An estimation device includes a memory and at least one processor. The at least one processor is configured to acquire information regarding a target object. The at least one processor is configured to estimate information regarding a location and a posture of a gripper relating to where the gripper is able to grasp the target object. The estimation is based on an output of a neural model having as an input the information regarding the target object. The estimated information regarding the posture includes information capable of expressing a rotation angle around a plurality of axes.

A functional safety system performs safety analysis on three-dimensional point cloud data measured by a time-of-flight (TOF) sensor that monitors a hazardous industrial area that includes an automation system. To reduce the amount of point cloud data to be analyzed for hazardous conditions, the safety system executes a real-time emulation of the automation system using a digital twin and live controller data read from an industrial controller that monitors and controls the automation system. The safety system generates simulated, or shadow, point cloud data based on the emulation and subtracts this simulate point cloud data from the measured point cloud data received from the TOF sensor. This removes portions of the point cloud data corresponding to known or expected elements within the monitored area. Any remaining entities detected in the reduced point cloud data can be further analyzed for safety concerns.

Various embodiments described herein relate to techniques for object height detection for palletizing operations and/or depalletizing operations. In this regard, an automated industrial system comprises at least a column portion, a robot arm portion, and an end effector configured to grasp an object. An image-capturing device is mounted onto the automated industrial system and is configured to rotate, based on movement of the robot arm portion, to scan the object grasped by the end effector and to generate image-capturing data associated with the object. Furthermore, a processing device is configured to determine height data for the object based on the image-capturing data. The processing device is also configured to determine location data for the object with respect to a conveyor system based on the height data.

Systems, devices, articles, and methods, described in greater detail herein, including robotic systems which include at least one rangefinder, at least one manipulator, and at least one processor in communication with the at least one rangefinder, and methods of operation of the same. The at least one processor obtains rangefinder pose information which represents, at least, the at least one manipulator in a plurality of poses. The at least one processor obtains manipulator pose information, optimizes a model of mismatch between the rangefinder pose information and the manipulator pose information, wherein the model of mismatch includes a plurality of parameters, and updates at least one processor readable storage device with the plurality of parameters based at least in part on the optimization.

According to one embodiment, a cargo handling apparatus includes a hand, a robot arm, a transfer device, a measurement device, and a control device. The hand holds an article. The robot arm moves the hand. The transfer device is arranged with the robot arm, and transfers the article. The measurement device measures a position and a size of the article. The control device performs a first operation of transferring the article to the transfer device by using the hand and the robot arm, and a second operation of transferring the transferred article by using the transfer device. The control device determines whether or not the robot arm will interfere with the transfer device or a second article on the transfer device when performing the first operation for a first article. The control device controls a start timing of the first operation according to a determination result of the interference.

A conveyance robot system according to the present disclosure includes a conveyance robot, and a robot control unit configured to control an operation of picking up an object performed by the conveyance robot, wherein the robot control unit determines that a movable range area, which is an area outside a safety cover where a robot arm is operated, satisfies a safety ensuring condition that can regard safety of the movable range area as equivalent to the safety inside the safety cover and allow the robot arm to perform a work while projecting toward the shelf.

Methods and apparatuses for detecting one or more objects (e.g., dropped objects) by a robotic device are described. The method comprises receiving a distance-based point cloud including a plurality of points in three dimensions, filtering the distance-based point cloud to remove points from the plurality of points based on at least one known surface in an environment of the robotic device to produce a filtered distance-based point cloud, clustering points in the filtered distance-based point cloud to produce a set of point clusters, and detecting one or more objects based, at least in part, on the set of point clusters.

A functional safety system performs safety analysis on three-dimensional point cloud data measured by a time-of-flight (TOF) sensor that monitors a hazardous industrial area that includes an automation system. To reduce the amount of point cloud data to be analyzed for hazardous conditions, the safety system executes a real-time emulation of the automation system using a digital twin and live controller data read from an industrial controller that monitors and controls the automation system. The safety system generates simulated, or shadow, point cloud data based on the emulation and subtracts this simulate point cloud data from the measured point cloud data received from the TOF sensor. This removes portions of the point cloud data corresponding to known or expected elements within the monitored area. Any remaining entities detected in the reduced point cloud data can be further analyzed for safety concerns.

An imaging apparatus includes a structural support rigidly coupled to a surface of a mobile robot and a plurality of perception modules, each of which is arranged on the structural support, has a different field of view, and includes a two-dimensional (2D) camera configured to capture a color image of an environment, a depth sensor configured to capture depth information of one or more objects in the environment, and at least one light source configured to provide illumination to the environment. The imaging apparatus further includes control circuitry configured to control a timing of operation of the 2D camera, the depth sensor, and the at least one light source included in each of the plurality of perception modules, and at least one computer processor configured to process the color image and the depth information to identify at least one characteristic of one or more objects in the environment.

The present disclosure relates to robot path planning. Depth information of a plurality of obstacles in an environment of a robot are obtained at a first time instance. A static distance map is generated based on the depth information. A path is computed for the robot based on the static distance map. At a second time instant, depth information of one or more obstacles is obtained. A dynamic distance map is generated based on the one or more obstacles, wherein for each obstacle that satisfies a condition: a vibration range of the obstacle is computed based on a position of the obstacle and the static distance map, and the obstacle is classified as a dynamic obstacle or a static obstacle based on a criterion associated with the vibration range. A repulsive speed of the robot is computed based on the dynamic distance map to avoid the dynamic obstacles.