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 swimming speed


Feedback Control of a Single-Tail Bioinspired 59-mg Swimmer

arXiv.org Artificial Intelligence

We present an evolved steerable version of the single-tail Fish-&-Ribbon-Inspired Small Swimming Harmonic roBot (FRISSHBot), a 59-mg biologically inspired swimmer, which is driven by a new shape-memory alloy (SMA)-based bimorph actuator. The new FRISSHBot is controllable in the two-dimensional (2D) space, which enabled the first demonstration of feedback-controlled trajectory tracking of a single-tail aquatic robot with onboard actuation at the subgram scale. These new capabilities are the result of a physics-informed design with an enlarged head and shortened tail relative to those of the original platform. Enhanced by its design, this new platform achieves forward swimming speeds of up to 13.6 mm/s (0.38 Bl/s), which is over four times that of the original platform. Furthermore, when following 2D references in closed loop, the tested FRISSHBot prototype attains forward swimming speeds of up to 9.1 mm/s, root-mean-square (RMS) tracking errors as low as 2.6 mm, turning rates of up to 13.1 °/s, and turning radii as small as 10 mm.


Optimizing Metachronal Paddling with Reinforcement Learning at Low Reynolds Number

arXiv.org Machine Learning

Metachronal paddling is a swimming strategy in which an organism oscillates sets of adjacent limbs with a constant phase lag, propagating a metachronal wave through its limbs and propelling it forward. This limb coordination strategy is utilized by swimmers across a wide range of Reynolds numbers, which suggests that this metachronal rhythm was selected for its optimality of swimming performance. In this study, we apply reinforcement learning to a swimmer at zero Reynolds number and investigate whether the learning algorithm selects this metachronal rhythm, or if other coordination patterns emerge. We design the swimmer agent with an elongated body and pairs of straight, inflexible paddles placed along the body for various fixed paddle spacings. Based on paddle spacing, the swimmer agent learns qualitatively different coordination patterns. At tight spacings, a back-to-front metachronal wave-like stroke emerges which resembles the commonly observed biological rhythm, but at wide spacings, different limb coordinations are selected. Across all resulting strokes, the fastest stroke is dependent on the number of paddles, however, the most efficient stroke is a back-to-front wave-like stroke regardless of the number of paddles.


A Novel Aerial-Aquatic Locomotion Robot with Variable Stiffness Propulsion Module

arXiv.org Artificial Intelligence

In recent years, the development of robots capable of operating in both aerial and aquatic environments has gained significant attention. This study presents the design and fabrication of a novel aerial-aquatic locomotion robot (AALR). Inspired by the diving beetle, the AALR incorporates a biomimetic propulsion mechanism with power and recovery strokes. The variable stiffness propulsion module (VSPM) uses low melting point alloy (LMPA) and variable stiffness joints (VSJ) to achieve efficient aquatic locomotion while reduce harm to marine life. The AALR's innovative design integrates the VSPM into the arms of a traditional quadrotor, allowing for effective aerial-aquatic locomotion. The VSPM adjusts joint stiffness through temperature control, meeting locomotion requirements in both aerial and aquatic modes. A dynamic model for the VSPM was developed, with optimized dimensional parameters to increase propulsion force. Experiments focused on aquatic mode analysis and demonstrated the AALR's swimming capability, achieving a maximum swimming speed of 77 mm/s underwater. The results confirm the AALR's effective performance in water environment, highlighting its potential for versatile, eco-friendly operations.


Computational and experimental design of fast and versatile magnetic soft robotic low Re swimmers

arXiv.org Artificial Intelligence

Miniaturized magnetic soft robots have shown extraordinary capabilities of contactless manipulation, complex path maneuvering, precise localization, and quick actuation, which have equipped them to cater to challenging biomedical applications such as targeted drug delivery, internal wound healing, and laparoscopic surgery. However, despite their successful fabrication by several different research groups, a thorough design strategy encompassing the optimized kinematic performance of the three fundamental biomimetic swimming modes at miniaturized length scales has not been reported till now. Here, we resolve this by designing magnetic soft robotic swimmers (MSRSs) from the class of helical and undulatory low Reynolds number (Re) swimmers using a fully coupled, experimentally calibrated computational fluid dynamics model. We study (and compare) their swimming performance, and report their steady-state swimming speed for different non-dimensional numbers that capture the competition by magnetic loading, non-linear elastic deformation and viscous solid-fluid coupling. We investigate their stability for different initial spatial orientations to ensure robustness during real-life applications. Our results show that the helical 'finger-shaped' swimmer is, by far, the fastest low Re swimmer in terms of body lengths per cycle, but that the undulatory 'carangiform' swimmer proved to be the most versatile, bi-directional swimmer with maximum stability.


Design of a Double-joint Robotic Fish Using a Composite Linkage

arXiv.org Artificial Intelligence

Robotic fish is one of the most promising directions of the new generation of underwater vehicles. Traditional biomimetic fish often mimic fish joints using tandem components like servos, which leads to increased volume, weight and control complexity. In this paper, a new double-joint robotic fish using a composite linkage was designed, where the propulsion mechanism transforms the single-degree-of-freedom rotation of the motor into a double-degree-of-freedom coupled motion, namely caudal peduncle translation and caudal fin rotation. Motion analysis of the propulsion mechanism demonstrates its ability to closely emulate the undulating movement observed in carangiform fish. Experimental results further validate the feasibility of the proposed propulsion mechanism. To improve propulsion efficiency, an analysis is conducted to explore the influence of swing angle amplitude and swing frequency on the swimming speed of the robotic fish. This examination establishes a practical foundation for future research on such robotic fish systems.


Morphing median fin enhances untethered bionic robotic tuna's linear acceleration and turning maneuverability

arXiv.org Artificial Intelligence

Median fins of fish-like swimmers play a crucial role in linear acceleration and maneuvering processes. However, few research focused on untethered robotic fish experiments. Imitating the behaviour of real tuna, we developed a free-swimming bionic tuna with a foldable dorsal fin. The erection of dorsal fin, at proper conditions, can reduce head heave by 50%, enhance linear acceleration by 15.7%, increase turning angular velocity by 32.78%, and turning radius decreasing by 33.13%. Conversely, erecting the dorsal fin increases the wetted surface area, resulting in decreased maximum speed and efficiency during steady swimming phase. This finding partially explains why tuna erect their median fins during maneuvers or acceleration and fold them afterward to reduce drag. In addition, we verified that folding the median fins after acceleration does not significantly affect locomotion efficiency. This study supports the application of morphing median fins in undulating underwater robots and helps to further understand the impact of median fins on fish locomotion.


Snapp: An Agile Robotic Fish with 3-D Maneuverability for Open Water Swim

arXiv.org Artificial Intelligence

Fish exhibit impressive locomotive performance and agility in complex underwater environments, using their undulating tails and pectoral fins for propulsion and maneuverability. Replicating these abilities in robotic fish is challenging; existing designs focus on either fast swimming or directional control at limited speeds, mainly within a confined environment. To address these limitations, we designed Snapp, an integrated robotic fish capable of swimming in open water with high speeds and full 3-dimensional maneuverability. A novel cyclic-differential method is layered on the mechanism. It integrates propulsion and yaw-steering for fast course corrections. Two independent pectoral fins provide pitch and roll control. We evaluated Snapp in open water environments. We demonstrated significant improvements in speed and maneuverability, achieving swimming speeds of 1.5 m/s (1.7 Body Lengths per second) and performing complex maneuvers, such as a figure-8 and S-shape trajectory. Instantaneous yaw changes of 15$^{\circ}$ in 0.4 s, a minimum turn radius of 0.85 m, and maximum pitch and roll rates of 3.5 rad/s and 1 rad/s, respectively, were recorded. Our results suggest that Snapp's swimming capabilities have excellent practical prospects for open seas and contribute significantly to developing agile robotic fishes.


Robot motor learning shows emergence of frequency-modulated, robust swimming with an invariant Strouhal-number

arXiv.org Artificial Intelligence

Fish locomotion emerges from a diversity of interactions among deformable structures, surrounding fluids and neuromuscular activations, i.e., fluid-structure interactions (FSI) controlled by fish's motor systems. Previous studies suggested that such motor-controlled FSI may possess embodied traits. However, their implications in motor learning, neuromuscular control, gait generation, and swimming performance remain to be uncovered. Using robot models, we studied how swimming behaviours emerged from the FSI and the embodied traits. We developed modular robots with various designs and used Central Pattern Generators (CPGs) to control the torque acting on robot body. We used reinforcement learning to learn CPG parameters to maximize the swimming speed. The results showed that motor frequency converged faster than other parameters, and the emergent swimming gaits were robust against disruptions applied to motor control. For all robots and frequencies tested, swimming speed was proportional to the mean undulation velocity of body and caudal-fin combined, yielding an invariant, undulation-based Strouhal number. The Strouhal number also revealed two fundamental classes of undulatory swimming in both biological and robotic fishes. The robot actuators also demonstrated diverse functions as motors, virtual springs, and virtual masses. These results provide novel insights into the embodied traits of motor-controlled FSI for fish-inspired locomotion.


To swim like a tuna, robotic fish need to change how stiff their tails are in real time

Robohub

Underwater vehicles haven't changed much since the submarines of World War II. They're rigid, fairly boxy and use propellers to move. And whether they are large manned vessels or small robots, most underwater vehicles have one cruising speed where they are most energy efficient. Fish take a very different approach to moving through water: Their bodies and fins are very flexible, and this flexibility allows them to interact with water more efficiently than rigid machines. Researchers have been designing and building flexible fishlike robots for years, but they still trail far behind real fish in terms of efficiency.