A system includes a processor that collects and analyzes meteorological data and sensor data in real time, predicts future disaster risks based on past disaster data, detects abnormal patterns and identifies precursors of disasters, automatically issues warnings based on identified precursors, calculates optimal evacuation routes and provides them to users in real time, and supports information exchange between affected areas and relief teams.

Providing avatar support for trapped seismic event survivors is provided. A survivor is detected in debris of a collapsed structure using a set of sensors. An avatar communicates with the survivor. Fresh air flow drawn from different directions is measured to detect a pathway through the debris to an environment outside the collapsed structure. The pathway is detected through the debris to the environment outside the collapsed structure based on measuring the fresh air flow drawn from the different directions.

Aspects of drone-assisted offroad vehicle emergency systems are addressed. In one aspect, a system includes an offroad vehicle having integrated therein a processing system coupled to a user interface. The processing system is configured to respond to a potential emergency situation including a stranded or lost person or a hazardous condition in a path of the vehicle. An aerial drone is equipped with multiple sensors for receiving data. Responsive to a trigger, the processing system may instruct the drone to deploy one or more times to survey a target region. The drone may receive sensor data relevant to an emergency situation and relay the data back to the vehicle. The processing system analyzes the data to provide a remedial response.

According to one or more exemplary embodiments an integrated emergency response system designed to provide live multimedia reporting, drone-assisted surveillance, and AI-enhanced decision support in crisis situations, all while remaining distinct from prior systems may be provided. The system may include a user application, a responder application, and a command center that includes dispatch coordinate and AI integration.

Disclosed is a system for determining drone deployment configuration using simulation of autonomous drone operations under real-world constraints. A deployment simulation engine is executed to evaluate multiple drone deployment configurations based on real-world historical incident data, drone specification, and geospatial constraints. The simulation engine computes performance metrics including any of response time, energy-constrained on-station time, mission duration, and incident coverage levels. These performance metrics are evaluated against a design parameter such as target coverage thresholds or target on-station time. A drone deployment optimization engine then determines a drone deployment configuration that satisfies the design parameters. The deployment configuration typically includes deployment parameters such as number of drone hives, drone hive geolocations, or drones per hive.

An autonomous distributed coordination system for a broad-area search in an unknown environment without the need for a prior plan and map sharing. It includes: a sensor input processing section for acquiring (i) relative position information relative to another mobile body, (ii) path information in past and (iii) surrounding shape information; other mobile body avoidance module for generating, based on the relative position information, a first action candidate for avoiding the another mobile body; a past path avoidance module for generating, based on the path information, a second action candidate for avoiding the past path; an obstruct avoidance module for generating, based on the depth information, a third action candidate for avoiding a surrounding obstruct; and an integration module for determining a velocity or angular velocity of the mobile body based on the first action candidate, the second action candidate and the third action candidate.

An unmanned device for a marine environment comprises a location sensor configured to gather location data corresponding to the unmanned device; at least one propulsion system; a transmitter and memory including computer program code. The computer program code is configured to, when executed, cause the processor to cause the propulsion system to propel the unmanned device in a pattern along the body of water, cause the sonar transducer to emit the one or more sonar beams into the body of water, receive sonar return data corresponding to sonar returns, and generate a sonar image corresponding to the sonar return data. Further, the computer program code is configured to cause the processor to detect an object within the sonar image, assign a score to the object indicating the likelihood that the object is a desired object type, and send an alert to the remote electronics device upon assignment of the score.

The invention relates to a system which monitors the forest fires, plans to extinguish small-scale fires before they grow, and prepares an effective action plan for controlling and extinguishing large and widely spread fires, and to a method ensuring the operation of the system.

A fire detection and automatic extinguishing method and system using a robot includes a robot configured to, upon detecting a fire during patrol, calculate a distance to a fire location based on a rotation angle toward the fire location from its own position as a reference, move to the fire location, and then spray an extinguishing agent according to a remote instruction; and a control server configured to, when the robot has moved to the fire location and extinguishing preparations are complete, instruct the robot to spray the extinguishing agent.

An unmanned vehicle management system according to an aspect includes: a collection unit configured to collect video data acquired by an unmanned vehicle and natural disaster data related to natural disasters from information sources; a storage unit configured to store the video data and the natural disaster data; an analysis unit configured to extract feature amounts of the video data and the natural disaster data, and predict a high-risk area where a risk of natural disaster occurrence is higher than in other areas; a prediction unit configured to compare the video data and the natural disaster data collected during a disaster with the video data and the natural disaster data collected during normal times, and predict a disaster occurrence area where a disaster will occur; and a deployment unit configured to determine deployment of the unmanned vehicle and a rescuer based on the high-risk area and the disaster occurrence area.