Systems and methods for detecting glass for robots are disclosed herein. According to at least one non-limiting exemplary embodiment, a method for detecting glass objects using a LiDAR or light based time-of-flight (“ToF”) sensor is disclosed. According to at least one non-limiting exemplary embodiment, a method for detecting glass objects using an image sensor is disclosed. Both methods may be used in conjunction to enable a robot to quickly detect, verify, and map glass objects on a computer readable map.

Techniques are described for using computing devices to perform automated operations for determining the acquisition location of an image using an analysis of the image's visual contents. In at least some situations, images to be analyzed include panorama images acquired at acquisition locations in an interior of a multi-room building, and the determined acquisition location information includes a location on a floor plan of the building and in some cases orientation direction information—in at least some such situations, the acquisition location determination is performed without having or using information from any distance-measuring devices about distances from an image's acquisition location to objects in the surrounding building. The acquisition location information may be used in various automated manners, including for controlling navigation of devices (e.g., autonomous vehicles), for display on one or more client devices in corresponding graphical user interfaces, etc.

An autonomous mobile robot uses a capability-aware pathfinding algorithm to traverse from a start pose to an end pose efficiently and effectively. The robot receives a start pose and an end pose, and determines a primary path from the start pose to the end pose based on a primary pathfinding algorithm. The robot may smooth the primary path using Bezier curves. The robot may identify a conflict point on the primary path where the robot cannot traverse, and may determine a secondary path from a first point before the conflict point to a second point after the conflict point. The secondary path may use a secondary pathfinding algorithm that uses motion primitives of the robot to generate the secondary path based on the motion capabilities of the robot. The robot may then traverse from the start pose to the end pose based on the primary path and the secondary path.

The disclosure relates to a method for monitoring a pet by a robot based on a grid map and a chip. A mutual position relationship between the pet and the robot is determined through wireless communication between a wireless signal device on the pet and the robot, and then whether there is an obstacle cell or not between grid cells where the robot and the pet are located in the grid map is judged. If NO, it is indicated that the pet may be effectively shot at a present position and shooting direction of the robot, and the present position and shooting direction of the robot are not required to be changed. If YES, it is indicated that no pet but an obstacle may be shot at the present position of the robot.

A robot for moving in an administrator mode, can include a positioning sensor configured to sense a transmitter for calculating a position of the transmitter; an obstacle sensor configured to sense an obstacle around the robot; a driver configured to move the robot; and a controller configured to in response to receiving a signal from the transmitter, align the robot toward the position of a transmitter, move the robot toward the transmitter while avoiding one or more obstacles sensed by the obstacle sensor, and in response to no longer receiving the signal from the transmitter or a distance between the transmitter and the robot being equal to or less than a preset distance, stop the robot.

Systems and methods for data center operational monitoring are disclosed. In at least one embodiment, one or more automated robotic repair units to be directed toward points of interest along a flow line, verify information associated with points of interest, and perform one or more repair actions.

The present disclosure discloses a navigation map updating method as well as an apparatus, and a robot using the same. The method includes: controlling a robot to move along a designated path after a successful relocalization of the robot, and recording key frame data of each frame on the designated path and a corresponding pose; creating a new navigation map, and copying information in an original navigation map into the new navigation map; and covering the key frame data of each frame on the designated path onto the new navigation map to obtain an updated navigation map. In this manner, there is no need for the user to manipulate the robot to recreate the map at the environment where the robot is operated, which saves a lot of time and manpower.

The present application relates to a multi-sensor-fusion-based autonomous mobile robot indoor and outdoor positioning method and a robot. The method includes: acquiring GPS information and three-dimensional point cloud data of a robot at a current position; acquiring, a two-dimensional map corresponding to the GPS information of the robot at the current position; projecting the three-dimensional point cloud data of the robot at the current position onto a road surface where the robot is currently moving, to obtain two-dimensional point cloud data of the robot at the current position; and matching the two-dimensional point cloud data of the robot at the current position with the two-dimensional map corresponding to the GPS information of the robot at the current position, to determine the current position of the robot.

A logistics transport system includes individual transport cart of traveling by first driving unit and transporting a cargo to a delivery position; a transport vehicle of traveling by second driving unit and driving to the delivery position in loading the individual transport cart thereon to deliver a cargo or driving to an intermediate base point and then separating the individual transport cart therefrom at the intermediate base point; and a control unit configured to, when the cargo is delivered from a terminal at which the cargo is loaded to the delivery position, control the individual transport cart to deliver the cargo from the terminal to the delivery position by the individual transport cart, or control the transport vehicle to deliver the individual transport cart to the intermediate base point and then control the individual transport cart to deliver the cargo from the intermediate base point to the delivery position.

A method includes receiving sensor data representative of surfaces in a physical environment containing an interaction point for a robotic device, and determining, based on the sensor data, a height map of the surfaces in the physical environment. The method also includes determining, by inputting the height map and the interaction point into a pre-trained model, one or more candidate positions for a base of the robotic device to allow a manipulator of the robotic device to reach the interaction point. The method additionally includes determining a collision-free trajectory to be followed by the manipulator to reach the interaction point when the base of the robotic device is positioned at a selected candidate position of the one or more candidate positions and, based on determining the collision-free trajectory, causing the base of the robotic device to move to the selected candidate position within the physical environment.