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971 results on '"Chen, Xiangsheng"'

4. Accumulation and Energy Characteristics in Debris Flow

Author

Ji, Fengling, Li, Wei, Chen, Xiangsheng, Yu, Jing-jing, Zhu, Guofei, Ceccarelli, Marco, Series Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Agrawal, Sunil K., Advisory Editor, and Zhou, Kun, editor

Published
2025
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5. Enhancing Grasping Performance of Novel Objects through an Improved Fine-Tuning Process

Author

Hu, Xiao and Chen, Xiangsheng

Subjects
Computer Science - Robotics
Abstract

Grasping algorithms have evolved from planar depth grasping to utilizing point cloud information, allowing for application in a wider range of scenarios. However, data-driven grasps based on models trained on basic open-source datasets may not perform well on novel objects, which are often required in different scenarios, necessitating fine-tuning using new objects. The data driving these algorithms essentially corresponds to the closing region of the hand in 6D pose, and due to the uniqueness of 6D pose, synthetic annotation or real-machine annotation methods are typically employed. Acquiring large amounts of data with real-machine annotation is challenging, making synthetic annotation a common practice. However, obtaining annotated 6D pose data using conventional methods is extremely time-consuming. Therefore, we propose a method to quickly acquire data for novel objects, enabling more efficient fine-tuning. Our method primarily samples grasp orientations to generate and annotate grasps. Experimental results demonstrate that our fine-tuning process for a new object is 400 \% faster than other methods. Furthermore, we propose an optimized grasp annotation framework that accounts for the effects of the gripper closing, making the annotations more reasonable. Upon acceptance of this paper, we will release our algorithm as open-source.

Published
2023

11. ONE PIECE: One Patchwork In Effectively Combined Extraction for grasp

Author

Hu, Xiao, Mo, HangJie, Chen, XiangSheng, Chen, JinLiang, and Chen, Xiangyu

Subjects
Computer Science - Robotics
Abstract

For grasp network algorithms, generating grasp datasets for a large number of 3D objects is a crucial task. However, generating grasp datasets for hundreds of objects can be very slow and consume a lot of storage resources, which hinders algorithm iteration and promotion. For point cloud grasp network algorithms, the network input is essentially the internal point cloud of the grasp area that intersects with the object in the gripper coordinate system. Due to the existence of a large number of completely consistent gripper area point clouds based on the gripper coordinate system in the grasp dataset generated for hundreds of objects, it is possible to remove the consistent gripper area point clouds from many objects and assemble them into a single object to generate the grasp dataset, thus replacing the enormous workload of generating grasp datasets for hundreds of objects. We propose a new approach to map the repetitive features of a large number of objects onto a finite set.To this end, we propose a method for extracting the gripper area point cloud that intersects with the object from the simulator and design a gripper feature filter to remove the shape-repeated gripper space area point clouds, and then assemble them into a single object. The experimental results show that the time required to generate the new object grasp dataset is greatly reduced compared to generating the grasp dataset for hundreds of objects, and it performs well in real machine grasping experiments. We will release the data and tools after the paper is accepted., Comment: Submitted to IROS2023

Published
2023

34. Comparative analysis of anchor cables in pullout tests using distributed fiber optic sensors

Author

Chen, Weibin, Hong, Chengyu, Chen, Xiangsheng, Luo, Guangbin, and Su, Dong

Subjects
Dynamic testing -- Methods, Anchorage (Structural engineering) -- Mechanical properties -- Testing, Cables -- Mechanical properties -- Testing
Abstract

The interaction mechanism between grouted anchor cables and the surrounding rock mass or soil are complicated and difficult to investigate using conventional monitoring technologies. This study focuses on the in-situ pullout behavior of grouted anchor cables using Brillouin Optical Time-Domain Analysis technique. A series of pullout tests were conducted on anchor cables with five different anchor lengths and three grouting methods. Distributed fiber optic sensors were used to measure the strain distribution of cable bolts from tip to top. The in-situ pullout test results show that both ultimate pullout resistance and ultimate displacement at the ultimate pullout resistance increase with anchor length. The bond strength suffers a slight variation with an increase in anchor length. Both ultimate pullout resistance and the ultimate displacement for Type-B grouting (grouting with reaming) and Type-C grouting (secondary grouting) are larger than those for Type-A grouting (one-time grouting). The shear bond strength of Type-C grouting is greater than that of Type-A grouting. Results from Gaussian functions analysis show that it is feasible to use the function to characterize the axial stress of anchor cables. Furthermore, the overall mobilized percentage [eta] was evaluated. The anchor cables with smaller anchor lengths will result in greater [eta]. The [eta] for the anchor cables of 6 m reaches 92% at the last load, whereas the anchor cables of 18 m were mobilized by only 29% at the last load. The grouting method has a negligible effect on the [eta], increasing from 22% to 29% in process of the pullout test. Key words: distributed fiber optic sensor, anchor cables, pullout tests, pullout resistance, 1. Introduction Fully grouted anchor cables are common reinforcements widely used in mining, excavations, and slopes (Stillborg 1986; Hyett etal. 1996) due to their affordability, simplicity of installation, and versatility. [...]

Published
2023
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