2020a)。 






Bozkurt,A.,Lal,A.和Gilmour,R.(2009)。提升辅助昆虫生物机器人的空中和地面运动控制。国际工程学年会(IEEE Eng Med Biol Soc),2009年:2058-2061。
曹F.,张C.,Choo HY和Sato H.(2016)。昆虫计算机混合腿式机器人,用户可调节速度,步长和步态。JR Soc接口13(116):20160060。
Cao F.和Sato,H.(2019)。昆虫计算机混合机器人通过用各向同性的人工腿棘代替各向异性的自然腿棘,从而实现了自然界罕见的行走步态。IEEE Trans机械手35(4):1034-1038。
达尔文(1859)。通过自然选择或保护生命中受支持的种族来保护物种的起源。伦敦:约翰·默里(John Murray)。
SP Colin和JH Costello(2002)。六个并发的水产美杜鹃的形态,游泳性能和推进方式。J Exp Biol 205(Pt 3):427-437。
Costello,JH,Colin,SP,Dabiri,JO,Gemmell,BJ,Lucas,KN和Sutherland,KR(2020)。水母游泳的水动力。Ann Rev Mar Sci 。
鱼,FE(2020年)。与目前的AUV技术相比,水生动物作为生物启发无人机的模型的优势。Bioinspir Biomim 15(2):025001。
Gemmell,BJ,Colin,SP,Costello,JH和Dabiri,JO(2015a)。基于吸力的推进力是有效动物游泳的基础。Nat Commun 6:8790。
Gemmell,BJ,Troolin,DR,Costello,JH,Colin,SP和Satterlie,RA(2015b)。控制涡流环的可操纵性。JR Soc接口12(108):20150389。
Gemmell,BJ,Colin,SP和Costello,JH(2018)。游泳水母中被动能量回收的广泛利用。J Exp Biol 221(Pt 1)。
哈兰奇(KM)(2015)。ten鳍血统比海绵还老吗?那是不对的!可以吗 Ĵ精通生物学218(4):592 - 597。
Hamner,WM,Hamner,PP和Strand,SW(1994)。Aurelia aurita (Scyphozoa)的太阳罗盘迁移:不列颠哥伦比亚省Saanich Inlet的种群保留和繁殖。 Mar Biol 119 (3):347-356。
Holzer,R.和Shimoyama,I.(1997)。通过电刺激对生物机器人系统的运动控制。于:1997年IEEE / RSJ国际智能机器人与系统国际会议论文集。适用于实际应用的创新机器人技术。IROS '97。
Katsuki,T.和Greenspan,RJ(2013)。水母的神经系统。Curr Biol 23(14):R592-594。
Latif,T.和Bozkurt,A.(2017年)。蟑螂生物机器人:朝着可靠性和控制优化的方向发展。IEEE Pulse 8(5):27-30。
Latif,T.,Whitmire,E.,Novak,T.和Bozkurt,A.(2016)。用于搜索和救援生物机器人的声音定位传感器。IEEE Sens J 16(10):3444-3453。
Li,Y.和Sato,H.(2018)。昆虫计算机混合机器人。Mol Front J 02(01):30-42。
Li Y,Wu,J. and Sato,H.(2018年)。基于反馈控制的飞行昆虫机器混合机器人导航。软机器人5(4):365-374。
Li Y,Cao F.,Thang Vo Doan,T.和Sato,H.(2016)。微型无线惯性测量单元测量鞘翅目飞行中的受控倾斜转弯。Bioinspir Biomim 11(5):056018。
Y.Morimoto,H.Onoe和S.Takeuchi(2018)。由一对拮抗的骨骼肌组织提供动力的生物混合机器人。科学机器人3(18):eaat4440 。
Nawroth,JC,Lee,H.,Feinberg,AW,Ripplinger,CM,McCain,ML,Grosberg,A.,Dabiri,JO和Parker,KK(2012)。具有仿生推进作用的组织工程水母。Nat Biotechnol 30(8):792-797。
NOAA。(2016)。NOAA船Okeanos Explorer:2016概述:NOAA海洋探索与研究办公室。国家海洋和大气管理局。美国商务部。
Page,BR,Ziaeefard,S.,Pinar,AJ和Mahmoudian,N.(2017)。高度机动的低成本水下滑翔机:设计和开发。IEEE Robot Auto Let 2(1):344-349。
里科蒂(Licotti,L.),特里默(B. 。机器人技术的生物混合执行器:对由活细胞驱动的设备的评论。科学机器人2(12)。
萨哈(Saha),梅塔(Mehta),阿特兰(Atlan),尚达克(Chandak),特拉纳(M.),罗(Lo),古普塔(Gupta),P。辛加马尼尼(Singamaneni),查克拉巴蒂(Chakrabartty)和拉曼(B.) (2020)。基于昆虫的生物机器人的爆炸感测。bioRxiv。https://doi.org/10.1101/2020.02.10.940866。
桑切斯(Sanchez),PJB,帕帕利亚斯(M.Papaelias)和马克斯(Marquez),FPG(2020)。自主水下航行器:仪表和测量。IEEE Instru Meas Mag 23(2):105-114。
Satterlie,RA(2002)。在水母中游泳的神经元控制:一个比较故事。Can J Zool 80(10):1654-1669。
Satterlie,RA(2011)。水母有中枢神经系统吗?J Exp Biol 214(Pt8):1215-1223。
尚克斯(美国)和格雷厄姆(美国)(1987)。在水母Stomolopus meleagris L. Agassiz(Scyphozoan :Rhizostomida)中定向游泳。J Exp Mar生物化学杂志108 (2):159-169。
Tadepalli,S.,Cao,S.,Saha,D.,Liu,K.-K.,Chen,A.,Bae,S. hyun,Raman,B.,and Singamaneni,S.(2020)。使用等离激元纳米纹身的遥控昆虫导航。bioRxiv。https://doi.org/10.1101/2020.02.10.942540。
Teoh,ZE,Phillips,BT,Becker,KP,Whittredge,G.,Weaver,JC,Hoberman,C.,Gruber,DF and Wood,RJ(2018)。旋转驱动的折叠多面体,用于在水中进行精细海洋生物的调查。Sci Rob 3(20):eaat5276。
Tsang,WM,Stone,A.,Aldworth,Z.,Otten,D.,Akinwande,AI,Daniel,T.,Hildebrand,JG,Levine,RB,and Voldman,J.(2010)。使用碳纳米管增强的柔性神经假体探针远程控制机器人飞蛾。在:2010 IEEE第23届微机电系统国际会议上。
Valero-Sarmiento,JM,Reynolds,J.,Krystal,A.和Bozkurt,A.(2017年)。对用于濒危动物物种的休眠无线监控的可注射EEG / ECG传感器的体外评估。IEEE传感器J 18(2)798-808。doi:10.1109 / JSEN.2017.2772844。
Vo Doan,TT,Tan,MYW,Bui,XH和Sato,H.(2018)。超轻量级活腿机器人。软机器人5(1):17-23。
弗吉尼亚州韦伯斯特(Webster),霍利(Hawley),EL,阿克库斯(Okkus),印第安纳州基尔(Chiel)和RD奎因(2016a)。致动细胞源对具有电致密化胶原蛋白骨架的有机生物机器运动的影响。Bioinspir Biomim 11(3):036012。
韦伯斯特(VA),查平(Chapin),肯尼迪(KJ),霍利(Hawley),埃尔(EL),帕特尔(Patel),杰米(JM),阿克库斯(O.),基尔(Chiel),约翰逊(HJ)和奎因(RD)(2016b)。Aplysia Californica是生物混合机器人和有机机械的新型材料来源。在仿生和生物混合系统365-374中。施普林格国际出版社。
WHOI。(2014)。HROV Nereus 。伍兹霍尔海洋研究所。
Wynn,RB,Huvenne,VAI,Le Bas,TP,Murton,BJ,Connelly,DP,Bett,BJ,Ruhl,HA,Morris,KJ,Peakall,J.,Parsons,DR,Sumner,EJ,Darby,SE, RM的Dorrell和JE的Hunt(2014)。自主水下航行器(AUV):它们对海洋地球科学发展的过去,现在和将来的贡献。Mar Geol 352:451-468。
Xu,NW和Dabiri,JO(2020)。嵌入在活水母中的低功率微电子器件增强了推进力。Sci Adv 6(5):eaaz3194。
Yang,GZ,Bellingham,J.,Dupont,PE,Fischer,P.,Floridi,L.,Full,R.,Jacobstein,N.,Kumar,V.,McNutt,M.,Merrifield,R.,Nelson, BJ,Scassellati,B.,Taddeo,M.,Taylor,R.,Veloso,M.,Wang,ZL和Wood,R.(2018)。科学机器人的巨大挑战。科学机器人3(14)。
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引用: Readers should cite both the Bio-protocol article and the original research article where this protocol was used:
  1. Xu, N. W., Townsend, J. P., Costello, J. H., Colin, S. P., Gemmell, B. J. and Dabiri, J. O. (2021). Developing Biohybrid Robotic Jellyfish (Aurelia aurita) for Free-swimming Tests in the Laboratory and in the Field. Bio-protocol 11(7): e3974. DOI: 10.21769/BioProtoc.3974.
  2. Xu, N. W. and Dabiri, J. O. (2020). Low-power microelectronics embedded in live jellyfish enhance propulsion. Sci Adv 6(5): eaaz3194.

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