The invention relates to a method for detecting and/or locating a forest fire, comprising the following method steps: receiving information, generating a control command, sending the control command and/or information and carrying out a forest fire detection process. The invention further relates to a forest fire detection system comprising a network device, a server unit, a gateway, a first terminal, the first terminal having a sensor unit, and a second terminal, the second terminal having a forest fire detection element.

The present disclosure relates to a system that comprises a lab space housing multiple workstations comprising at least two workstations each performing a different type of bio lab task from another. The lab space can have a lab floor space comprising an occupied lab floor space on which the multiple workstations are occupied, and an unoccupied lab floor space on which a stand-alone robotic arm moves through.

Techniques for extracting data from a remotely located edge node are disclosed. A service causes a drone to travel a flight path to reach a designated region within which the edge node is located. While the drone is within the designated region, the service establishes a LiFi communication session with the edge node. The service then collects data from the edge node. The service terminates the LiFi communication session. The service causes the drone to continue along the flight path until reaching an infrastructure node. The service transmits the collected data to the infrastructure node.

Aspects of the subject disclosure may include, for example, receiving a user selection relating to a course, transmitting a request to a controller in a vehicle for information regarding capabilities of available devices onboard the vehicle, wherein the available devices include uncrewed aerial vehicles (UAVs), based on the transmitting, obtaining, from the controller, the information regarding the capabilities, responsive to the obtaining, sending a command to the controller to facilitate deployment of one or more of the UAVs to collect data for the course, and after the sending, receiving the data from the controller and incorporating the data into the course for delivery to one or more users onboard the vehicle. Other embodiments are disclosed.

Disclosed herein are systems, devices, and apparatuses for improved perception/sensing systems in robots or other vehicles. The system receives sensor data representative of a field of view of a robot and determines, based on the sensor data and an object detection model, an identification of an object within the field of view and an accuracy metric of the identification of the object, wherein the object detection model relates the sensor data to the identification. The system also requests, based on the accuracy metric, an informational feedback from the identification of the object and updates the object detection model to an updated object detection model based on the informational feedback.

An autonomous system for detecting, localizing, and potentially deactivating chemical threats or emissions using multiple sensing modalities and reinforcement learning techniques. The system includes visual sensors (e.g., RGB, RGBD, LIDAR), non-visual sensors (e.g., gas concentration, airflow, GPS, RADAR), a neural network architecture and processor to fuse information from different sensors, a module based on deep reinforcement learning for decision making, and a robotic interface for executing actions. The neural network extracts relevant information from sensor streams and encodes them into a joint embedding space. The module considers the current observations, historical data, and previous actions to determine the optimal action for threat localization under partially observable conditions. The system is trained in simulated environments to minimize source localization time while accounting for various constraints. The autonomous system enables effective chemical threat detection and source localization in complex, dynamic environments without endangering human operators.

A method is provided for managing and tracking scouting tasks to obtain map information using a fleet of autonomous vehicles. For instance, the method includes defining a scouting quest to obtain the map information. The scouting quest includes a plurality of objectives. Each objective is associated with a geographic location from which sensor data is to be captured. The method also includes receiving a first update message from an autonomous vehicle of the fleet. The update message identifies a location of the autonomous vehicle. The method also includes assigning at least one of the objectives to the autonomous vehicle based on the location of the autonomous vehicle. The method also includes sending instructions to the autonomous vehicle in order to cause the autonomous vehicle to complete the at least one objective and after sending, tracking a status of the scouting quest.

The present disclosure relates to the field of unmanned aerial vehicles (UAV), and discloses a gimbal control method, a controller, an unmanned aerial vehicle and an unmanned aerial vehicle inspection system. The gimbal control method, applied to a UAV, acquires inspection information about the UAV, including an observation flight leg, an observation interval corresponding to the observation flight leg, a total flight range corresponding to the observation interval and the current flight range. Then, the observation progress of the UAV is determined according to the current flight range and the total flight range. A first position, position of center point of the field of view of the nacelle of UAV, is determined according to the observation progress and the observation interval. Finally, the angle of the gimbal of the UAV is controlled according to the first position and the current position of the UAV.

Provided are a method and apparatus for inspecting an aircraft located on a ground surface. The apparatus includes a mobility system that transports the apparatus over the ground surface, and sensor circuitry supported by the mobility system. The sensor circuitry includes a proximity sensor that captures proximity data indicative of a presence of the aircraft adjacent to the apparatus, and a damage sensor that captures damage data in response to inspecting a portion of the aircraft for potential damage. A computing system executes computer-executable instructions to detect the presence of the aircraft based on the proximity data, and the potential damage to the aircraft based on the damage data. An indication system issues an alert in response to the potential damage being detected, and a transmitter transmits data indicative of the potential damage to a remote terminal for inclusion in a database entry specific to the aircraft.

This automated analysis support robot for carrying out an inspection of an analysis module that automatically analyzes a biological sample comprises a vehicle body, a camera mounted on the vehicle body, a communication device which communicates directly or indirectly with the analysis module, and a computer for controlling the vehicle body and the camera, wherein the computer: controls the vehicle body to move to a predetermined operating position and to face an inspection target provided in the analysis module; images the inspection target using the camera; and processes a video of the inspection target to calculate management data relating to the inspection target.