Flexible margin kinematics and vortex formation of Aurelia aurita and Robojelly

A resonant squid-inspired robot unlocks biological propulsive efficiency

Elasticity has been linked to the remarkable propulsive efficiency of pulse-jet animals such as the squid and jellyfish, but reports that quantify the underlying dynamics or demonstrate its application in robotic systems are rare. This work identifies the pulse-jet propulsion mode used by these animals as a coupled mass-spring-mass oscillator, enabling the design of a flexible self-propelled robot. We use this system to experimentally demonstrate that resonance greatly benefits pulse-jet swimming speed and efficiency, and the robot's optimal cost of transport is found to match that of the most efficient biological swimmers in nature, such as the jellyfish Aurelia aurita The robot also exhibits a preferred Strouhal number for efficient swimming, thereby bridging the gap between pulse-jet propulsion and established findings in efficient fish swimming. Extensions of the current robotic framework to larger amplitude oscillations could combine resonance effects with optimal vortex formation to further increase propulsive performance and potentially outperform biological swimmers altogether.

Field Testing of Biohybrid Robotic Jellyfish to Demonstrate Enhanced Swimming Speeds

Biohybrid robotic designs incorporating live animals and self-contained microelectronic systems can leverage the animals’ own metabolism to reduce power constraints and act as natural chassis and actuators with damage tolerance. Previous work established that biohybrid robotic jellyfish can exhibit enhanced speeds up to 2.8 times their baseline behavior in laboratory environments. However, it remains unknown if the results could be applied in natural, dynamic ocean environments and what factors can contribute to large animal variability. Deploying this system in the coastal waters of Massachusetts, we validate and extend prior laboratory work by demonstrating increases in jellyfish swimming speeds up to 2.3 times greater than their baseline, with absolute swimming speeds up to 6.6 ± 0.3 cm s⁻¹. These experimental swimming speeds are predicted using a hydrodynamic model with morphological and time-dependent input parameters obtained from field experiment videos. The theoretical model can provide a basis to choose specific jellyfish with desirable traits to maximize enhancements from robotic manipulation. With future work to increase maneuverability and incorporate sensors, biohybrid robotic jellyfish can potentially be used to track environmental changes in applications for ocean monitoring.

Low-power microelectronics embedded in live jellyfish enhance propulsion

Artificial control of animal locomotion has the potential to simultaneously address longstanding challenges to actuation, control, and power requirements in soft robotics. Robotic manipulation of locomotion can also address previously inaccessible questions about organismal biology otherwise limited to observations of naturally occurring behaviors. Here, we present a biohybrid robot that uses onboard microelectronics to induce swimming in live jellyfish. Measurements demonstrate that propulsion can be substantially enhanced by driving body contractions at an optimal frequency range faster than natural behavior. Swimming speed can be enhanced nearly threefold, with only a twofold increase in metabolic expenditure of the animal and 10 mW of external power input to the microelectronics. Thus, this biohybrid robot uses 10 to 1000 times less external power per mass than other aquatic robots reported in literature. This capability can expand the performance envelope of biohybrid robots relative to natural animals for applications such as ocean monitoring.

From single neurons to behavior in the jellyfish Aurelia aurita

Jellyfish nerve nets provide insight into the origins of nervous systems, as both their taxonomic position and their evolutionary age imply that jellyfish resemble some of the earliest neuron-bearing, actively-swimming animals. Here, we develop the first neuronal network model for the nerve nets of jellyfish. Specifically, we focus on the moon jelly Aurelia aurita and the control of its energy-efficient swimming motion. The proposed single neuron model disentangles the contributions of different currents to a spike. The network model identifies factors ensuring non-pathological activity and suggests an optimization for the transmission of signals. After modeling the jellyfish's muscle system and its bell in a hydrodynamic environment, we explore the swimming elicited by neural activity. We find that different delays between nerve net activations lead to well-controlled, differently directed movements. Our model bridges the scales from single neurons to behavior, allowing for a comprehensive understanding of jellyfish neural control of locomotion.

FludoJelly: Experimental Study on Jellyfish-Like Soft Robot Enabled by Soft Pneumatic Composite (SPC)

Several bio-inspired underwater robots have been demonstrated in the last few years that can horizontally swim using different smart actuators. However, very few works have been presented on robots which can swim vertically, have a payload and resemble a jellyfish-like creature. In this work, we present the design, fabrication, and performance characterization of a new tethered robotic jellyfish, which is based on inflatable soft pneumatic composite (SPC) actuators. These soft actuators use compressed air to expand and contract, which help the robot to swim vertically in water. The soft actuators consist of elastomeric air chambers and very thin steel springs, which contribute to gaining faster motion of the biomimetic robot. A prototype of 220 mm in diameter and consisting of eight actuating units was fabricated and tested underwater in a fish tank. It reached a height of 400 mm within 2.5 s while carrying a dead weight of 100 g when tested at 70 psi (483 kPa) pressure. This high performance (160 mm/s on average speed) suggests that faster motion with a payload can be achieved by using SPC actuators. The inflatable structures help to flap the bell segments as well as in buoyancy effect for rapid vertical motion. The major achievement of this work is the ability to demonstrate a novel use of inflatable structures and biomimetic flapping wings for fast motion in water. The experimental and deduced data from this work can be used for the design of future small unmanned underwater vehicles (UUVs). This work adds a new robot to the design space of biomimetic jellyfish-like soft robots. Such kind of vehicle design might also be useful for transporting objects underwater effectively.

An Overview of Shape Memory Alloy-Coupled Actuators and Robots

The one-dimensional deformation of shape memory alloy (SMA) wires and springs can be implemented into different types of functional structures with three-dimensional deformations. These structures can be classified based on the type of structure and how the SMA element has been implemented into the following categories: rigid mechanical joints, semi-rigid flexural hinges, SMA elements externally attached to a soft structure, and embedded into the soft structure. These structures have a wide range of properties and implementation requirements, and they have been used to produce a variety of robots with rigid and soft motions. The different research efforts to develop actuators and robots related to each type of structure are presented along with their respective strengths and weaknesses. A model is then developed to discuss the performance and applicability of SMA wires versus SMA springs for actuators with a polymeric matrix to see the effect of each type of SMA on the selection of design parameters. A comparison of the different types of structures and the applicability of different types of SMA elements for different types of structures is then presented.

Cyanea capillata bell kinematics analysis through corrected in situ imaging and modeling using strategic discretization techniques

Obtaining accurate kinematic data of animals is essential for many biological studies and bio-inspired engineering. Many animals, however, are either too large or too delicate to transport to controlled environments where accurate kinematic data can be easily obtained. Often, in situ recordings are the only means available but are often subject to multi-axis motion and relative magnification changes with time leading to large discrepancies in the animal kinematics. Techniques to compensate for these artifacts were applied to a large jellyfish, Cyanea capillata, freely swimming in ocean waters. The bell kinematics were captured by digitizing exumbrella profiles for two full swimming cycles. Magnification was accounted for by tracking a reference point on the ocean floor and by observing the C. capillata exumbrella arclength in order to have a constant scale through the swimming cycles. A linear fit of the top bell section was used to find the body angle with respect to the camera coordinate system. Bell margin trajectories over two swimming cycles confirmed the accuracy of the correction techniques. The corrected profiles were filtered and interpolated to provide a set of time-dependent points along the bell. Discrete models of the exumbrella were used to analyze the bell kinematics. Exumbrella discretization was conducted using three different methods. Fourier series were fitted to the discretized models and subsequently used to analyze the bell kinematics of the C. capillata. The analysis showed that the bell did not deform uniformly over time with different segments lagging behind each other. Looping of the bell trajectory between contraction and relaxation was also present through most of the exumbrella. The bell margin had the largest looping with an outer path during contraction and inner path during relaxation. The subumbrella volume was approximated based on the exumbrella kinematics and was found to increase during contraction.