A method for operating a transport robot includes receiving image data representative of a group of objects. One or more target objects are identified in the group based on the received image data. Addressable vacuum regions are selected based on the identified one or more target objects. The transport robot is command to cause the selected addressable vacuum regions to hold and transport the identified one or more target objects. The transport robot includes a multi-gripper assembly having an array of addressable vacuum regions each configured to independently provide a vacuum. A vision sensor device can capture the image data, which is representative of the target objects adjacent to or held by the multi-gripper assembly.

A method for operating a transport robot includes receiving image data representative of a group of objects. One or more target objects are identified in the group based on the received image data. Addressable vacuum regions are selected based on the identified one or more target objects. The transport robot is command to cause the selected addressable vacuum regions to hold and transport the identified one or more target objects. The transport robot includes a multi-gripper assembly having an array of addressable vacuum regions each configured to independently provide a vacuum. A vision sensor device can capture the image data, which is representative of the target objects adjacent to or held by the multi-gripper assembly.

A grasping robot includes: a grasping mechanism configured to grasp a target object; an image-pickup unit configured to shoot a surrounding environment; an extraction unit configured to extract a graspable part that can be grasped by the grasping mechanism in the surrounding environment by using a learned model that uses an image acquired by the image-pickup unit as an input image; a position detection unit configured to detect a position of the graspable part; a recognition unit configured to recognize a state of the graspable part by referring to a lookup table that associates the position of the graspable part with a movable state thereof; and a grasping control unit configured to control the grasping mechanism so as to displace the graspable part in accordance with the state of the graspable part recognized by the recognition unit.

A grasping robot includes: a grasping mechanism configured to grasp a target object; an image-pickup unit configured to shoot a surrounding environment; an extraction unit configured to extract a graspable part that can be grasped by the grasping mechanism in the surrounding environment by using a learned model that uses an image acquired by the image-pickup unit as an input image; a position detection unit configured to detect a position of the graspable part; a recognition unit configured to recognize a state of the graspable part by referring to a lookup table that associates the position of the graspable part with a movable state thereof; and a grasping control unit configured to control the grasping mechanism so as to displace the graspable part in accordance with the state of the graspable part recognized by the recognition unit.

A mobile robot includes a traveling unit configured to move a main body, a LiDAR sensor configured to acquire geometry information, a camera sensor configured to acquire an image of the outside of the main body, and a controller. The controller generates odometry information based on the geometry information acquired by the LiDAR sensor. The controller determines a current location of the mobile robot by performing feature matching between images acquired by the camera sensor based on the odometry information. The controller combines the information obtained by the camera sensor and the LiDAR sensor to accurately determine the current location of the mobile robot.

A mobile robot includes a traveling unit configured to move a main body, a LiDAR sensor configured to acquire geometry information, a camera sensor configured to acquire an image of the outside of the main body, and a controller. The controller generates odometry information based on the geometry information acquired by the LiDAR sensor. The controller determines a current location of the mobile robot by performing feature matching between images acquired by the camera sensor based on the odometry information. The controller combines the information obtained by the camera sensor and the LiDAR sensor to accurately determine the current location of the mobile robot.

A method includes generating a workspace model having one or more robots and a plurality of image sensors, defining a plurality of pose scenarios of the one or more robots, and defining sensor characteristics of the plurality of image sensors. The method includes, for each of the plurality of pose scenarios, simulating a sensor operation of the plurality of image sensors within the workspace model based on the sensor characteristics and identifying an undetectable area within the workspace model based on the simulated sensor operation. The method includes performing a sensor placement control based on the undetectable areas associated with each of the plurality of pose scenarios.

A method includes generating a workspace model having one or more robots and a plurality of image sensors, defining a plurality of pose scenarios of the one or more robots, and defining sensor characteristics of the plurality of image sensors. The method includes, for each of the plurality of pose scenarios, simulating a sensor operation of the plurality of image sensors within the workspace model based on the sensor characteristics and identifying an undetectable area within the workspace model based on the simulated sensor operation. The method includes performing a sensor placement control based on the undetectable areas associated with each of the plurality of pose scenarios.

Disclosed is a mobile robot capable of communicating with other electronic devices and an external server in a 5G communication environment by executing mounted artificial intelligence (AI) algorithms and/or machine learning algorithms. The mobile robot may include a wheel driver and a controller. By providing the mobile robot, an autonomous driving-based transportation service may be provided.

Disclosed is a mobile robot capable of communicating with other electronic devices and an external server in a 5G communication environment by executing mounted artificial intelligence (AI) algorithms and/or machine learning algorithms. The mobile robot may include a wheel driver and a controller. By providing the mobile robot, an autonomous driving-based transportation service may be provided.