Fabric Pneumatic Artificial Muscles Based on the Drawstring Principle
- URL: http://arxiv.org/abs/2607.18641v1
- Date: Tue, 21 Jul 2026 02:20:38 GMT
- Title: Fabric Pneumatic Artificial Muscles Based on the Drawstring Principle
- Abstract summary: This paper proposes a novel drawstring fabric pneumatic artificial muscle (DPAM)<n>Unlike traditional counterparts, the proposed DPAM produces no extra radial deformation during contraction, greatly improving structural compactness.<n>The DPAM exhibits outstanding mechanical performance: a load capacity over 800 times its self-weight, a maximum contraction ratio of 44%, and a power density up to 4.98 kW/kg.
- Score: 10.313921711241436
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Pneumatic artificial muscles have wide applications in robotics and industrial fields. Conventional pneumatic artificial muscles generate extra radial deformation during axial contraction, which severely wastes available working space. Inspired by the widely adopted drawstring principle in textile products, this paper proposes a novel drawstring fabric pneumatic artificial muscle (DPAM). Unlike traditional counterparts, the proposed DPAM produces no extra radial deformation during contraction, greatly improving structural compactness. The DPAM exhibits outstanding mechanical performance: a load capacity over 800 times its self-weight, a maximum contraction ratio of 44%, and a power density up to 4.98 kW/kg, alongside excellent scalability. Two representative application scenarios, bionic robots and industrial production lines, are demonstrated to validate its practicability. The DPAM can be easily expanded within a two-dimensional plane, as verified by the fabricated DPAM matrix. This work not only presents a high-performance novel pneumatic artificial muscle but also inspires researchers to draw design inspiration from conventional textile structures to address existing challenges in soft robotics.
Related papers
- Soft Robotic Exogloves for Dexterous Mobility -- Towards Personalized Rehabilitation [0.0]
Soft robotic exogloves can provide hand rehabilitation and assistance.<n> fitting these gloves often relies on standardized measurements not tailored to the individual.<n>We present the design, fabrication, modeling, and testing of a personalized pneumatically-actuated soft robotic exoglove.
arXiv Detail & Related papers (2026-07-08T22:48:29Z) - Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing [73.88859384645264]
Out-Of-Distribution (OOD) generalization to various racetracks utilizes Machine Learning (ML) to encode the mathematical relation between sensor data and vehicle actuation for end-to-end control.<n>This paper presents a DRL method that parameterizes nonlinear vehicle dynamics from the spectral distribution of depth measurements with a non-geometric, physics-informed reward, to infer vehicle time-optimal and overtaking racing controls with an Artificial Neural Network (ANN)<n>The policy outperforms human demonstrations by 12% in OOD tracks on proportionally scaled hardware, by maximizing the friction circle with tire dynamics that resemble an empirical Pacejka tire model.
arXiv Detail & Related papers (2026-04-10T17:12:07Z) - Sim-to-Real Transfer for Muscle-Actuated Robots via Generalized Actuator Networks [53.12548325396181]
Tendon drives paired with soft muscle actuation enable faster and safer robots.<n>We propose a sim-to-real pipeline that learns a neural network model of this complex actuation.<n>We successfully deploy precise goal-reaching and dynamic ball-in-a-cup policies trained entirely in simulation.
arXiv Detail & Related papers (2026-04-10T16:52:54Z) - Elastomeric Strain Limitation for Design of Soft Pneumatic Actuators [0.0]
This thesis presents strategies for the design, modeling, and strain-based control of human-safe elastomeric soft pneumatic actuators.<n>We investigate electroadhesive (EA) strain limiters for variable shape generation, rapid force application, and targeted inflation trajectories.<n>We validate theoretical models based on material properties and energy minimization using active learning and automated testing.
arXiv Detail & Related papers (2026-04-03T00:56:14Z) - Multi-Mode Pneumatic Artificial Muscles Driven by Hybrid Positive-Negative Pressure [11.862266769445684]
This paper introduces an architecture for Inflatable Fluid-Driven Origami-Inspired Artificial Muscles (IN-FOAMs)<n>A typical IN-FOAM consists of an inflatable skeleton enclosed within an outer skin, which can be driven using a combination of positive and negative pressures.<n>The skeleton patterns are programmable, enabling a variety of motions, including contracting, bending, twisting, and rotating.
arXiv Detail & Related papers (2026-03-16T10:20:58Z) - Fabric Pneumatic Artificial Muscle-Based Head-Neck Exosuit: Design, Modeling, and Evaluation [1.1199585259018459]
We propose a fabric pneumatic artificial muscle-based exosuit design for head-neck support.<n>We introduce compression force along the neck as a novel, potentially comfort-related metric.<n>Our test dummy and human user demonstration confirm that the exosuit can provide functional head support and trajectory tracking.
arXiv Detail & Related papers (2026-03-13T19:10:46Z) - Programmable Deformation Design of Porous Soft Actuator through Volumetric-Pattern-Induced Anisotropy [1.3464152928754487]
A novel design method is presented to realize soft porous actuators with programmable deformation by incising specific patterns into the porous foam body.<n>Our work provides a new, efficient, and scalable paradigm for the design of multi-functional soft porous robots.
arXiv Detail & Related papers (2025-12-13T13:17:17Z) - MHR: Momentum Human Rig [56.93912398188499]
We present MHR, a parametric human body model that combines the decoupled skeleton/shape paradigm of ATLAS with a flexible, modern rig and pose corrective system inspired by the Momentum library.<n>Our model enables expressive, anatomically plausible human animation, supporting non-linear pose correctives, and is designed for robust integration in AR/VR and graphics pipelines.
arXiv Detail & Related papers (2025-11-19T16:18:02Z) - Efficient Deformable Tissue Reconstruction via Orthogonal Neural Plane [58.871015937204255]
We introduce Fast Orthogonal Plane (plane) for the reconstruction of deformable tissues.
We conceptualize surgical procedures as 4D volumes, and break them down into static and dynamic fields comprised of neural planes.
This factorization iscretizes four-dimensional space, leading to a decreased memory usage and faster optimization.
arXiv Detail & Related papers (2023-12-23T13:27:50Z) - 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) - Neural LerPlane Representations for Fast 4D Reconstruction of Deformable
Tissues [52.886545681833596]
LerPlane is a novel method for fast and accurate reconstruction of surgical scenes under a single-viewpoint setting.
LerPlane treats surgical procedures as 4D volumes and factorizes them into explicit 2D planes of static and dynamic fields.
LerPlane shares static fields, significantly reducing the workload of dynamic tissue modeling.
arXiv Detail & Related papers (2023-05-31T14:38:35Z)
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.