Zobrazeno 1 - 10
of 1 404
pro vyhledávání: '"path finding"'
Publikováno v:
IEEE Access, Vol 12, Pp 139336-139345 (2024)
Federated learning is a distributed learning approach which can protect clients’ privacy. The success of federated learning depends on the participation of high quality clients. However, many works neglect how to incentivize high quality clients to
Externí odkaz:
https://doaj.org/article/c4e1daf0a7364afba3798757685a42dd
Publikováno v:
IEEE Access, Vol 12, Pp 62177-62188 (2024)
In this study, we propose and develop a Machine Learning-based metasolver for the Multi-Agent Path Finding (MAPF) problem, with the aim of selecting the most suitable solver based on the specific characteristics of the problem and a user-provided tim
Externí odkaz:
https://doaj.org/article/33a6cb1454474ea6ab0eee3e18242ff5
Publikováno v:
Nuclear Engineering and Technology, Vol 56, Iss 1, Pp 92-99 (2024)
To minimize the cumulative radiation dose, various path-finding approaches for single agent have been proposed. However, for emergence situations such as nuclear power plant accident, these methods cannot be effectively utilized for evacuating a larg
Externí odkaz:
https://doaj.org/article/7b0283c0e62546b8a286b307999b29de
Autor:
Thanaporn Arunthong, Laddawan Rianthakool, Khanchai Prasanai, Chakrit Na Takuathung, Sakchai Chomkokard, Wiwat Wongkokua, Noparit Jinuntuya
Publikováno v:
Applied Sciences, Vol 14, Iss 22, p 10674 (2024)
In this work, we propose the general idea of using a path-finding algorithm to solve a variational problem. By interpreting a variational problem of finding the function that minimizes a functional integral as a shortest path finding, we can apply th
Externí odkaz:
https://doaj.org/article/086b44f831014daa988455b08d0334b2
Publikováno v:
Journal of Agricultural Machinery, Vol 13, Iss 4, Pp 453-475 (2023)
IntroductionNowadays, machine vision systems are extensively used in agriculture. The application of this technology in the field can help preserve agricultural resources while reducing manual labor and production costs. In the field of agricultural
Externí odkaz:
https://doaj.org/article/6a4c08909edb42f1b5a7212d9b94b507
Publikováno v:
Information, Vol 15, Iss 9, p 518 (2024)
The more path conflicts between multiple robots, the more time it takes to avoid each other, and the more navigation time it takes for the robots to complete all tasks. This study designs a multi-robot navigation system based on deep reinforcement le
Externí odkaz:
https://doaj.org/article/3f05d87fce434813ba08b0e79c699cf8
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 36, Iss 1, Pp 101930- (2024)
Path finding is an essential problem in multi-agent systems, widely employed in warehousing and logistics. However, most of the current studies focus on the problem of assigning one agent with one task in a period, which may hinder the efficiency of
Externí odkaz:
https://doaj.org/article/3e8857c19a644cd9b8f2fe416242c3c8
Publikováno v:
Complex & Intelligent Systems, Vol 9, Iss 6, Pp 6767-6780 (2023)
Abstract Many existing multi-agent path finding (MAPF) solvers focus on completeness, speed, or optimization. However, completeness and rapidity are usually in conflict with each other, which makes these algorithms far from satisfactory in practical
Externí odkaz:
https://doaj.org/article/bb4bc1ba2dca49b0bfe6866e1a9957a4
Publikováno v:
Complex & Intelligent Systems, Vol 9, Iss 5, Pp 5937-5948 (2023)
Abstract Task scheduling (TS) and multi-agent-path-finding (MAPF) are two cruxes of pickup-and-delivery in automated warehouses. In this paper, the two cruxes are optimized simultaneously. Firstly, the system model, task model, and path model are est
Externí odkaz:
https://doaj.org/article/08436a26ac244eccbbc4a96ba37cc9bb
Publikováno v:
Applied Sciences, Vol 14, Iss 10, p 3960 (2024)
In this paper, we propose a hybrid centralized training and decentralized execution neural network architecture with deep reinforcement learning (DRL) to complete the multi-agent path-finding simulation. In the training of physical robots, collisions
Externí odkaz:
https://doaj.org/article/35ce9a61d7f94434a9023a8e7ef3858e