22 citations · 23 across the 4 of their papers we have counts for
6 papers
Socially Consistent Multi-Robot Navigation Using Decoupled Planning and Trajectory Coordination
Matthew M. Sato, Kincho H. Law
The successful integration of mobile robots in human-centric environments requires navigation that is not only safe and efficient, but also consistent and compliant with social con…
Cooperative Informative Sensing for Monitoring Dynamic Indoor Environments via Multi-Agent Reinforcement Learning
Kanghoon Lee, Matthew M. Sato, Jinnyeong Yang +7
Monitoring human activity in indoor environments is important for applications such as facility management, safety assessment, and space utilization analysis. While mobile robot te…
Generating Dispatching Rules for the Interrupting Swap-Allowed Blocking Job Shop Problem Using Graph Neural Network and Reinforcement Learning
Vivian W. H. Wong, Sang Hun Kim, Junyoung Park +2
The interrupting swap-allowed blocking job shop problem (ISBJSSP) is a complex scheduling problem that is able to model many manufacturing planning and logistics applications reali…
Automatic Volumetric Segmentation of Additive Manufacturing Defects with 3D U-Net
Vivian Wen Hui Wong, Max Ferguson, Kincho H. Law +2
Segmentation of additive manufacturing (AM) defects in X-ray Computed Tomography (XCT) images is challenging, due to the poor contrast, small sizes and variation in appearance of d…
Building Information Modeling and Classification by Visual Learning At A City Scale
Qian Yu, Chaofeng Wang, Barbaros Cetiner +4
In this paper, we provide two case studies to demonstrate how artificial intelligence can empower civil engineering. In the first case, a machine learning-assisted framework, BRAIL…
Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning
Max Ferguson, Ronay Ak, Yung-Tsun Tina Lee +1
Quality control is a fundamental component of many manufacturing processes, especially those involving casting or welding. However, manual quality control procedures are often time…