Results 151 to 160 of about 245,191 (306)

Universal Gripper for Industrial Manipulation With Enhanced Rigid Mechanics and Self‐Adaptable Fingers

open access: yesAdvanced Robotics Research, EarlyView.
An enhanced universal gripper combining rigid mechanics with self‐adaptable fingers is presented for industrial automation. The novel six‐bar linkage with integrated compliant pad eliminates mechanical interference while enabling passive shape adaptation.
Muhammad Usman Khalid   +7 more
wiley   +1 more source

Development of a collaborative design tool for Virtual Environment utilizing a 3D game engine

open access: yes, 2008
Collaboration in architectural design can be enhanced by using a virtual environment (VE). The visual aspects in a VE facilitates shared understanding across interdisciplinary groups, further enhanced 3D models, provide visualization support, and allows ...
Shiratuddin, M.F., Breland, J.
core  

Muscle Control of an Extra Robotic Digit

open access: yesAdvanced Robotics Research, EarlyView.
This study compares muscle‐ and movement‐based control for operating a supernumerary robotic thumb. While movement control performs better in the proposed tasks, muscle‐based (EMG) control promotes broader motor learning. The results highlight the promise and challenges of using biosignals for human augmentation, offering new insights into intuitive ...
Julien Russ   +7 more
wiley   +1 more source

myExperiment – A Web 2.0 Virtual Research Environment

open access: yes, 2007
e-Science has given rise to new forms of digital object in the Virtual Research Environment which can usefully be shared amongst collaborating scientists to assist in generating new scientific results.
De Roure, David   +4 more
core  

Robotic Control for Human–Robot Collaborative Assembly Based on Digital Human Model and Reinforcement Learning

open access: yesAdvanced Robotics Research, EarlyView.
This work presents a robotic control method for human–robot collaborative assembly based on a biomechanics‐constrained digital human model. Reinforcement learning is used to generate physiologically plausible human motion trajectories, which are integrated into a virtual environment for robot control learning.
Bitao Yao   +4 more
wiley   +1 more source

Multimodal Human–Robot Interaction Using Human Pose Estimation and Local Large Language Models

open access: yesAdvanced Robotics Research, EarlyView.
A multimodal human–robot interaction framework integrates human pose estimation (HPE) and a large language model (LLM) for gesture‐ and voice‐based robot control. Speech‐to‐text (STT) enables voice command interpretation, while a safety‐aware arbitration mechanism prioritizes gesture input for rapid intervention.
Nasiru Aboki   +2 more
wiley   +1 more source

Supporting meetings in Virtual Worlds with enhanced Communication features

open access: yes, 2010
With the rapid growth in the use of computer for addressing our day to day needs and the increased use of technology in our daily life, we cannot imagine a day without the use of the internet for our routine needs. Today, without any doubt in our mind we
Sathe, Saurabh G
core  

Design and Modeling of a High‐Displacement, Skin‐Integrated Flexible Electromagnetic Actuator for Haptic Interfaces in Virtual Reality

open access: yesAdvanced Robotics Research, EarlyView.
A flexible, skin‐integrated electromagnetic actuator is developed for wearable virtual/augmented reality (VR/AR) haptic systems. A tunable design model enables control over displacement and resonance frequency. The system is validated through a custom VR application with a 6 × 4 actuator array, demonstrating real‐time, spatially targeted tactile ...
Naji Tarabay   +9 more
wiley   +1 more source

Is spatial intelligibility critical to the design of largescale virtual environments?

open access: yes, 2002
This paper discusses the concept of 'intelligibility', a concept usually attributed to the design of real-world environments and suggests how it might be applied to the construction of virtual environments.
Conroy-Dalton, R   +2 more
core  

DRIVE‐SAFE: Data‐Driven Robustness and Informed Validation for Evolving Specifications via Formal Evaluation

open access: yesAdvanced Robotics Research, EarlyView.
DRIVE‐SAFE evaluates learning‐based, black‐box autonomous driving policies against evolving temporal safety requirements using Signal Temporal Logic robustness metrics. It aggregates distributional robustness measures with domain‐informed weights to guide iterative retraining.
Kristy Sakano   +3 more
wiley   +1 more source

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