Results 181 to 190 of about 52,202 (265)

In Vivo Classification of Patellar Motion Trajectories in Individuals: A 4D-CT-Based Study with Unsupervised Clustering. [PDF]

open access: yesDiagnostics (Basel)
Wei J   +12 more
europepmc   +1 more source

Cteno‐Bot: An Untethered Metachronally Swimming Robot With Magnetoactive Propulsors

open access: yesAdvanced Intelligent Systems, EarlyView.
We present Cteno‐bot, an untethered ctenophore‐inspired robot which swims using metachronally coordinated appendages. A single mechanism controls up to 216 magnetoactive propulsors via a dynamically varying magnetic field. We show that the swimming speed of the robot can be increased without a corresponding increase in power requirement, simply by ...
David J. Peterman, Margaret L. Byron
wiley   +1 more source

Multimodal Quad‐Finger Soft Robotic Hand With Dual‐Chamber Origami Actuator for Large‐Workspace Manipulation

open access: yesAdvanced Intelligent Systems, EarlyView.
A multimodal quad‐finger soft robotic hand (QDO hand) uses dual‐chamber straight–curved origami prismatic (SCOP) origami actuators. By coordinating positive and negative pressurization in the two chambers, each finger produces axial extension, contraction and bidirectional bending.
Qinlin Tan   +6 more
wiley   +1 more source

Generalized Task‐Driven Design of Soft Robots via Reduced‐Order Finite Element Method‐Based Surrogate Modeling

open access: yesAdvanced Intelligent Systems, EarlyView.
A unified, reusable modeling pipeline enables task‐driven design of soft robots across actuator families and task scenarios. High‐fidelity simulations are compressed into compact pseudo‐rigid‐body joint surrogates, while a design‐conditioned meta‐model generates new surrogates from geometry parameters without rerunning finite element method.
Yao Yao, David Howard, Perla Maiolino
wiley   +1 more source

Driver Behavior Modeling with Subjective Risk‐Driven Inverse Reinforcement Learning

open access: yesAdvanced Intelligent Systems, EarlyView.
A subjective risk‐driven inverse reinforcement learning framework is proposed to model driver decision‐making. It infers drivers' risk perception and risk tolerance from driving data. A learnable risk threshold is used to regulate decisions, enabling interpretable and human‐like driving behavior decisions.
Yang Liang   +6 more
wiley   +1 more source

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