Zobrazeno 1 - 10
of 182
pro vyhledávání: '"Cao, Hanwen"'
Multi-robot simultaneous localization and mapping (SLAM) enables a robot team to achieve coordinated tasks by relying on a common map of the environment. Constructing a map by centralized processing of the robot observations is undesirable because it
Externí odkaz:
http://arxiv.org/abs/2404.18331
Autor:
Cao, Hanwen, Zhou, Jianshu, Huang, Junda, Li, Yichuan, Meng, Ng Cheng, Cao, Rui, Dou, Qi, Liu, Yunhui
This paper proposes a novel bin picking framework, two-stage grasping, aiming at precise grasping of cluttered small objects. Object density estimation and rough grasping are conducted in the first stage. Fine segmentation, detection, grasping, and p
Externí odkaz:
http://arxiv.org/abs/2303.02604
Autor:
Ghanem, Walid R., Jamali, Vahid, Schellmann, Malte, Cao, Hanwen, Eichinger, Joseph, Schober, Robert
This paper investigates the resource allocation algorithm design for wireless systems assisted by large intelligent reflecting surfaces (IRSs) with coexisting enhanced mobile broadband (eMBB) and ultra reliable low-latency communication (URLLC) users
Externí odkaz:
http://arxiv.org/abs/2208.03798
Autor:
Ghanem, Walid R., Jamali, Vahid, Schellmann, Malte, Cao, Hanwen, Eichinger, Joseph, Schober, Robert
In this paper, we focus on large intelligent reflecting surfaces (IRSs) and propose a new codebook construction method to obtain a set of pre-designed phase-shift configurations for the IRS unit cells. Since the complexity of online optimization and
Externí odkaz:
http://arxiv.org/abs/2203.01630
Publikováno v:
In Environmental Pollution 1 November 2024 360
Autonomous grasping is an important factor for robots physically interacting with the environment and executing versatile tasks. However, a universally applicable, cost-effective, and rapidly deployable autonomous grasping approach is still limited b
Externí odkaz:
http://arxiv.org/abs/2110.10924
Publikováno v:
In Food Chemistry 1 July 2024 445
Publikováno v:
IEEE ROBOTICS AND AUTOMATION LETTERS, VOL. 6, NO. 4, 2021
Suction is an important solution for the longstanding robotic grasping problem. Compared with other kinds of grasping, suction grasping is easier to represent and often more reliable in practice. Though preferred in many scenarios, it is not fully in
Externí odkaz:
http://arxiv.org/abs/2103.12311
Human attention mechanisms often work in a top-down manner, yet it is not well explored in vision research. Here, we propose the Top-Down Attention Framework (TDAF) to capture top-down attentions, which can be easily adopted in most existing models.
Externí odkaz:
http://arxiv.org/abs/2012.07248
Recent works of point clouds show that mulit-frame spatio-temporal modeling outperforms single-frame versions by utilizing cross-frame information. In this paper, we further improve spatio-temporal point cloud feature learning with a flexible module
Externí odkaz:
http://arxiv.org/abs/2008.05149