Disclosed in the present invention is a shuttle vehicle traveling and positioning control method based on encoder self-correction. Provided is a self-correction solution based on track positioning identifiers and external encoders. When a shuttle vehicle travels through each identifier, information is fed back and a servo target position is updated instantaneously, so as to eliminate, at any time, a cumulative error caused by skidding; and the shuttle vehicle realizes a full-closed-loop traveling and positioning control process under the guidance of position information which is corrected at any time. The shuttle vehicle traveling and positioning control method based on encoder self-correction comprises the following implementation stages: 1) performing customization and initialization; 2) performing self-learning; 3) performing self-correction; 4) updating a target position; and 5) handling a position offset.

A method for determining a motion path on a surface in an environment, along which motion path a mobile appliance, in particular a robot, preferably a domestic robot or a robot vacuum cleaner, is intended to move. The method includes obtaining environment information and determining a region of the surface intended to be covered by the motion of the mobile appliance; determining, while taking into account the environment information, whether within the region there is at least one uneven area in the surface that can be negotiated by the mobile appliance; and determining the motion path while taking into account the at least one uneven area, if there is one. A mobile appliance is also described.

A method and system are provided for characterizing a vehicle motion of an autonomous mobile robot in response to a triggering event. The method and system involve an autonomous mobile robot and a vehicle processor operable to navigate the autonomous mobile robot. The system further includes a motion characterization system coupled to the autonomous mobile robot, the motion characterization system comprising an odometry system operable to collect vehicle motion data associated with the vehicle motion; a triggering component; a storage component for storing an event start time, an event end time and the vehicle motion data between the event start time and the event end time; and a motion characterization processor operable to: receive an initialization input to initiate the triggering event; generate a trigger signal to cause the triggering component to cause the triggering event; and identify the event start time and an event end time.

An external device for use with one or more robotic tools, the external device including a display and an electronic processor, where when an initiate setup button is selected by a first user input, the processor is configured to send a signal to the first robotic garden tool to travel from a dock and along a perimeter of an operating area. When the add start point button is selected by a second user input, the processor is configured to retrieve a first position of the first robotic garden tool, the first position being indicative of a first start point remote of the dock, and where the first robotic garden tool is configured to return to the dock after traveling along the perimeter and to communicate a calculated boundary length based on the data gathered by the odometry unit to the processor.

An autonomous driving robot includes a driving unit that moves the autonomous robot; a camera; a traveling distance measurement sensor; and a control unit that estimates a location of the autonomous robot using a captured image and traveling distance information. In this case, the operation control program generates a robot viewpoint map based on the image captured by the camera, estimates a location of the autonomous robot based on the robot viewpoint map and the measured traveling distance information, and generates a global map based on the robot viewpoint map and position estimation information, and the operation control program inputs the generated robot viewpoint map and global map into a style-transfer model, and inputs a style-transferred robot viewpoint map and a style-transferred global map output by the style-transfer model into the operation agent to correct the estimated position.

A mobile body controller according to the present disclosure includes circuitry configured to recognize an environment surrounding a mobile body to be controlled, and change parameters used for self-position estimation by the mobile body based on the recognized environment.

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.

A system and method of an adaptive mapping system for semi-autonomous cleaning devices for improved navigation using a randomized dot pattern to represent dynamic areas and ensure precise localization in changing environments. A map is parameterized as an occupancy grid, where each cell is assigned the likelihood that it contains a physical object in the environment. A novel mapping technique is disclosed that intelligently distinguishes between static features (e.g., walls and pillars) and dynamic areas (e.g., places prone to frequent changes). By representing dynamic areas with a randomized dot pattern, an adaptive mapping system maintains high localization confidence for autonomous mobile robots (AMRs). This approach ensures uninterrupted robot operations, significantly reducing or eliminating the need for human intervention due to localization uncertainties and addresses the critical problem of navigating and operating efficiently in environments that undergo frequent changes.

There is provided a mobile body that includes an imaging unit to capture an image of an environment around the mobile body, an estimation unit to estimate a position of the mobile body on the basis of the image captured by the imaging unit, a calculation unit to calculate the position of the mobile body on the basis of a control command for controlling movement of the mobile body, and a wind information calculation unit to calculate information regarding wind acting on the mobile body on the basis of a first position that is the position of the mobile body, which is estimated by the estimation unit, and a second position that is the position of the mobile body, which is calculated by the calculation unit.

Systems and methods provide for estimating a trajectory of a robot by fusing a plurality of robot velocity measurements from a plurality of robot sensors located within a robot to generate a fused robot velocity based on the accuracy of those robot velocity measurements and applying Kalman filtering to the fused robot velocity to compute a current robot location.