A system for moving an object against a working surface of a finishing machine. A mounting platform is provided that is supported by a stationary frame. The mounting platform can only move reciprocally relative to the stationary frame along a linear line of motion. An articulating arm is mounted on the mounting platform and moves with the mounting platform. A linear actuator is provided having a first end coupled to the mounting platform and an opposite end mounted to the stationary frame. The linear actuator has a midline that is parallel to, and aligned with, the linear line of motion. A finishing machine is provided that has a working surface. The articulating arm touches objects to the working surface at a point of contact that is coplanar with the linear line of motion. This directs forces along the linear line of motion and into the linear actuator.

A method and a device for controlling a number of robots to emergency stop are provided. The method includes arranging multiple position points in a site where robots work and position identifiers corresponding to the position points, and providing corresponding recognizers at bottoms of the robots; when fault signals reported by the robots are detected, determining, according to the fault signals, whether all the robots in the site need to be controlled to emergency stop, wherein if yes, according to current positions and moving speeds of the robots, position points in the site are allocated to the robots as respective emergency stop positions, the robots are controlled to move to the corresponding emergency stop positions, and thereafter the robots are controlled to stop movement. The device comprises a configuration module, a determination module, an allocation module and a control module.

The present disclosure provides a foot-waist coordinated gait planning method and an apparatus and a robot using the same. The method includes: obtaining an orientation of each foot of the legged robot, and calculating a positional compensation amount of each ankle of the legged robot based on the orientation of the foot; obtaining an orientation of a waist of the legged robot, and calculating a positional compensation amount of each hip of the legged robot based on the orientation of the waist; calculating a hip-ankle positional vector of the legged robot; compensating the hip-ankle positional vector based on the positional compensation amount of the ankle and the positional compensation amount of the hip to obtain the compensated hip-ankle positional vector; and performing an inverse kinematics analysis on the compensated hip-ankle positional vector to obtain joint angles of the legged robot.

Dynamically adjusting, using artificial intelligence (AI), sensors and models of an autonomous roaming robotic device, which includes receiving data regarding an asset at a computer of a roaming robotic device from sensors on the robotic device. The robotic device identifies an asset at a location using the sensors, and the robotic device has instructions, received from a control system, to inspect the location or items at the location. The data is analyzed using the computer of the robotic device, and the analysis includes using historical data for the asset. An AI model is loaded using the computer of the robotic device, based on the identification of the asset. A sensor is selected using the computer of the robotic device, for conducting an inspection of the asset based on the analysis of the data and the AI model.

An automated carrier system is disclosed for moving objects to be processed. The automated carrier system includes a discontinuous plurality of track sections on which an automated carrier may be directed to move, and the automated carrier includes a base structure on which an object may be supported, and at least two wheels assemblies being pivotally supported on the base structure for pivoting movement from a first position to a second position to effect a change in direction of movement of the carrier.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for audio augmentation of physical robot sounds. A robot can determine that a first physically moveable component of the robot is to be actuated and in response, obtain a conditional state of the robot. The robot can obtain an audio object that generates an audio enhancement for the first physically moveable component being actuated, the audio enhancement having one or more characteristics that match the obtained conditional state of the robot. The robot can output the audio enhancement while actuating the first physically moveable component.

A robot system includes a robot arm driven by an electric motor and a vehicle that is movable and supports the robot arm. A control method includes (a) moving the vehicle to a work station of a first type and (b) driving the robot arm in the work station of the first type. The (a) executes a first operation mode for, in a part of the movement to the work station of the first type, moving the vehicle in a state in which electric power is not supplied to the electric motor, starting supply of the electric power to the electric motor during the movement of the vehicle in the state in which the electric power is not supplied to the electric motor, and arranging the vehicle in the work station of the first type in a state in which the electric power is supplied to the electric motor.

A computing device performs computation for controlling operations of a mobile manipulator configured to hold a plurality of target objects with a manipulator and move the target objects to predetermined positions. The computing device includes a storage and a calculator. The storage stores a trained machine learning model trained by inputting a plurality of training data sets, which are combinations of state variables and pieces of determination data associated with the state variables. The training data sets are acquired in advance. The calculator outputs a movement-target object to be moved to a predetermined position at current time by inputting the state variable to the trained machine learning model read from the storage. The state variable contains relative positions of the target objects to a specific portion of the mobile manipulator. The determination data associated with the state variable represents the movement-target object.

A multi-sensor fusion SLAM system and a robot. The system operates on a mobile robot and comprises: a visual inertia module, a laser scanning matching module, a loop closure detection module, and a visual laser image optimization module. According to the multi-sensor fusion SLAM system and the robot, the calculation amount of laser matching constraint optimization can be reduced by using a voxel subgraph so that the pose calculation is more accurate, accumulated errors of long-time operation of the system can be corrected in time by means of sufficient fusion of modules, and the robustness of the system and the accuracy of positioning and mapping are integrally improved.

Systems and methods for determining movement of a robot are provided. A computing system of the robot receives information including an initial state of the robot and a goal state of the robot. The computing system determines, using nonlinear optimization, a candidate trajectory for the robot to move from the initial state to the goal state. The computing system determines whether the candidate trajectory is feasible. If the candidate trajectory is feasible, the computing system provides the candidate trajectory to a motion control module of the robot. If the candidate trajectory is not feasible, the computing system determines, using nonlinear optimization, a different candidate trajectory for the robot to move from the initial state to the goal state, the nonlinear optimization using one or more changed parameters.