This paper introduces the RoboCup-Rescue Simulation Project, a contribution to the disaster mitigation, search and rescue problem. A comprehensive urban disaster simulator is constructed on distributed computers. Heterogeneous intelligent agents such as fire fighters, victims and volunteers conduct search and rescue activities in this virtual disaster world. A real world interface integrates various sensor systems and controllers of infrastructures in the real cities with the virtual world. Real-time simulation is synchronized with actual disasters, computing complex relationship between various damage factors and agent behaviors. A mission-critical man-mnchine interface provides portability and robustness of disaster mitigation centers, and augmented-reality interfaces f o r rescue parties an real disasters. It also provides a virtualreality training function for the Public. This diverse spectrum of RoboCup-Rescue cont:-ihtes to the creation of the safer social system.

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This field report describes two deployments of heterogeneous unmanned marine vehicle teams at the 2011 Great Eastern Japan Earthquake response and recovery by the Center for Robot‐Assisted Search and Rescue (USA) in collaboration with the International Rescue System Institute (Japan). Four remotely operated underwater vehicles (ROVs) were fielded in Minamisanriku and Rikuzentakata from April 18 to 24, 2011, for port clearing and victim recovery missions using sonar and video. The ROVs were used for multirobot operations only 46% of the time due to logistics. The teleoperated ROVs functioned as a dependent team 86% of the time to avoid sensor interference or collisions. The deployment successfully reopened the Minamisanriku New Port area and searched areas prohibited to divers in Rikuzentakata. The IRS‐CRASAR team planned to return from October 18 to 28, 2011, with an unmanned aerial vehicle (UAV), an autonomous underwater vehicle (AUV), and an ROV to conduct debris mapping for environmental remediation missions. The intent was to investigate an interdependent strategy by which the UAV and AUV would rapidly conduct low‐resolution scans identifying areas of interest for further investigation by the ROV. The UAV and AUV could not be used; however, the ROV was able to cover 80,000 m2 in 6 h, finding submerged wreckage and pollutants in areas previously marked clear by divers. The field work (i) showed that the actual and planned multirobot system configurations did not fall neatly into traditional taxonomies, (ii) identified a new measure, namely perceptual confidence, and (iii) posed five open research questions for multirobot systems operating in littoral regions. © 2012 Wiley Periodicals, Inc.

This study uses mobility statistics combined with business census data for the eight Japanese prefectures with the highest coronavirus disease-2019 (COVID-19) infection rates to study the effect of mobility reductions on the effective reproduction number (i.e., the average number of secondary cases caused by one infected person). Mobility statistics are a relatively new data source created by compiling smartphone location data; they can be effectively used for understanding pandemics if integrated with epidemiological findings and other economic data sets. Based on data for the first wave of infections in Japan, we found that reductions targeting the hospitality industry were slightly more effective than restrictions on general business activities. Specifically, we found that to hold back the pandemic (that is, to reduce the effective reproduction number to one or less for all days), a 20%–35% reduction in weekly mobility is required, depending on the region. A lesser goal, 80% of days with one or less observed transmission, can be achieved with a 6%–30% reduction in weekly mobility. These are the results if other potential causes of spread are ignored; for a fuller picture, more careful observations, expanded data sets, and advanced statistical modeling are needed.

Problem:Strategic action planning and scheduling (SAP) in the coordination of a disaster response team involves selecting and decomposing an objective into sub-goals, grouping available units into coalitions and assigning them to the sub-goals, allocating units to tasks, and adjusting the decisions that have been made. The primary responsibility of a team’s incident commander (IC) in SAP is to coordinate the actions of operational units in disaster crisis/emergency response management by making macro/strategic decisions.Objective:In this paper, we completely model a real-world problem and present data related to the SAP problem. This data model is used to support the design and development of an appropriate approach to SAP.Method:The employed methodology is to analyze and study the SAP problem, which is composed of six essential dimensions: the problem domain, geographic information, geospatial-temporal macro tasks, strategic action planning, strategic action scheduling, and team structure.Result:The contribution of this paper is the SAP problem data model. It is designed as a unified modeling language (UML) class diagram consisting of entity types, attributes, and relationships associated with SAP problem data modeling.Conclusion:To evaluate the quality of SAP data modeling, the SAP problem data model is used to propose and develop an intelligent assistant software system to assist and collaborate with incident commanders in SAP. The study makes five novel contributions: 1) a complete data model for SAP problem modeling, 2) a presentation and aggregation of task information in geographic objects, 3) the expression and encoding of human intuition as human high-level strategy guidance for SAP, 4) the formulation of a strategic action plan, and 5) the integration of strategic action schedule information with other entities.