Robotic teleoperation is essential for hazardous environments where human safety is at risk. However, efficient and intuitive human-machine interaction for multi-robot systems remains challenging. This article aims to demonstrate a robotic teleoperation system, denominated AutoNav, centered around autonomous navigation and gesture commands interpreted through computer vision. The central focus is on recognizing the palm of the hand as a control interface to facilitate human-machine interaction in the context of multi-robots. The MediaPipe framework was integrated to implement gesture recognition from a USB camera. The system was developed using the Robot Operating System, employing a simulated environment that includes the Gazebo and RViz applications with multiple TurtleBot 3 robots. The main results show a reduction of approximately 50% in the execution time, coupled with an increase in free time during teleoperation, reaching up to 94% of the total execution time. Furthermore, there is a decrease in collisions. These results demonstrate the effectiveness and practicality of the robotic control algorithm, showcasing its promise in managing teleoperations across multi-robots. This study fills a knowledge gap by developing a hand gesture-based control interface for more efficient and safer multi-robot teleoperation. These findings enhance human-machine interaction in complex robotic operations. A video showing the system working is available at https://youtu.be/94S4nJ3IwUw .
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11618356 | PMC |
| http://dx.doi.org/10.1038/s41598-024-80898-x | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Sign Language Recognition System for Deaf Patients: Protocol for a Systematic Review.
JMIR Res Protoc
January 2025
Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
Background: Individuals with hearing impairments may face hindrances in health care assistance, which may significantly impact the prognosis and the incidence of complications and iatrogenic events. Therefore, the development of automatic communication systems to assist the interaction between this population and health care workers is paramount.
Objective: This study aims to systematically review the evidence on communication systems using human-computer interaction techniques developed for deaf people who communicate through sign language that are already in use or proposed for use in health care contexts and have been tested with human users or videos of human users.
MI-Mamba: A hybrid motor imagery electroencephalograph classification model with Mamba's global scanning.
Ann N Y Acad Sci
January 2025
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
Deep learning has revolutionized electroencephalograph (EEG) decoding, with convolutional neural networks (CNNs) being a predominant tool. However, CNNs struggle with long-term dependencies in sequential EEG data. Models like long short-term memory and transformers improve performance but still face challenges of computational efficiency and long sequences.
View Article and Find Full Text PDFHierarchical Iontronic Flexible Sensor with High Sensitivity over Ultrabroad Range Enabled by Equilibration of Microstructural Compressibility and Stability.
ACS Sens
January 2025
School of Advanced Manufacturing, Nanchang University, Nanchang 330031, Jiangxi, China.
Despite improved sensitivity of iontronic pressure sensors with microstructures, structural compressibility and stability issues hinder achieving exceptional sensitivity across a wide pressure range. Herein, the interplay between ion concentration, mechanical properties, structural geometry, and aspect ratio (AR) on the sensitivity of lithium bis(trifluoromethanesulfonyl) imide/thermoplastic polyurethane (LiTFSI/TPU) ionogel is delved into. The results indicate that cones exhibit superior compressibility compared to pyramids and hemispheres, manifesting in an enhanced sensitivity toward the LiTFSI/TPU ionogel.
View Article and Find Full Text PDFDigital channel-enabled distributed force decoding via small datasets for hand-centric interactions.
Sci Adv
January 2025
Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, 999077, China.
Tactile interfaces are essential for enhancing human-machine interactions, yet achieving large-scale, precise distributed force sensing remains challenging due to signal coupling and inefficient data processing. Inspired by the spiral structure of and the processing principles of neuronal systems, this study presents a digital channel-enabled distributed force decoding strategy, resulting in a phygital tactile sensing system named PhyTac. This innovative system effectively prevents marker overlap and accurately identifies multipoint stimuli up to 368 regions from coupled signals.
View Article and Find Full Text PDFOn face value: a ghost driver field study investigating interactions between pedestrians and a driverless vehicle with anthropomorphic displays.
Ergonomics
January 2025
Human Factors Research Group, University of Nottingham, University Park, Nottingham, United Kingdom.
In a novel, on-road study, using a 'Ghost Driver' to emulate an automated vehicle (AV), we captured over 10 hours of video (n = 520) and 64 survey responses documenting the behaviour and attitudes of pedestrians in response to the AV. Three prototype external human-machine interfaces (eHMIs) described the AV's behaviour, awareness and intention using elements of anthropomorphism: High (human face), Low (car motif), Abstract (partial representation of human features that lacked precise visual reference); these were evaluated against a (no eHMI) baseline. Despite many pedestrians reporting that they still relied on vehicular cues to negotiate their crossing, there was a desire/expectation expressed for explicit communication with future AVs.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!