... in digital models during the programming of conventional painting robots. Design/methodology/approach This work combines high-precision 3D point cloud sensing devices with deep learning technology to enhance the system’s adaptability. Surface geometries are captured via line-structured light scanning...

...Lebin Zhao; Tao Chen; Peipei Yuan; Xiaoyang Li; Bin Chen Purpose This study aims to enhance the understanding of the current research status, challenges and potential development directions of deep learning ( DL )-based visual simultaneous localization and mapping ( VSLAM ), thereby laying...

... objectives, the authors designed a framework that combines deep learning techniques with off-policy reinforcement learning. The framework was trained in a simulated environment to generate a diverse set of images, which enabled accurate object detection and plane segmentation. An off-policy reinforcement...

... Machine vision Deep learning With the rapid development of sensor measurement and artificial intelligence (AI), research on intelligent welding robots has become an inevitable trend (Lai et al., 2018 ; Rout et al., 2019 ; Wang et al., 2020a ; Xu and Wang, 2021...

.... Findings According to market trends, floor-cleaning robots dominate other cleaning areas. This review mainly focuses on five attributes of floor-cleaning robots: design and development of AFCR, complete coverage path planning, the application of machine learning (ML)/deep learning (DL), optimisation...

... Humanoid robot Pose estimation Deep learning Pose estimation is the premise of action recognition and behavior analysis (Sarafianos et al., 2016 ; Moshaii et al., 2015). It can be divided into two kinds by estimation means: marked and unmarked. For humanoid robots, the marked...

... on the traditional visual simultaneous localisation and mapping (VSLAM) framework, a dynamic object detection framework based on deep learning is introduced, and dynamic objects in the scene are culled during mapping. Design/methodology/approach Typical SLAM algorithms or data sets assume a static environment...

... . Jianjun Yao can be contacted at: travisyao@126.com 29 11 2023 24 12 2023 18 01 2024 © Emerald Publishing Limited 2024 Emerald Publishing Limited Licensed re-use rights only SLAM Keypoint detection Vision localization Relocalization Deep learning Significant...

...Wei Xiao; Zhongtao Fu; Shixian Wang; Xubing Chen Purpose Because of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this paper is to propose a deep learning torque prediction method...

... ^ is the predicted offset and θ ^ is the rotation angle, from which the grasps can be recovered according to the proposed grasp representation method. Deep learning Robotic grasping Grasp detection Grasp generation Pick and place The remainder of this paper is organized as follows...

...Xuan Zhao; Hancheng Yu; Mingkui Feng; Gang Sun Purpose Robot automatic grasping has important application value in industrial applications. Recent works have explored on the performance of deep learning for robotic grasp detection. They usually use oriented anchor boxes (OABs) as detection prior...

... using different filters and cubic spline techniques. After processing, the data are labelled into seven activities. For data acquisition, a Python-based GUI has been designed to analyse and display the processed data. The data is further classified using four different deep learning model: deep neural...

... Dongfang Yang can be contacted at: yangdf301@163.com 31 05 2018 19 08 2018 01 10 2018 © Emerald Publishing Limited 2019 Emerald Publishing Limited Licensed re-use rights only Remote sensing Deep learning Autoencoder Cross-modal Road segmentation Transfer learning...

... a challenging task for an autonomous mobile robot because of the highly unstructured and different characteristics of outdoor environments. This study examines a complete vision guided real-time approach for robot navigation in urban roads based on drivable road area detection by using deep learning. During...