AEROSPACE SHANGHAI, Volume. 41, Issue 4, 58(2024)
A Task-based Grasping Method for Aerospace Electrical Connectors
[4] S HINTERSTOISSER, V LEPETF, S ILIC et al. Model-based training,detection and pose estimation of texture-less 3D objects in heavily cluttered scenes. Computer Vision-ACCV 2012, 7724, 548-562(2013).
[5] K PARK, T PATTEN, J PRANKL et al. Multi-task template matching for object detection,segmentation and pose estimation using depth images, 789-794(2019).
[6] B TEKIN, S N SINHA. Real-time seamless single shot 6D object pose prediction, 292-301(2018).
[7] V HOLOMJOVA, A J STARKEY, B YUN et al. One-shot learning for task-oriented grasping. IEEE Robotics and Automation Letters, 8, 8232-8238(2023).
[8] B JIANG, R LUO, J MAO, T XIAO, Y JIANG. Acquisition of localization confidence for accurate object detection. Computer Vision-ECCV 2018, 11218, 816-832(2018).
[10] J TOBIN, R FONG et al. Domain randomization for transferring deep neural networks from simulation to the real world, 23-30(2017).
[11] S M HU, J X CAI, Y K LAI. Semantic labeling and instance segmentation of 3D point clouds using patch context analysis and multiscale processing. IEEE Transactions on Visualization and Computer Graphics, 26, 2485-2498(2020).
[12] W WANG, R YU, Q HUANG et al. Similarity group proposal network for 3D point cloud instance segmentation, 3942-3993(2018).
[13] M A FISCHLER, R C BOLLES, J D FOLEY. Random sample consensus:a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the Association for Computing Machinery, 24, 381-395(1981).
[14] PAS A TEN, R PLATT. Using geometry to detect grasp poses in 3D point clouds. Robotics Research, 307-324(2017).
[15] H WANG, S SRIDHAR, J HUANG et al. Normalized object coordinate space for category-level 6D object pose and size estimation, 2637-2646(2019).
[16] P A TEN. Grasp pose detection in point clouds. International Journal of Robotics Research, 36, 1455-1473(2017).
[17] J MAHLER, M MATTHEW, X LIU et al. Dex-Net 3.0:Computing robust vacuum suction grasp targets in point clouds using a new analytic model and deep learning. Robotics Science and Systems, 5620-5627(2018).
[18] C R QI, H SU, K MO et al. Pointnet:deep learning on point sets for 3D classification and segmentation, 77-85(2017).
[19] Z QIN, K FANG, Y ZHU et al. Keto:learning keypoint representations for tool manipulation, 7278-7285(2020).
[20] B LI, W OUYANG, L SHENG et al. GS3D:An efficient 3D object detection framework for autonomous driving, 111-119(2019).
[21] S HINTERSTOISSER, V LEPETF, S ILIC et al. Model based training,detection and pose estimation of texture-less 3D objects in heavily cluttered scenes. Lecture Notes in Computer Science, 7724, 563-577(2013).
[22] B YANG, J WANG, R CLARK et al. Learning object bounding boxes for 3D instance segmentation on point clouds, 6708-6717(2020).
[23] K FANG, Y ZHU, A GARG et al. Learning task-oriented grasping for tool manipulation from simulated self-supervision. International Journal of Robotics Research, 39, 202-216(2020).
[24] R ANTONOVA, M KOKIC, J OHANNES A. Global search with Bernoulli alteration kernel for task-oriented grasping informed by simulation. Proceedings of Machine Learning Research, 87, 641-650(2018).
[25] F J CHU, R N XU, P A VELA. Learning affordance segmentation for real-world robotic manipulation via synthetic images. IEEE Robotics and Automation Letters, 4, 1140-1147(2019).
[28] Z D DAI. Progress and key technologies in several frontiers of space robots. Manned Spaceflight, 22, 9-15(2016).
[29] S SALTI, F TOMBARI, L D STEFANO. Shot:unique signatures of histograms for surface and texture description. Computer Vision and Image Understanding, 125, 251-264(2014).
Get Citation
Copy Citation Text
Wanqin LI, Haitao JING, Faming FANG, Xiaolong MA, Huaiwu ZOU, Feng LI. A Task-based Grasping Method for Aerospace Electrical Connectors[J]. AEROSPACE SHANGHAI, 2024, 41(4): 58
Category: Innovation and Exploration
Received: Mar. 8, 2024
Accepted: --
Published Online: Nov. 18, 2024
The Author Email: