A mobile robot is configured for operation in a commercial or industrial setting, such as an office building or retail store. The mobile robot may include cameras for capturing images and videos and include microphones for capturing audio of its surroundings. To improve privacy by preventing confidential information from being transmitted, the mobile robot may detect text in images and modify the images to make the text illegible before transmitting the images. The mobile robot may also detect human voice in audio and modify audio to make the human voice unintelligible before transmitting the audio.

Provided are a service robot and a display control method thereof, a controller and a storage medium. The service robot display control method comprises receiving a start signal sent by a human body recognition sensor, wherein the human body recognition sensor outputs the start signal to a controller in the case where a user appears within a predetermined range around the service robot, and controlling the mounted device to start operation in the case where the start signal is received. A first display screen of the robot is in a standby state when there is no user, and when a user approaches the robot, the first display screen starts to light up.

A robot position calibration apparatus is disclosed including a scan position controller configured to control the position of the robot by individually setting parameter sets related to the position of the robot for causing a scanner mounted on an end of the robot to scan an object in multiple scan positions around the robot, and a data receiver configured to receive, from the scanner, multiple scan data items generated by the scanner scanning the object in each of the multiple scan positions, and a parameter calibrator configured to calculate calibration values for the parameter sets having been individually set, by using multiple position information items corresponding to the parameter sets and the multiple scan data items.

A visual recognition based method for projecting patterned light includes: projecting a calibration image onto a projection screen by a projection module; capturing the calibration image by an image-capturing module to obtain a calibration information between the projection module and the image-capturing module; capturing an object by the image-capturing module to obtain a to-be-recognized image of the object; detecting the object in the to-be-recognized image and acquiring a plurality of feature points associated with a plurality of feature areas of the object in the to-be-recognized image; retrieving a plurality of target feature points corresponding to a target object from the feature points; obtaining a projection coordinate of the target feature points\according to the calibration information and providing the projection coordinate to the projection module; and projecting a projection pattern with shape corresponding to the target object onto the object by the projection module according to the projection coordinate.

One embodiment can provide a robotic system. The system can include a machine-vision module, a robotic arm comprising an end-effector, a robotic controller configured to control movements of the robotic arm, and an error-compensation module configured to compensate for pose errors of the robotic arm by determining a controller-desired pose corresponding to a camera-instructed pose of the end-effector such that, when the robotic controller controls the movements of the robotic arm based on the controller-desired pose, the end-effector achieves, as observed by the machine-vision module, the camera-instructed pose. The error-compensation module can include a machine learning model configured to output an error matrix that correlates the camera-instructed pose to the controller-desired pose.

An approach to positioning one or more robotic arms in an assembly system may be described herein. For example, an apparatus may include a first robotic arm having a distal end and a proximal end. The distal end may be configured for movement and the proximal end may secure the first robotic arm. The apparatus may further include a camera connected with the distal end of the first robotic arm. The camera may be configured to capture image data of a marker connected with a second robotic arm and provide the image data to a computer. The computer may generate a set of instructions for the first robotic arm based on the image data of the marker. The movement of the first robotic arm may be caused by the computer according to the generated set of instructions.

A robot management system (RMS) includes a plurality of service robots deployed within an operations venue that includes a plurality of gaming devices, an operator terminal presenting a graphical user interface (GUI) to an operator, and a robot management system server (RMS server) configured in networked communication with the plurality of service robots. The RMS server is configured to: identify location data for the service robots; create an interactive overlay map of the operations venue that includes a static map of the operations venue, overlay data showing the location data of the plurality of service robots over the static map, and an interactive icon for each service robot of the plurality of service robots; display, via the GUI, the overlay map; receive a first input indicating a selection of a first interactive icon associated with a first service robot; and display current status information associated with the first service robot.

The invention provides a nursing robot for monitoring critically ill patients, which relates to the field of medical robots, including a robot body, an image taking module arranged at the head of the robot body, a walking device arranged at the bottom of the robot body, a power source module, a processor module, a path finding module, a wireless communication module and a vital sign monitor sampling device arranged in the robot body The video acquisition module is used to collect the video data of the target and send the video data to the cloud server through the wireless communication module. The vital signs monitor sampling module is used to collect the monitoring data of the vital signs monitor and analyze the monitoring data through the processor module to predict the target The development of the disease.

A surgical robot includes a controller configured or programmed to control a display to superimpose guide information indicating a moving direction of a medical cart based on a steering angle of a steering device on an image captured by an imaging device and display the guide information.

Systems and methods for detecting a waste receptacle, the system including a camera for capturing an image, a convolutional neural network, and processor. The convolutional neural network can be trained for identifying target waste receptacles. The processor can be mounted on the waste-collection vehicle and in communication with the camera and the convolutional neural network configured for using the convolutional neural network. The processor can be configured for using the convolutional neural network to generate an object candidate based on the image; using the convolutional neural network to determine whether the object candidate corresponds to a target waste receptacle; and selecting an action based on whether the object candidate is acceptable.