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Titlebook: Intelligent Robotics and Applications; 16th International C Huayong Yang,Honghai Liu,Zhiyong Wang Conference proceedings 2023 The Editor(s)

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樓主: 掩飾
31#
發(fā)表于 2025-3-26 23:41:04 | 只看該作者
32#
發(fā)表于 2025-3-27 04:08:17 | 只看該作者
33#
發(fā)表于 2025-3-27 07:25:28 | 只看該作者
34#
發(fā)表于 2025-3-27 11:22:33 | 只看該作者
Realtime 3D Reconstruction at?Scale and?Object Pose Estimation for?Bin Picking System steps such as preprocessing, model feature construction, and positional result optimization. In addition, an improved voting weight and a composite index based on distance and normal vector are proposed. The final experiment verifies that the reconstruction and object recognition algorithm of this paper achieves better application results.
35#
發(fā)表于 2025-3-27 16:40:52 | 只看該作者
Adapted Mapping Estimator in Visual Servoing Control for Model-Free Robotics Manipulatoroved Kalman filtering (KF) and network learning techniques, moreover, an observation correlation updating method is used to estimate the variance of the noises via online learning. Various grasping positioning experiments are conducted to verify the proposed approach by using an eye-in-hand robotic manipulator without calibration.
36#
發(fā)表于 2025-3-27 18:12:17 | 只看該作者
Trajectory Planning of Aerial Manipulators Based on Inertial Decompositionount to ensure the pitch angle and angular velocity of quadrotors are suitable and feasible. A geometry controller is used to ensure accurate tracking of the planned trajectory. Simulations are carried out to verify the proposed method.
37#
發(fā)表于 2025-3-27 23:19:13 | 只看該作者
A Feature Fusion Network for Skeleton-Based Gesture Recognition for fusion at the feature level, which relies on 3D heat map video streams and uses the video streams as the input to the network. Finally, the Double_C3D framework was evaluated on the SHREC dynamic gesture recognition dataset and the JHMBD dynamic behavior recognition dataset with an accuracy of 91.72% and 70.54%, respectively.
38#
發(fā)表于 2025-3-28 04:22:11 | 只看該作者
Dynamic Hand Gesture Recognition Based on Multi-skeletal Features for Sign Language Recognition Systistance noise in the expression of skeleton features. The model has superior performance on the SHREC dataset and can achieve an accuracy of 96.43% in 14 gesture classifications. At the same time, we combine our method with a hand pose estimator to design a real-time sign language recognition (SLR) system.
39#
發(fā)表于 2025-3-28 06:53:07 | 只看該作者
Conference proceedings 202323, which took place in Hangzhou, China, during July 5–7, 2023...The 413 papers included in these proceedings were carefully reviewed and selected from 630 submissions. They were organized in topical sections as follows:..Part I: Human-Centric Technologies for Seamless Human-Robot Collaboration;?Mul
40#
發(fā)表于 2025-3-28 14:24:23 | 只看該作者
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