Results 171 to 180 of about 23,148 (262)
A Configurable Real2Sim Bimanual Teleoperation Framework for Surgical Robotic Tool Design and Evaluation. [PDF]
Li T +4 more
europepmc +1 more source
Habitat‐Specific Design Strategies for Untethered Soft Robots
Habitat shapes the design of untethered soft robots. Across land, water, air, and living tissues, environmental constraints govern viable combinations of compliant structures, actuation mechanisms, and energy delivery. This review connects these constraints with robotic performance and identifies design principles for advancing adaptable, durable, and ...
Siyang Li +3 more
wiley +1 more source
Why inverse simulations overestimate optimal ankle exoskeleton assistance: the role of bi-articular coordination and joint stiffness. [PDF]
Luis I +2 more
europepmc +1 more source
This Perspective charts the modeling landscape for soft manipulators, spanning sparse system identification, neural dynamics, physics‐informed models and hybrid intelligence. We examine how physics, data, sensing, adaptation and control can assume complementary roles within this landscape, and provide a framework for selecting modeling approaches ...
Wenbo Hui +4 more
wiley +1 more source
An Error-Adaptive Competition-Based Inverse Kinematics Approach for Bimanual Trajectory Tracking of Humanoid Upper-Limb Robots. [PDF]
Liu J, Wang Z, Tang H, Jin H, Zhao J.
europepmc +1 more source
This work introduces a CeHP‐enabled multifunctional matrix‐activated electrode (MME) for high‐temperature PEM fuel cells, forming an electrochemically and mechanically functional matrix within the catalyst layer. The design increases peak power density by 38% and reduces voltage decay by 62% at 240 °C, while using only 40% of the Pt loading of a ...
Gyeongseok Gwak +12 more
wiley +1 more source
Model predictive control for velocity tracking in synchronous fly-around based on dual quaternion error dynamics. [PDF]
Lu L, Xia L, Chai H, Yang C, Wang R.
europepmc +1 more source
This article implements a unified human digital twin framework that integrates cutting edge actuation, sensing, simulation, and bidirectional feedback capability. The approach includes integrating multimodal sensing, AI, and biomechanical simulation into one compact system.
Tajbeed Ahmed Chowdhury +4 more
wiley +1 more source
Update of the articular representation of the TLEM 2.0 musculoskeletal model. [PDF]
Conconi M, Dumas R, Sancisi N.
europepmc +1 more source
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia +2 more
wiley +1 more source

