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pro vyhledávání: '"Guo, Teng"'
Autor:
Guo, Teng, Yu, Jingjin
Publikováno v:
Journal of Artificial Intelligence Research, vol. 81, pages 443-479, 2024
Multi-Agent Path Finding (MAPF) is NP-hard to solve optimally, even on graphs, suggesting no polynomial-time algorithms can compute exact optimal solutions for them. This raises a natural question: How optimal can polynomial-time algorithms reach? Wh
Externí odkaz:
http://arxiv.org/abs/2408.05385
Autor:
Guo, Teng, Yu, Jingjin
Multi-Robot Path Planning (MRPP) on graphs, equivalently known as Multi-Agent Path Finding (MAPF), is a well-established NP-hard problem with critically important applications. As serial computation in (near)-optimally solving MRPP approaches the com
Externí odkaz:
http://arxiv.org/abs/2402.11768
Autor:
Guo, Teng, Yu, Jingjin
Parking lots and autonomous warehouses for accommodating many vehicles/robots adopt designs in which the underlying graphs are \emph{well-connected} to simplify planning and reduce congestion. In this study, we formulate and delve into the \emph{larg
Externí odkaz:
http://arxiv.org/abs/2402.11766
Autor:
Guo, Teng, Yu, Jingjin
Path planning for multiple non-holonomic robots in continuous domains constitutes a difficult robotics challenge with many applications. Despite significant recent progress on the topic, computationally efficient and high-quality solutions are lackin
Externí odkaz:
http://arxiv.org/abs/2402.11767
Global feature based Pedestrian Attribute Recognition (PAR) models are often poorly localized when using Grad-CAM for attribute response analysis, which has a significant impact on the interpretability, generalizability and performance. Previous rese
Externí odkaz:
http://arxiv.org/abs/2312.06049
Autor:
Guo, Teng, Yu, Jingjin
At modern warehouses, mobile robots transport packages and drop them into collection bins/chutes based on shipping destinations grouped by, e.g., the ZIP code. System throughput, measured as the number of packages sorted per unit of time, determines
Externí odkaz:
http://arxiv.org/abs/2310.17753
Autor:
Guo, Teng, Yu, Jingjin
Optimal Multi-Robot Path Planning (MRPP) has garnered significant attention due to its many applications in domains including warehouse automation, transportation, and swarm robotics. Current MRPP solvers can be divided into reduction-based, search-b
Externí odkaz:
http://arxiv.org/abs/2306.14409
We study the problem of allocating many mobile robots for the execution of a pre-defined sweep schedule in a known two-dimensional environment, with applications toward search and rescue, coverage, surveillance, monitoring, pursuit-evasion, and so on
Externí odkaz:
http://arxiv.org/abs/2302.04319
Autor:
Guo, Teng, Yu, Jingjin
Parking in large metropolitan areas is often a time-consuming task with further implications toward traffic patterns that affect urban landscaping. Reducing the premium space needed for parking has led to the development of automated mechanical parki
Externí odkaz:
http://arxiv.org/abs/2302.01305
POI-level geo-information of social posts is critical to many location-based applications and services. However, the multi-modality, complexity and diverse nature of social media data and their platforms limit the performance of inferring such fine-g
Externí odkaz:
http://arxiv.org/abs/2211.01336