Autonomous posture detection and self-localization of roadheaders is the key to automatic tunneling and roadheader robotization. In this paper, a multi-sensor based positioning method, involving an inertial system for altitude angles measurement, total station for coordinate measurement, and sensors for measuring the real-time length of the hydraulic cylinder is presented for roadheader position measurement and posture detection. Based on this method, a positioning model for roadheader and cutter positioning is developed. Additionally, flexible trajectory planning methods are provided for automatic cutting. Based on the positioning model and the trajectory planning methods, an automatic cutting procedure is proposed and applied in practical tunneling. The experimental results verify the high accuracy and efficiency of both the positioning method and the model. Furthermore, it is indicated that arbitrary shapes can be generated automatically and precisely according to the planned trajectory, employing the automatic cutting procedure. Therefore, unmanned tunneling can be realized by employing the proposed automatic cutting process.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891534 | PMC |
http://dx.doi.org/10.3390/s19224955 | DOI Listing |
J Imaging
December 2024
College of Electrical and Information, Northeast Agricultural University, 600 Changjiang Road, Harbin 150038, China.
Alzheimer's disease (AD), a degenerative condition affecting the central nervous system, has witnessed a notable rise in prevalence along with the increasing aging population. In recent years, the integration of cutting-edge medical imaging technologies with forefront theories in artificial intelligence has dramatically enhanced the efficiency of identifying and diagnosing brain diseases such as AD. This paper presents an innovative two-stage automatic auxiliary diagnosis algorithm for AD, based on an improved 3D DenseNet segmentation model and an improved MobileNetV3 classification model applied to brain MR images.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Ningbo Institute of Dalian University of Technology, Ningbo 315032, China.
In the high-stakes domain of precision manufacturing, Cubic Boron Nitride (CBN) inserts are pivotal for their hardness and durability. However, post-production surface defects on these inserts can compromise product integrity and performance. This paper proposes an automated detection and classification system using machine vision to scrutinize these surface defects.
View Article and Find Full Text PDFBull Entomol Res
December 2024
Department of Land, Environment, Agriculture and Forestry, University of Padova, Legnaro, Padua, Italy.
In December 2017 the Venetian Region (local Authority), financed the creation of the Operational Group (OG) 'Serinnovation', within the framework of the Rural Development Plan supported by the European Community. The OG aims at coordinating and spreading innovation in sericulture through mechanisation of processes and centralisation of some rearing steps, the use of waste as by-products and traceability to promote local productions. The project acts on perceived quality by increasing the added value, through production cost efficiency, and on the recovery of the waste material for further applications (circular economy).
View Article and Find Full Text PDFNeurosurg Focus
December 2024
1Department of Orthopaedics, Peking University Third Hospital.
Objective: This study aimed to introduce a novel artificial intelligence (AI)-based robotic system for autonomous planning of spinal posterior decompression and verify its accuracy through a cadaveric model.
Methods: Seventeen vertebrae from 3 cadavers were included in the study. Three thoracic vertebrae (T9-11) and 3 lumbar vertebrae (L3-5) were selected from each cadaver.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!