An adaptive robot grasp planning technique for bin picking. Workpieces in a bin having random positions and poses are to be grasped by a robot and placed in a goal position and pose. The workpiece shape is analyzed to identify a plurality of robust grasp options, each grasp option having a position and orientation. The workpiece shape is also analyzed to determine a plurality of stable intermediate poses. Each individual workpiece in the bin is evaluated to identity a set of feasible grasps, and the workpiece is moved to the goal pose if such direct movement is possible. If direct movement is not possible, a search problem is formulated, where each stable intermediate pose is a node. The search problem is solved by evaluating the feasibility and optimality of each link between nodes. Feasibility of each link is evaluated in terms of collision avoidance constraints and robot joint motion constraints.

A picking system is configured to pick items from, and put items into, storage containers. The picking system includes a picking station. The picking station includes: a picking system controller configured to receive product orders from a warehouse management system; at least one container contents handling position; a camera configured to produce an image of contents of a storage container; an image processing system in communication with the camera for processing the image produced by the camera in order to identify a position of a specific item in the storage container, and a robotic picking device. The image processing system is further in communication with a picking system controller and is adapted to inform the picking system controller of the position of the specific item. The robotic picking device is in communication with the picking system controller and is configured to, under guidance from the picking system controller, to pick said specific item from said position in the storage container. The camera and the robotic picking device are arranged to operate, at any one instance, on different containers such that the camera is producing an image and the image processing system is processing the produced image of the contents of a storage container in a first product order while the robotic picking device is handling a second storage container on the basis of an earlier image that has been produced by the camera and processed by the image processing system.

A detecting device for detecting liquid or colloid, comprising: a light emitting device, configured to emit first light, wherein a first angle between a first emitting direction of the first light and a surface when the detecting device is located on the surface, wherein the first angle is larger than 0° and smaller than 90°; an optical sensor, configured to detect first optical data generated based on the first light; and a processing circuit, configured to determine if the liquid or the colloid exists in a predetermined range of the detecting device based on the first optical data. An automatic cleaner applying the detecting device is also disclosed.

A grasping device includes: a grasping part module including a first surface and a second surface and configured to grasp an object between the first surface and the second surface; an arm part configured to change a position of the grasping part module; an imaging unit provided at a position that moves together with the grasping part module and configured to capture an image of at least a part of the object; and a control unit configured to control, based on specified amount information indicating a contact state in a case where a specified amount of the object and the first surface are in contact with each other, and information indicating a contact state captured by the imaging unit, at least one of the grasping part module and the arm part such that an amount of the object that is grasped approaches the specified amount.

The present disclosure provides a rotary firing device, furnace and rotary firing method thereof. The rotary firing device is arranged on the roof of the furnace and includes an installation base, an adjusting arm and a tubular burner. The installation base and the adjusting arm are fixed on the roof of the furnace, the middle portion of the tubular burner is rotationally connected to the installation base, and the output end of the tubular burner is located inside the furnace. The output end of the adjusting arm is connected to the middle portion of the tubular burner.

A system for collecting tissue samples, such as meat tissues on carcasses, for example in the food industry. Also provided are methods for collecting tissue samples, and to a non-transitory computer-readable medium comprising program instructions to execute at least one step of the method for collecting tissue samples.

A system for connecting a first ship to a second ship, the system having a plurality of target items coupled to the second ship, a camera module coupled to the first ship and configured to provide information comprising positions of images of the target items in a FOV, and a processor coupled to the camera module and a memory and configured to determine a first position and a first orientation of the second ship relative to the first ship.

A method for vision-based tool localization (VTL) in a robotic assembly system including one or more calibrated cameras, the method comprising capturing a plurality of images of the tool contact area from a plurality of different vantage points, determining an estimated position of the tool contact area based on an image, and refining the estimated position based on another image from another vantage point. The method further comprises providing the refined position to the robotic assembly system to enable accurate control of the tool by the robotic assembly system.

This document describes systems and methods for enhancing the efficiencies of order fulfillment and inventory management processes. For example, this document describes automated robotic systems that can autonomously pick and place a particular quantity of desired items from a container that is storing the items. The autonomous robotic systems can thereby facilitate order fulfillment and inventory management processes in an efficient manner. In particular, the systems and methods described herein can greatly reduce the amount of time required for a human worker to pick orders. Accordingly, the efficiency of item picking processes, as measured by the number of line items picked per human labor hour for example, is greatly enhanced.

Deep machine learning methods and apparatus, some of which are related to determining a grasp outcome prediction for a candidate grasp pose of an end effector of a robot. Some implementations are directed to training and utilization of both a geometry network and a grasp outcome prediction network. The trained geometry network can be utilized to generate, based on two-dimensional or two-and-a-half-dimensional image(s), geometry output(s) that are: geometry-aware, and that represent (e.g., high-dimensionally) three-dimensional features captured by the image(s). In some implementations, the geometry output(s) include at least an encoding that is generated based on a trained encoding neural network trained to generate encodings that represent three-dimensional features (e.g., shape). The trained grasp outcome prediction network can be utilized to generate, based on applying the geometry output(s) and additional data as input(s) to the network, a grasp outcome prediction for a candidate grasp pose.