Industrial robots can perform motion with sub-millimeter repeatability when programmed using the teach-and-playback method. While effective, this method requires significant up-front time, tying up the robot and a person during the teaching phase. Off-line programming can be used to generate robot programs, but the accuracy of this method is poor unless supplemented with good calibration to remove systematic errors, feed-forward models to anticipate robot response to loads, and sensing to compensate for unmodeled errors. These increase the complexity and up-front cost of the system, but the payback in the reduction of recurring teach programming time can be worth the effort. This payback especially benefits small-batch, short-turnaround applications typical of small-to-medium enterprises, who need the agility afforded by off-line application development to be competitive against low-cost manual labor. To fully benefit from this agile application tasking model, a common representation of tasks should be used that is understood by all of the resources required for the job: robots, tooling, sensors, and people. This paper describes an information model, the Canonical Robot Command Language (CRCL), which provides a high-level description of robot tasks and associated control and status information.
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http://dx.doi.org/10.1108/IR-01-2016-0037 | DOI Listing |
Front Neurosci
December 2024
Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Objective: High Angular Resolution Diffusion Imaging (HARDI) models have emerged as a valuable tool for investigating microstructure with a higher degree of detail than standard diffusion Magnetic Resonance Imaging (dMRI). In this study, we explored the potential of multiple advanced microstructural diffusion models for investigating preterm birth in order to identify non-invasive markers of altered white matter development.
Approach: Rather than focusing on a single MRI modality, we studied on a compound of HARDI techniques in 46 preterm babies studied on a 3T scanner at term-equivalent age and in 23 control neonates born at term.
Bioengineering (Basel)
October 2024
Department of Artificial Intelligence and Robotics, Sejong University, Seoul 05006, Republic of Korea.
Alzheimer's disease (AD) is a degenerative neurological condition characterized by cognitive decline, memory loss, and reduced everyday function, which eventually causes dementia. Symptoms develop years after the disease begins, making early detection difficult. While AD remains incurable, timely detection and prompt treatment can substantially slow its progression.
View Article and Find Full Text PDFCogn Neurodyn
October 2024
Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, 300192 China.
Brain-computer interface (BCI)-based robot combines BCI and robotics technology to realize the brain's intention to control the robot, which not only opens up a new way for the daily care of the disabled individuals, but also provides a new way of communication for normal people. However, the existing systems still have shortcomings in many aspects such as friendliness of human-computer interaction, and interaction efficient. This study developed a humanoid robot control system by integrating an augmented reality (AR)-based BCI with a simultaneous localization and mapping (SLAM)-based scheme for autonomous indoor navigation.
View Article and Find Full Text PDFBioengineering (Basel)
September 2024
Department of Artificial Intelligence and Robotics, Sejong University, Seoul 05006, Republic of Korea.
Oral cancer, also known as oral squamous cell carcinoma (OSCC), is one of the most prevalent types of cancer and caused 177,757 deaths worldwide in 2020, as reported by the World Health Organization. Early detection and identification of OSCC are highly correlated with survival rates. Therefore, this study presents an automatic image-processing-based machine learning approach for OSCC detection.
View Article and Find Full Text PDFData Brief
October 2024
Automation Technology and Mechanical Engineering, Tampere University, 33720 Tampere, Finland.
Robotic assembling is a challenging task that requires cognition and dexterity. In recent years, perception tools have achieved tremendous success in endowing the cognitive capabilities to robots. Although these tools have succeeded in tasks such as detection, scene segmentation, pose estimation and grasp manipulation, the associated datasets and the dataset contents lack crucial information that requires adapting them for assembling pose estimation.
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