Zobrazeno 1 - 10
of 3 456
pro vyhledávání: '"Occupancy grid map"'
Safe control in unknown environments is a significant challenge in robotics. While Control Barrier Functions (CBFs) are widely used to guarantee system safety, they often assume known environments with predefined obstacles. The proposed method constr
Externí odkaz:
http://arxiv.org/abs/2405.10703
Autor:
Jang, Harin1 (AUTHOR) tjsdud9536@kookmin.ac.kr, Kim, Taehyun1 (AUTHOR) martin6773@kookmin.ac.kr, Ahn, Kyungjae1 (AUTHOR) kahn@kookmin.ac.kr, Jeon, Soo2 (AUTHOR) soojeon@uwaterloo.ca, Kang, Yeonsik1 (AUTHOR) ykang@kookmin.ac.kr
Publikováno v:
Sensors (14248220). May2024, Vol. 24 Issue 9, p2828. 18p.
Several studies rely on the de facto standard Adaptive Monte Carlo Localization (AMCL) method to localize a robot in an Occupancy Grid Map (OGM) extracted from a building information model (BIM model). However, most of these studies assume that the B
Externí odkaz:
http://arxiv.org/abs/2308.05443
Akademický článek
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Recent advances in LiDAR technology have opened up new possibilities for robotic navigation. Given the widespread use of occupancy grid maps (OGMs) in robotic motion planning, this paper aims to address the challenges of integrating LiDAR with OGMs.
Externí odkaz:
http://arxiv.org/abs/2302.14819
Publikováno v:
Sensors, Vol 24, Iss 9, p 2828 (2024)
In the field of robotics and autonomous driving, dynamic occupancy grid maps (DOGMs) are typically used to represent the position and velocity information of objects. Although three-dimensional light detection and ranging (LiDAR) sensor-based DOGMs h
Externí odkaz:
https://doaj.org/article/03adc1e8d3364dcca1d2df437dce1902
Publikováno v:
IEEE Access, Vol 12, Pp 74724-74736 (2024)
In recent years, indoor mobile robots have played an increasingly important role in various home, medical, commercial, and industrial applications. However, mirror surfaces commonly found in indoor environments pose challenges to the localization and
Externí odkaz:
https://doaj.org/article/429f3854bb934a1486ea115e14ebbb5d
Autor:
Xie, Zhanteng, Dames, Philip
This paper presents two variations of a novel stochastic prediction algorithm that enables mobile robots to accurately and robustly predict the future state of complex dynamic scenes. The proposed algorithm uses a variational autoencoder to predict a
Externí odkaz:
http://arxiv.org/abs/2210.08577
Publikováno v:
2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), 2022, pp. 805-810
The advance towards higher levels of automation within the field of automated driving is accompanied by increasing requirements for the operational safety of vehicles. Induced by the limitation of computational resources, trade-offs between the compu
Externí odkaz:
http://arxiv.org/abs/2207.01902
Publikováno v:
IEEE Access, Vol 11, Pp 23517-23530 (2023)
The presence of mobile robots in indoor environments is becoming increasingly significant. While there are algorithms such as SLAM that allow robots to navigate these environments, they do not take into account structural elements of modern buildings
Externí odkaz:
https://doaj.org/article/1b24214d89d747679fecb5c36f222ca8