A method for automatic glue-spraying of stringers and inspection of glue-spraying quality based on measured data. Three-dimensional (3D) point cloud data of a stringer-skin assembly is collected by 3D laser scanner, and then processed by denoising and sampling. Feature points of an intersection line of a site to be glued of the stringer-skin assembly are extracted by K-means clustering method based on Gaussian mapping, and a minimum spanning tree is constructed based on a set of the extracted feature points. A connected region is established to obtain an initial feature intersection line of the string-skin assembly, which is optimized by random sample consensus algorithm to remove redundant small branch structures to obtain the actual glue-spraying trajectory. The quality of the glue sprayed on the stringer-skin assembly is inspected by line laser to determine positions of the defects, which are then subjected to secondary glue-spraying.

A method and associated system for providing robotic paint repair includes receiving coordinates of identified defects in a substrate along with characteristics of the defects, and communicating the coordinates to a robot controller module along with additional data needed to control a robot manipulator to bring an end effector of the robot manipulator into close proximity to the identified defect on the substrate. The characteristics of the defect and current state of at least the end effector is provided to a policy server that provides repair actions based on a previously learned control policy that is updated by a machine learning unit. The repair action is executed by communicating instructions for the repair action to the robot controller module and end effector.

An example system includes at least one acoustic sensor and one optical sensor to monitor a thermal spray system controlled by a plurality of control parameters and performing a process associated with a plurality of process outputs. The system includes a computing device including a machine learning module and a control module. The machine learning module is configured to determine, based on at least the plurality of control parameters, an at least one time-dependent acoustic data signal, an at least one image data signal, and the plurality of process outputs, a relationship between the plurality of control parameters and the plurality of process outputs by machine learning. The control module is configured to control the thermal spray system to adjust the plurality of process outputs toward a plurality of respective operating ranges.

Methods and systems for providing nutrients to a plant are provided herein in which a feed solution containing water and solubilized nutrients are delivered to the plant at a desired ratio of concentration of water to fertilizer at each watering event in order to provide the plant with a consistent water to mineral ratio within their vascular system.

A robotic programming apparatus, while using a robot equipped with a spraying device to move the spraying device, for creating an operation program of an application operation for applying a sprayed material sprayed from a nozzle of a spraying device to a member, that includes an operation pattern storage section configured to store a plurality of types of operation patterns each indicating operation of the spraying device that are operation patterns each formed of a continuous trajectory including periodic iteration of a constant pattern, and a pitch interval determination section configured to determine, for one operation pattern of the plurality of types of operation patterns stored in the operation pattern storage section, a pitch interval of the periodic iteration of the constant pattern in the one operation pattern based on a spray parameter representing a spray characteristic of the sprayed material by the nozzle of the spraying device.

This method allows a coating product to be applied to a component (13) being moved by a conveyor (12), along which conveyor at least one spray (62.1, 62.2) is arranged. It comprises automated steps involving determining, within a fixed frame of reference (X12, Y12, Z12), the coordinates of the points (A1, B1, C1, A2, B2, C2) of one or more lines (L1, L2) of the exterior profile of the component which are distributed along the length of the component, in assigning to each spray the points of each exterior profile line that lie within its field of spraying, in identifying, from among the points assigned to each spray, the point (A1, A2; B1, B2) closest to the spray for each exterior profile line, in determining, for each spray, a line (L3, L4) to follow that passes through all the points (A1, A2, B1, B2) closest to the spray as identified in step c), and in establishing a reference path for each spray according to the points on the line (L3, L4) to follow so that the application distance of each spray is adjusted automatically and independently according to the exterior profile of the component.

The present invention relates to an apparatus for spray management. It is described to provide (210) a processing unit with at least one image of a field and provide (220) the processing unit with historical details relating to spray application of a weed control liquid and/or a pest control liquid to the field. The processing unit analyses (230) the at least one image to determine at least one location within the field for activation of at least one weed control spray gun and/or activation of at least one pest control spray gun. The processing unit determines (240) a configuration of the at least one weed control spray gun for application at the at least one location and/or a configuration of the at least one pest control spray gun for application at the at least one location. The determination comprises utilization of the historical details. Information is out-put (250) that is useable to activate the at least one weed control spray gun and/or the at least one pest control spray gun at the at least one location.

A computer-controlled system of placing product on a conveyor belt entrance to an industrial tool is based upon the combination of a 3D vision system (used to capture image data defining the surface area of a next product(s) to be placed) and a processor that is configured to determine an optimum location for placing that next product(s) on the conveyor belt. The processor then instructs a robotic arm to pick up and place the product at the processor-defined optimum location. Depending on the specific task to be performed by the industrial tool, the detailed analysis used to determine the optimum location will differ.

The present invention provides for a wall mountable system for automated drawing of an image upon a wall which includes a horizontal mounting track for mounting on a wall, a robot having a y-track rigidly mounted it where the robot and the mounted y-track travel along the horizontal track. An end effector, which includes a pen holding mechanism holding a pen, is in electrical communication with the robot and travels along the y-track of the robot. The present invention provides a system and device that can attach to a wall or vertical surface in a damage-free manner and draw fast any complexity or style image of custom size in both the horizontal (X) and vertical (Y) directions, that is easy to remove and transport and require small floor space to operate.

Systems and a method predict a generation of a robotic program for industrial coating. Inputs are received including a virtual representation of a robot, a coating gun, elements of the object surface to be coated and a set of desired coating thickness ranges. Inputs on a coating dispersion object are also received. Training data of a plurality of robotic programs for industrial coating and of their corresponding coating thickness coverage on a plurality of surfaces are received. The training data are processed in x, y tuples so as to learn a mapping function to generate a coating prediction module. Starting with a given selected valid thickness coverage as input parameters, it is proceeded in an iterative manner to predict a robotic program via the coating prediction module. A coating robotic program is generated for each surface element based on the resulting predicted coating programs.