In a ship control system of one embodiment, a route command unit outputs a route command including a target ship position and a target bow direction of a ship. An information detector detects ship information including an actual ship position and an actual bow direction of a ship. An external force vector estimation unit estimates an external force vector received by a ship, using the ship information and a hull motion model related to a hull motion of the ship. A control command unit controls both rotational frequency of a main engine and a rudder angle of a ship, based on the route command, the ship information, and the external force vector.

A positioning system includes an elevation angle calculator that determines, as a second elevation angle, an inverse cosine of a first value, upon occurrence of a condition in which an absolute value of a first elevation angle is greater than or equal to a second predetermined angle, the second predetermined angle being greater than a first predetermined angle, the first value being obtained by dividing a second value that is obtained by correcting, with a correction value, an altitude measured by an altitude measurement unit, by a distance measured by a distance measurement unit. The elevation angle calculator selects any one of the first elevation angle and the second elevation angle, based on a value of the first elevation angle.

The invention relates to a household robot, in particular an automatically movable cleaning robot for a floor area, having a housing, having running gear which is arranged on the underside of the housing, having a sensor system for detecting the area surrounding the housing and having a control for automatically controlling the running gear, in which the technical problem of preventing soiling by excrement from a living being is solved by providing detection element for detecting a sub-area of the floor area which has been soiled by excrement from a living being and by the control element changing the operating mode of the household robot dependent on an output signal from the detection element. The invention also relates to a method for operating a household robot.

Disclosed is a robot cleaner. The robot cleaner of the present disclosure comprise: a gas sensor which is disposed inside the robot cleaner and senses suctioned air; and a processor which identifies contaminants on the basis of a sensing value of the gas sensor, and controls the robot cleaner so that the robot cleaner travels while avoiding the identified contaminants.

An electrical system for an aircraft is disclosed, comprising: at least one processor configured to: receive first sensor data from at least one inertial sensor of the aircraft, wherein the first sensor data is indicative of a state of the aircraft, receive second sensor data from at least one of an airspeed sensor indicating an airspeed of the aircraft or a propeller speed sensor indicating a propeller speed of at least one propeller of the aircraft, and determine the state of the aircraft based on the first sensor data, wherein determining the state of the aircraft comprises filtering aircraft state measurements based on the second sensor data to lessen influence of propeller vibrations on at least one aircraft signal. The at least one processor is further configured to control the aircraft based on a pilot input command and the determined state of the aircraft.

The present invention relates to a safety compliance, identification, and security monitoring system that includes a monitoring device operable to capture images/videos of a predefined area and persons and events within the area. The monitoring device is further operable to determine whether persons within the area are wearing required personal protective equipment. In one embodiment, the monitoring device initiates an alert a safety equipment infraction. In one embodiment, the system is operable to further record an incident report identifying the nature, date, and location of the infraction, as well as the identity of the person committing the infraction. In one embodiment, the system is operable to monitor various external conditions, such as heat, humidity, smoke, and initiate an alert of the external condition detected. In one embodiment, the system detects quality assurance/quality control issues, including situations involving inspecting equipment, tools, and/or materials for non-conformance.

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.

Intelligent algorithms and systems (vehicles and/or mechanisms) locate and extract economic sound concentrations of e.g. any combinations of nodules, manganese crusts and/or sulphide deposit, and separate uneconomic matter from valuable minerals by applying differences in electric and/or acoustic properties to differentiate economically valuable minerals from cost bearing unprofitable other matters (e.g. mud, gravel, rocks, organic matter), thus providing added profitability compared to existing mining machines. Complex and multiple sophisticated technological fields, including, but not limited to Geophysics, Advanced sensor technology (acoustic and electric parameter detection), Signal processing (feature extraction and pattern recognition), Machine learning/AI (classification algorithms and adaptive systems), Mechanical engineering (precision collection mechanisms), Real-time control systems (feedback-based operation), Economic modeling (dynamic threshold determination) are combined. Both independent systems and add-on vehicles to existing mining machines have been developed. Environmental impacts are minimized by the nature of the invented technical solutions. MS and/or AI methods and algorithms can be incorporated.

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 positioning system includes an elevation angle calculator that determines, as a second elevation angle, an inverse cosine of a first value, upon occurrence of a condition in which an absolute value of a first elevation angle is greater than or equal to a second predetermined angle, the second predetermined angle being greater than a first predetermined angle, the first value being obtained by dividing a second value that is obtained by correcting, with a correction value, an altitude measured by an altitude measurement unit, by a distance measured by a distance measurement unit. The elevation angle calculator selects any one of the first elevation angle and the second elevation angle, based on a value of the first elevation angle.