A method for controlling a patrolling robot is provided. The method includes the steps of: acquiring, as first situation information on the patrolling robot, at least one of weight information on a support coupled to the patrolling robot and image information on the support and information on a location of the patrolling robot in a patrolling place; and determining a task and a travel route of the patrolling robot on the basis of the first situation information.

A robot control system includes: a mobile robot that controls movement of an object; an estimation unit that estimates a position of the object based on detection information of the object acquired by first sensors installed on base portions of the mobile robot; and a control unit that controls an end effector of the mobile robot. The control unit moves the end effector to the position estimated by the estimation unit, and controls the movement of the object using the end effector when a distance between the object and the second sensor acquired by the second sensor is equal to or less than a distance threshold value.

One variation of a method for autonomously scanning and processing a part includes: accessing a part model representing a part positioned in a work zone adjacent a robotic system; retrieving a sanding head translation speed; retrieving a toolpath for execution on the part defining positions, orientations, and target forces applied by the sanding head to the part. The method includes traversing the sanding head along the toolpath, at the sanding head translation speed; reading a sequence of applied forces from a force sensor coupled to the sanding head at positions along the toolpath; and deviating from the toolpath to maintain the set of applied forces within a threshold difference of a sequence of target forces along the toolpath. In one variation of the method, the robotic system executes a toolpath at a duration less than target duration by selectively varying target force and sanding head translation speed across the part.

The present disclosure provides an external parameter calibration method for robot sensors as well as an apparatus, robot and storage medium with the same. The method includes: obtaining first sensor data and second sensor data obtained through a first sensor and a second sensor of the robot by collecting position information of a calibration reference object and converting to a same coordinate system to obtain corresponding first converted sensor data and second converted sensor data, thereby determining a first coordinate and a second coordinate of a reference point of the calibration reference object; using the first coordinate and the second coordinate are as a set of coordinate data; repeating the above-mentioned steps to obtain N sets of the coordinate data to calculate the external parameter between the first sensor and the second sensor in response to a relative positional relationship between the robot and the calibration reference object being changed.

A characteristic estimation system, comprising circuitry configured to, cause a robot hand configured to grip an object to operate based on operation information defining an operation of the robot hand, acquire a physical quantity at a time when the robot hand grips the object, and estimate a characteristic of the object based on the physical quantity.

Provided are a robot positioning method and apparatus, an intelligent robot, and a storage medium. The method includes: configuring a camera and various sensors on a robot so that the robot may acquire an image collected by the camera and various sensing data collected by the various sensors (step 101); next extracting semantic information contained in the collected image (step 102) and identifying, according to the semantic information, a scenario where the robot is currently identified (step 103); finally, determining a current position of the robot according to target sensing data corresponding to the scenario where the robot is located (step 104). In the method, the sensing data used during determining the pose of the robot is not all the sensing data, but is the target sensing data corresponding to the scenario. Therefore, the basis for determining the pose is more targeted, thus further improving the accuracy of the pose.

A decoupling control method for a humanoid robot includes: decomposing tasks of the humanoid robot to obtain kinematic tasks and dynamic tasks, and classifying corresponding joints of the humanoid robot into kinematic task joints or dynamic task joints; solving desired positions and desired speeds of the kinematic task joints for performing the kinematic tasks according to desired positions and desired speeds of ends in the kinematic tasks using inverse kinematics; calculating torques of the kinematic task joints based on the desired positions and desired speeds of the kinematic task joints; and solving a pre-built optimization model of torques required for the dynamic task joints based on the calculated torques of the kinematic task joints, to obtain torques required by the dynamic task joints for performing the dynamic tasks.

Technology identifies that an end effector is provisioned to a robot. The technology accesses identification data of the end effector. The identification data is specific to the end effector. The identification data includes one or more of at least one setting associated with the end effector or at least one parameter associated with the end effector. The technology controls the end effector based on the identification data to adjust one or more runtime parameters of the robot based on the identification data.

A method, apparatus, and computer-readable storage media for robotic programming are disclosed. To improve upon or even solve the dilemma that teach-in techniques cannot work for all kinds of objects and offline programming requires complicated simulation of a robot and objects, a solution is provided to use a virtual item marked by a marker during programming of the robot and display the virtual item to a user. As such, even very large items can be used and also replaced easily during programming, which makes the programming procedures go smoothly and efficiently.

An object pose estimation system, an execution method thereof and a graphic user interface are provided. The execution method of the object pose estimation system includes the following steps. A feature extraction strategy of a pose estimation unit is determined by a feature extraction strategy neural network model according to a scene point cloud. According to the feature extraction strategy, a model feature is extracted from a 3D model of an object and a scene feature is extracted from the scene point cloud by the pose estimation unit. The model feature is compared with the scene feature by the pose estimation unit to obtain an estimated pose of the object.