A robotic cleaning appliance includes a housing to which is coupled a surface treatment item and a sensor assembly with first and second transducers and an acoustic interface. The first sonic transducer transmits sonic signals through an acoustic interface and out of a first acoustic opening toward a surface beneath the robotic cleaning appliance. The sonic signals reflect from the surface as corresponding returned signals received by the second sonic transducer via a second acoustic opening port of the acoustic interface. A first plurality of annular rings is defined in the external surface around the first acoustic opening port and a second plurality of annular rings is defined in the external surface around the second acoustic opening port. The pluralities of annular rings attenuate direct path echoes from a subset of the transmitted sonic signals which attempt to travel across the external surface to the second acoustic opening port.

A method (1000) for determining a position deviation of a first node the method comprising obtaining (1110) input data, at a first and second position. Said input data comprises, an estimated position of the first node (p1) and a first velocity vector (v1) of the first node, obtaining (1120) the exact position (p.sub.2*) of the second node; obtaining (1125) the emitted frequency (f.sub.e) of an acoustic signal a source; (1130) receiving the acoustic signal (S) and measuring the observed frequency; calculating (1140) a second velocity vector (v12) which defines the velocity of the first node in relation to the second node; and calculating (1150), the angle () between the first velocity vector and the second velocity vector; determining (1160) based on the angle, the first velocity vector, and the estimated position of the first node, a line of direction (L) indicating the direction from the estimated position of the first node towards an estimated position of the second node, and determining (1300) based on a first and second line of direction an intersection point defining the estimated position of the second node (p.sub.2); determining (1400) a deviation vector (V.sub.d) corresponding to the difference between the estimated position of the second node and the exact position of the second node, and determining (1500) the position deviation of the first node which corresponds to the deviation vector. The disclosure further relates to a positioning system for determining a position deviation for a first node and an underwater vehicle.

Techniques are disclosed for systems and methods to provide remote sensing imagery for mobile structures. A remote sensing imagery system includes a radar assembly mounted to a mobile structure and a coupled logic device. The radar assembly includes an orientation and position sensor (OPS) coupled to or within the radar assembly and configured to provide orientation and position data associated with the radar assembly. The logic device is configured to receive radar returns corresponding to a detected target from the radar assembly and orientation and/or position data corresponding to the radar returns from the OPS, determine a target radial speed corresponding to the detected target, and then generate remote sensor image data based on the remote sensor returns and the target radial speed. Subsequent user input and/or the sensor data may be used to adjust a steering actuator, a propulsion system thrust, and/or other operational systems of the mobile structure.

Disclosed is a system for managing loads in a storage facility. The system comprises: a racking structure configured to store loads; mobile robot assembly(ies) (MRA(s)) operable to traverse within a storage facility, wherein MRA(s) comprises a mobile robot and a docking arrangement; and pick and drop robots (PDRs) operable to traverse along a height of storage facility for picking and dropping loads from racking structure, wherein PDRs comprise a latch arrangement and climb arrangement. Herein, MRA is operable to carry a PDR from amongst PDRs, PDR being operatively mounted on docking arrangement of MRA, wherein when MRA is at a first predefined distance from racking structure, PDR engages itself to racking structure via latch arrangement, and climb arrangement is configured to extend or retract vertically along a length of racking structure, for picking and dropping loads from racking structure upon engagement with racking structure.

In some embodiments, provided is a robot for water tunnel inspection, comprising: (a) a shell, comprising an upper shell and a lower shell; wherein the upper shell and the lower shell are sized and shaped to match each other, together defining a closed cavity therewithin; (b) a camera system, configured to capture an image or video of a field of view of surrounding; (c) a lighting system, configured to provide illumination at least partially for the field of view; (d) a propulsion system, configured to provide propulsion force to the robot in water; and (e) a controlling system, configured to provide power and control operation of the robot, wherein the robot is configured to float on water and to have a center of gravity positioned lower than geometric center. Other example embodiments are described herein. In certain embodiments, the robots provide safe and efficient tunnel inspections without human operation.

An autonomous driving control system for a vehicle that travels on a road provided with a marker that emits a steady magnetic field or a quasi-steady magnetic field includes: a magnetic sensor array that is equipped on the vehicle and senses magnetism; and an information processing circuit that generates an image showing a magnetic field in a region closer to the marker than the magnetic sensor array, according to a sensing result of the magnetism and a fundamental equation of the steady magnetic field and the quasi-steady magnetic field, and controls travel of the vehicle according to the image.

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.

A mobile internet of things (IoT) unit for cleaning grease vents, herein referred to as the unit, is disclosed. The unit is comprised of the following parts: a mobile platform with magnetic tracks; a mobile device software application (app); cleaning attachments such as power washers and lasers, sensors such as conductivity meters (to measure buildup), air temperature, velocity and pressure; recording devices such as digital still and streaming cameras; a microcontroller with wireless communications; onboard lighting and a rechargeable battery. Additional details regarding the unit are examined further in this disclosure.

A working system can perform work by a working mobile body remotely operated by a user, and includes an image generation unit configured to generate a virtual space image corresponding to a real space around the working mobile body, and a head-mounted display configured to be worn by the user and give the user the virtual space image generated by the image generation unit. The image generation unit generates the virtual space image corresponding to the position and direction of the working mobile body.

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.