Zobrazeno 1 - 10
of 50
pro vyhledávání: '"Han, Ruihua"'
Autor:
Ji, Zhiyou, Li, Guoliang, Han, Ruihua, Wang, Shuai, Bai, Bing, Xu, Wei, Ye, Kejiang, Xu, Chengzhong
Robotic data gathering (RDG) is an emerging paradigm that navigates a robot to harvest data from remote sensors. However, motion planning in this paradigm needs to maximize the RDG efficiency instead of the navigation efficiency, for which the existi
Externí odkaz:
http://arxiv.org/abs/2404.10541
Autor:
Han, Ruihua, Wang, Shuai, Wang, Shuaijun, Zhang, Zeqing, Chen, Jianjun, Lin, Shijie, Li, Chengyang, Xu, Chengzhong, Eldar, Yonina C., Hao, Qi, Pan, Jia
Navigating a nonholonomic robot in a cluttered environment requires extremely accurate perception and locomotion for collision avoidance. This paper presents NeuPAN: a real-time, highly-accurate, map-free, robot-agnostic, and environment-invariant ro
Externí odkaz:
http://arxiv.org/abs/2403.06828
Virtual reality (VR) is a promising data engine for autonomous driving (AD). However, data fidelity in this paradigm is often degraded by VR inconsistency, for which the existing VR approaches become ineffective, as they ignore the inter-dependency b
Externí odkaz:
http://arxiv.org/abs/2403.03541
This article investigates the practical scenarios of chasing an adversarial evader in an unbounded environment with cluttered obstacles. We propose a Voronoi-based decentralized algorithm for multiple pursuers to encircle and capture the evader by re
Externí odkaz:
http://arxiv.org/abs/2312.01326
Low-cost distributed robots suffer from limited onboard computing power, resulting in excessive computation time when navigating in cluttered environments. This paper presents Edge Accelerated Robot Navigation (EARN), to achieve real-time collision a
Externí odkaz:
http://arxiv.org/abs/2311.08983
Autor:
Zhang, Zeqing, Jia, Ruixing, Yan, Youcan, Han, Ruihua, Lin, Shijie, Jiang, Qian, Zhang, Liangjun, Pan, Jia
Proximity sensing detects an object's presence without contact. However, research has rarely explored proximity sensing in granular materials (GM) due to GM's lack of visual and complex properties. In this paper, we propose a granular-material-embedd
Externí odkaz:
http://arxiv.org/abs/2307.05935
Spatial-temporal graph learning has emerged as a promising solution for modeling structured spatial-temporal data and learning region representations for various urban sensing tasks such as crime forecasting and traffic flow prediction. However, most
Externí odkaz:
http://arxiv.org/abs/2306.10683
Autonomous parking (AP) is an emering technique to navigate an intelligent vehicle to a parking space without any human intervention. Existing AP methods based on mathematical optimization or machine learning may lead to potential failures due to eit
Externí odkaz:
http://arxiv.org/abs/2305.13663
Autor:
Han, Ruihua, Wang, Shuai, Wang, Shuaijun, Zhang, Zeqing, Zhang, Qianru, Eldar, Yonina C., Hao, Qi, Pan, Jia
Autonomous motion planning is challenging in multi-obstacle environments due to nonconvex collision avoidance constraints. Directly applying numerical solvers to these nonconvex formulations fails to exploit the constraint structures, resulting in ex
Externí odkaz:
http://arxiv.org/abs/2210.00192
In this paper, we introduce a generalized continuous collision detection (CCD) framework for the mobile robot along the polynomial trajectory in cluttered environments including various static obstacle models. Specifically, we find that the collision
Externí odkaz:
http://arxiv.org/abs/2206.13175