The use of machine learning techniques in the development of microscopic swimmers has drawn considerable attention in recent years. In particular, reinforcement learning has been shown useful in enabling swimmers to learn effective propulsion strategies through its interactions with the surroundings. In this work, we apply a reinforcement learning approach to identify swimming gaits of a multi-link model swimmer. The swimmer consists of multiple rigid links connected serially with hinges, which can rotate freely to change the relative angles between neighboring links. Purcell [“Life at low Reynolds number,” Am. J. Phys. 45, 3 (1977)] demonstrated how the particular case of a three-link swimmer (now known as Purcell's swimmer) can perform a prescribed sequence of hinge rotation to generate self-propulsion in the absence of inertia. Here, without relying on any prior knowledge of low-Reynolds-number locomotion, we first demonstrate the use of reinforcement learning in identifying the classical swimming gaits of Purcell's swimmer for case of three links. We next examine the new swimming gaits acquired by the learning process as the number of links increases. We also consider the scenarios when only a single hinge is allowed to rotate at a time and when simultaneous rotation of multiple hinges is allowed. We contrast the difference in the locomotory gaits learned by the swimmers in these scenarios and discuss their propulsion performance. Taken together, our results demonstrate how a simple reinforcement learning technique can be applied to identify both classical and new swimming gaits at low Reynolds numbers.
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Comparative Model Evaluation with a Symmetric Three-Link Swimming Robot
In this paper we present swimming and modeling for Trident, a three-link lamprey-inspired robot that is able to climb on flat smooth walls. We explore two gaits proposed to work for linear swimming, and three gaits for turning maneuvers. We compare the experimental results obtained from these swimming experiments with two different reduced order fluid interaction models, one a previously published potential flow model, and the other a slender cylinder model we developed. We find that depending on the the parameters of swimming chosen, we are able to move forward, backward and sideways with a peak speed of 2.5 cm/s. We identify the conditions when these models apply and aspects that will require additional complexity.
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- Award ID(s):
- 1935278
- PAR ID:
- 10385269
- Date Published:
- Journal Name:
- 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Page Range / eLocation ID:
- 2672-2678
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract In animal and robot swimmers of body and caudal fin (BCF) form, hydrodynamic thrust is mainly produced by their caudal fins, the stiffness of which has profound effects on both thrust and efficiency of swimming. Caudal fin stiffness also affects the motor control and resulting swimming gaits that correspond to optimal swimming performance; however, their relationship remains scarcely explored. Here using magnetic, modular, undulatory robots (
μ Bots), we tested the effects of caudal fin stiffness on both forward swimming and turning maneuver. We developed six caudal fins with stiffness of more than three orders of difference. For aμ Bot equipped with each caudal fin (andμ Bot absent of caudal fin), we applied reinforcement learning in experiments to optimize the motor control for maximizing forward swimming speed or final heading change. The motor control ofμ Bot was generated by a central pattern generator for forward swimming or by a series of parameterized square waves for turning maneuver. In forward swimming, the variations in caudal fin stiffness gave rise to three modes of optimized motor frequencies and swimming gaits including no caudal fin (4.6 Hz), stiffness <10−4Pa m4(∼10.6 Hz) and stiffness >10−4Pa m4(∼8.4 Hz). Swimming speed, however, varied independently with the modes of swimming gaits, and reached maximal at stiffness of 0.23 × 10−4Pa m4, with theμ Bot without caudal fin achieving the lowest speed. In turning maneuver, caudal fin stiffness had considerable effects on the amplitudes of both initial head steering and subsequent recoil, as well as the final heading change. It had relatively minor effect on the turning motor program except for theμ Bots without caudal fin. Optimized forward swimming and turning maneuver shared an identical caudal fin stiffness and similar patterns of peduncle and caudal fin motion, suggesting simplicity in the form and function relationship inμ Bot swimming. -
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Fish locomotion emerges from diverse 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 the embodied traits in fish-inspired swimming. 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 for maximizing 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 were also demonstrated to function as motors, virtual springs and virtual masses. These results provide novel insights in understanding fish-inspired locomotion.
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Locomotion is an important behavior in the life history of animals and is characterized by discrete gaits, which may be adopted for optimal energetic efficiency, fatigue resistance, or maneuverability. We evaluated the kinematics and electromyography of Bluegill Sunfish (Lepomis macrochirus) swimming at different gaits to evaluate which factors might influence gait choice. When placed in the flume, Bluegill adopted a steady swimming gait until speeds reached 2.0 BL/s. When swimming volitionally, either in a laboratory pool or the field, Bluegill adopted an intermittent swimming gait (burst phase followed by a glide phase) and swam at average speeds of 1.0-1.3 BL/s. No statistical relationship was found between the kinematics of the burst and glide phases in either the lab or the field, so the phases were considered uncoupled. Furthermore, since the kinematics (tailbeat frequency, glide-duty factor) of lab and field volitional swimming were statistically identical, the EMGs of volition swimming in the lab likely reflect field effort. When relativized to volitional swimming speeds, the EMG intensities for both gaits were statistically identical. These results suggest that intermittent swimming may not reflect a strategy for energetic efficiency. Instead, the decoupling between the burst and glide phase may improve maneuverability, since 75% of 3D tracked intermittent swimming bouts (n=129) in the field involved a directional change. Although previous research suggests that intermittent swimming may also provide fatigue resistance, we hypothesize that intermittent swimming evolved in Bluegill as an adaptive gait for navigating their densely vegetated habitat.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IROS47612.2022.9981131
