An Untethered Bioinspired Robotic Tensegrity Dolphin with Multi-Flexibility Design for Aquatic Locomotion

• URL: http://arxiv.org/abs/2411.00347v1
• Date: Fri, 01 Nov 2024 04:05:24 GMT
• Title: An Untethered Bioinspired Robotic Tensegrity Dolphin with Multi-Flexibility Design for Aquatic Locomotion
• Abstract summary: This paper presents the first steps toward a soft dolphin robot using a bio-inspired approach to mimic dolphin flexibility. The current dolphin robot uses a minimalist approach, with only two actuated cable-driven degrees of freedom actuated by a pair of motors. The actuated tail moves up and down in a swimming motion, but this first proof of concept does not permit controlled turns of the robot.
• Score: 26.745400349653767
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents the first steps toward a soft dolphin robot using a bio-inspired approach to mimic dolphin flexibility. The current dolphin robot uses a minimalist approach, with only two actuated cable-driven degrees of freedom actuated by a pair of motors. The actuated tail moves up and down in a swimming motion, but this first proof of concept does not permit controlled turns of the robot. While existing robotic dolphins typically use revolute joints to articulate rigid bodies, our design -- which will be made opensource -- incorporates a flexible tail with tunable silicone skin and actuation flexibility via a cable-driven system, which mimics muscle dynamics and design flexibility with a tunable skeleton structure. The design is also tunable since the backbone can be easily printed in various geometries. The paper provides insights into how a few such variations affect robot motion and efficiency, measured by speed and cost of transport (COT). This approach demonstrates the potential of achieving dolphin-like motion through enhanced flexibility in bio-inspired robotics.

Related papers

• Modeling and Control of an Eel-Inspired Soft Robot for Design Optimization [1.6686955491488271]
  Anguilliform locomotion is a highly efficient swimming mode.<n>The advent of new materials for soft robots enables the development of an eel-inspired soft robot.<n>This paper presents a simulation model of an eel-inspired soft robot designed for anguilliform swimming.
  arXiv  Detail & Related papers  (2026-08-14T19:54:40Z)
• Cuttlebot: a platform demonstration for complex, autonomous, bio-inspired swimmers [62.91681227094761]
  This work presents an autonomous robotic platform: the CORE, capable of driving six artificial muscles while sensing visual and spatial information.<n>To validate the platform, we developed the Cuttlebot - a cuttlefish-inspired robot that swims in three dimensions using undulatory fin locomotion.<n>The CORE system was capable of driving specialized control signals into the artificial muscles to controllably output force and torque in six axes.
  arXiv  Detail & Related papers  (2026-05-29T16:48:52Z)
• A Bioinspired Underwater Robot with a Latch-Mediated Soft Bistable Mechanism [5.9498629991010175]
  We introduce a bioinspired, soft bistable actuator with an integrated latch mechanism that enables asymmetric energy input and release using a single motor.<n> Experimental results demonstrate stable periodic flapping, precise steering, and a maximum thrust of 0.528 N, impulse of 0.147 Ns, and vertical displacement of 30 mm.<n>This study presents a novel, energy-efficient approach for controlling motion in compact underwater robots, paving the way for advanced biomimetic designs.
  arXiv  Detail & Related papers  (2026-05-26T12:29:05Z)
• Design, Modeling and Direction Control of a Wire-Driven Robotic Fish Based on a 2-DoF Crank-Slider Mechanism [7.409300331963032]
  We develop a wire-driven robotic fish equipped with a crank-slider mechanism that decouples propulsion from steering.<n>We propose a combined feedforward-feedback control strategy to achieve independent regulation of propulsion and steering.
  arXiv  Detail & Related papers  (2026-03-03T10:55:45Z)
• KungfuBot: Physics-Based Humanoid Whole-Body Control for Learning Highly-Dynamic Skills [50.34487144149439]
  This paper presents a physics-based humanoid control framework, aiming to master highly-dynamic human behaviors such as Kungfu and dancing.<n>For motion processing, we design a pipeline to extract, filter out, correct, and retarget motions, while ensuring compliance with physical constraints.<n>For motion imitation, we formulate a bi-level optimization problem to dynamically adjust the tracking accuracy tolerance.<n>In experiments, we train whole-body control policies to imitate a set of highly-dynamic motions.
  arXiv  Detail & Related papers  (2025-06-15T13:58:53Z)
• SpikingSoft: A Spiking Neuron Controller for Bio-inspired Locomotion with Soft Snake Robots [9.358725923314006]
  This work explores the possibility of generating locomotion gaits by utilizing physical oscillations in a soft snake. We introduce the Double Threshold Spiking neuron model with adjustable thresholds to generate varied output patterns. We demonstrate that our approach, termed SpikingSoft, naturally pairs and integrates with reinforcement learning.
  arXiv  Detail & Related papers  (2025-01-31T12:00:54Z)
• DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models [102.13968267347553]
  We present DiffuseBot, a physics-augmented diffusion model that generates soft robot morphologies capable of excelling in a wide spectrum of tasks. We showcase a range of simulated and fabricated robots along with their capabilities.
  arXiv  Detail & Related papers  (2023-11-28T18:58:48Z)
• Generalized Animal Imitator: Agile Locomotion with Versatile Motion Prior [14.114972332185044]
  This paper introduces the Versatile Motion prior (VIM) - a Reinforcement Learning framework designed to incorporate a range of agile locomotion tasks. Our framework enables legged robots to learn diverse agile low-level skills by imitating animal motions and manually designed motions. Our evaluations of the VIM framework span both simulation environments and real-world deployment.
  arXiv  Detail & Related papers  (2023-10-02T17:59:24Z)
• Automated Gait Generation For Walking, Soft Robotic Quadrupeds [6.005998680766498]
  Gait generation for soft robots is challenging due to the nonlinear dynamics and high dimensional input spaces of soft actuators. We present a sample-efficient, simulation free, method for self-generating soft robot gaits. This is the first demonstration of completely autonomous gait generation in a soft robot.
  arXiv  Detail & Related papers  (2023-09-30T21:31:30Z)
• Barkour: Benchmarking Animal-level Agility with Quadruped Robots [70.97471756305463]
  We introduce the Barkour benchmark, an obstacle course to quantify agility for legged robots. Inspired by dog agility competitions, it consists of diverse obstacles and a time based scoring mechanism. We present two methods for tackling the benchmark.
  arXiv  Detail & Related papers  (2023-05-24T02:49:43Z)
• SoftZoo: A Soft Robot Co-design Benchmark For Locomotion In Diverse Environments [111.91255476270526]
  We introduce SoftZoo, a soft robot co-design platform for locomotion in diverse environments. SoftZoo supports an extensive, naturally-inspired material set, including the ability to simulate environments such as flat ground, desert, wetland, clay, ice, snow, shallow water, and ocean. It provides a variety of tasks relevant for soft robotics, including fast locomotion, agile turning, and path following, as well as differentiable design representations for morphology and control.
  arXiv  Detail & Related papers  (2023-03-16T17:59:50Z)
• Skeleton2Humanoid: Animating Simulated Characters for Physically-plausible Motion In-betweening [59.88594294676711]
  Modern deep learning based motion synthesis approaches barely consider the physical plausibility of synthesized motions. We propose a system Skeleton2Humanoid'' which performs physics-oriented motion correction at test time. Experiments on the challenging LaFAN1 dataset show our system can outperform prior methods significantly in terms of both physical plausibility and accuracy.
  arXiv  Detail & Related papers  (2022-10-09T16:15:34Z)
• VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait Representation [78.92147339883137]
  We show that it is pivotal in increasing controller robustness by learning a latent space capturing the key stance phases constituting a particular gait. We demonstrate that specific properties of the drive signal map directly to gait parameters such as cadence, footstep height and full stance duration. The use of a generative model facilitates the detection and mitigation of disturbances to provide a versatile and robust planning framework.
  arXiv  Detail & Related papers  (2022-05-02T19:49:53Z)
• Learning Material Parameters and Hydrodynamics of Soft Robotic Fish via Differentiable Simulation [26.09104786491426]
  Our framework allows high fidelity prediction of dynamic behavior for composite bi-morph bending structures in real hardware. We demonstrate an experimentally-verified, fast optimization pipeline for learning the material parameters and hydrodynamics of our robots. Although we focus on a specific application for underwater soft robots, our framework is applicable to any pneumatically actuated soft mechanism.
  arXiv  Detail & Related papers  (2021-09-30T05:24:02Z)
• Learning Quadrupedal Locomotion over Challenging Terrain [68.51539602703662]
  Legged locomotion can dramatically expand the operational domains of robotics. Conventional controllers for legged locomotion are based on elaborate state machines that explicitly trigger the execution of motion primitives and reflexes. Here we present a radically robust controller for legged locomotion in challenging natural environments.
  arXiv  Detail & Related papers  (2020-10-21T19:11:20Z)
• Control for Multifunctionality: Bioinspired Control Based on Feeding in Aplysia californica [0.3277163122167433]
  We develop a hybrid Boolean model framework capable of modeling neural bursting activity and simple biomechanics at speeds faster than real time. We present a multifunctional model of Aplysia californica feeding that qualitatively reproduces three key feeding behaviors. We demonstrate that the model can be used for formulating testable hypotheses and discuss the implications of this approach for robotic control and neuroscience.
  arXiv  Detail & Related papers  (2020-08-11T19:26:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.