Disclosed is a method for determining the position of a vehicle, in which highly accurate localization data for positions of the vehicle are determined when traveling along a route. Odometric measured variables for the own odometry of the vehicle are captured when traveling along the route. The highly accurate localization data determined and the odometric measured variables captured are jointly evaluated. The own odometry of the vehicle is corrected on the basis of the evaluation. An error model is used to calculate a vehicle-specific drift for the own odometry of the vehicle and to continually correct the own odometry of the vehicle provided highly accurate localization data can be determined. The corrected own odometry of the vehicle can then be used in areas in which highly accurate localization data cannot be determined in order to determine the position of the vehicle.

A robotic lawn mower includes a mowing element, a body, a drive assembly, a first detection module, a second detection module, a failure determination module, an execution module, and a control module. The first detection module detects a first journey of the robotic lawn mower in a period. The second detection module detects a motion parameter of the drive assembly in the period and calculates a second journey of the robotic lawn mower in the period. The failure determination module determines whether a difference between the second journey and the first journey is greater than or equal to a first preset value. The execution module drives the robotic lawn mower to execute a response program. When the difference is greater than or equal to the first preset value in each of n1 consecutive periods, the control module controls the execution module to execute the response program.

The present disclosure proposes a method and an apparatus for recognizing a stuck status as well as a computer storage medium, with the method comprising: building an environmental map within a preset extent by taking the current position of the mobile robot as center; real-time monitoring the march information of the mobile robot and predicting whether the mobile robot is stuck or not; acquiring data from multiple sensors of the mobile robot, if the mobile robot is stuck; and recognizing the current stuck status of the mobile robot based on the data from multiple sensors.

The present disclosure discloses a map creation method of a mobile robot, the mobile robot working indoors, comprising the following steps: S1: obtaining Euler angles of a current point relative to a reference point according to a ceiling image taken from the current point and the reference point; S2: determining whether the roll angle of the Euler angles is lower than a set value, if so, saving the map data of the current point, otherwise, not saving the map data of the current point; S3: returning to step S1 after the mobile robot moves a predetermined distance or for a predetermined time; S4: repeating steps S1 through S3 until the map creation in the working area is complete. The present disclosure also discloses a mobile robot using the above method.

Disclosed herein are a robot that generates a map and configures a correlation of nodes, based on multi sensors and artificial intelligence, and that moves based on the map, and a method of generating a map, and the robot according to an embodiment generates a pose graph comprised of LiDAR branch, visual branch, and backbone, and the LiDAR branch includes one or more of the LiDAR frames, the visual branch includes one or more of the visual frames, and the backbone includes two or more frame nodes registered with any one or more of the LiDAR frames or the visual frames, and to generate a correlation between nodes in the pose graph.

The present disclosure provides a neural network-based method for calibration and localization of an indoor inspection robot. The method includes the following steps: presetting positions for N label signal sources capable of transmitting radio frequency (RF) signals; computing an actual path of the robot according to numbers of signal labels received at different moments; computing positional information moved by the robot at a t.sup.th moment, and computing a predicted path at the t.sup.th moment according to the positional information; establishing an odometry error model with the neural network and training the odometry error model; and inputting the predicted path at the t.sup.th moment to a well-trained odometry error model to obtain an optimized predicted path. The present disclosure maximizes the localization accuracy for the indoor robot by minimizing the error of the odometer with the odometry calibration method.

A materials handling vehicle includes a camera, odometry module, processor, and drive mechanism. The camera captures images of an identifier for a racking system aisle and a rack leg portion in the aisle. The processor uses the identifier to generate information indicative of an initial rack leg position and rack leg spacing in the aisle, generate an initial vehicle position using the initial rack leg position, generate a vehicle odometry-based position using odometry data and the initial vehicle position, detect a subsequent rack leg using a captured image, correlate the detected subsequent rack leg with an expected vehicle position using rack leg spacing, generate an odometry error signal based on a difference between the positions, and update the vehicle odometry-based position using the odometry error signal and/or generated mast sway compensation to use for end of aisle protection and/or in/out of aisle localization.

Provided herein is a system comprising: one or more processors; and a memory storing instructions that, when executed by the one or more processors, causes the system to perform: obtaining a previous pose of a vehicle; acquiring one or more previous readings corresponding to one or more wheel encoders during the previous pose; acquiring one or more readings corresponding to one or more wheel encoders acquired after the previous pose; and adjusting the previous pose based on the one or more readings to obtain a current pose.

Disclosed is a system for operating a Programmable Logic Controller (PLC) for each movement position of a logistics robot, and a method thereof. A system for operating a PLC for each movement position of a logistics robot according to an exemplary embodiment of the present disclosure includes: a logistics robot configured to supply a necessary component for each process at an industrial site; a PLC which is installed in each process and controls one or more connected equipment; and a server which allocates a transport path of a component to the logistics robot, controls an interworking operation of the equipment based on a movement position of the logistics robot through an input of a PLC memory value of the PLC, traces a control history of each equipment, and recognizes whether the equipment is normally operated and the input of the PLC memory value is omitted.

A method for correcting a position of a vehicle when parking in a parking space. The method includes determining the position of the vehicle on the basis of odometry information, sensing ultrasonic signals from a linear object, carrying out a method for simultaneous localization and mapping (SLAM) on the basis of the linear object and the ultrasonic signals, and correcting the position of the vehicle A control device for a driving support system of a vehicle is also disclosed, which is designed to receive odometry information of the vehicle, to receive ultrasonic signals from at least one ultrasonic sensor of the driving support system, and to carry out the aforementioned method. A driving support system for a vehicle with an aforementioned control device and with at least one ultrasonic sensor is disclosed. The invention likewise relates to a vehicle with an aforementioned driving support system.