An automated food waste processing system including an enclosure secured to prevent unauthorized access to contents contained therein, the enclosure including a plurality of exterior walls and a food waste processing system housed within the enclosure. The food waste processing system including an imaging system configured to capture a plurality of images of the food waste and the non-biodegradable material received by the sorting receptacle, a processing system configured to process the plurality of images using a trained neural network to identify at least plastic waste and metal waste as the non-biodegradable material when included in the food waste input stream as received by the sorting receptacle, and a sorting system configured to, in response to instructions received from the processing system, automatically locate and remove the non-biodegradable material from the sorting receptacle to create a bio-degradable input stream to the anaerobic digester.

An apparatus that facilitates the manual sorting of objects is disclosed. The apparatus includes a display surface having a surface that can be selectively illuminated and that is adapted for receiving the objects. An identification reader reads identification information stored on the objects. A controller causes an area on the display surface corresponding to one of the objects to be illuminated based on the identification information and indicates a location to which the illuminated object is to be moved. In one aspect of the invention, the display surface includes an area adapted for positioning a receiver for the objects. The display surface provides an indication of a position in the receiver at which the one of the objects is to be placed.

Feedback information is an important aspect in all machine learning systems. In robot systems that are picking objects from a plurality of objects this has been arranged by acquiring images of objects that have been picked. When images are acquired after picking they can be imaged accurately and the information about picking and also dropping success can be improved by using acquired images as a feedback in the machine learning arrangement being used for controlling the picking and dropping of objects.

Apparatuses, components and methods are provided herein useful to provide assistance to customers and/or workers in a shopping facility. In some embodiments, a shopping facility personal assistance system comprises: a plurality of motorized transport units located in and configured to move through a shopping facility space; a plurality of user interface units, each corresponding to a respective motorized transport unit during use of the respective motorized transport unit; and a central computer system having a network interface such that the central computer system wirelessly communicates with one or both of the plurality of motorized transport units and the plurality of user interface units, wherein the central computer system is configured to control movement of the plurality of motorized transport units through the shopping facility space based at least on inputs from the plurality of user interface units.

Systems and methods for sorting recyclable items and other materials are provided. In one embodiment, a system for sorting objects comprises: at least one imaging sensor; a controller comprising a processor and memory storage, wherein the controller receives image data captured by the image sensor; and at least one pusher device coupled to the controller, wherein the at least one pusher device is configured to receive an actuation signal from the controller. The processor is configured to detect objects travelling on a conveyor device and recognize at least one target item traveling on a conveyor device by processing the image data and to determine an expected time when the at least one target item will be located within a diversion path of the pusher device. The controller selectively generates the actuation signal based on whether a sensed object detected in the image data comprise the at least one target item.

A multi-task device includes at least: a rivet support module able to contain a rivet and/or a temporary fastener support module able to contain a temporary fastener; a device for introducing a rivet or temporary fastener into the modules at at least one loading station; and a device for moving the at least one module to a work station for setting a rivet or temporary fastener in a hole provided in a structure to be worked on. The multi-task device includes a device for checking compliance of the rivet or temporary fastener depending on at least one predetermined compliance criterion, and a device for discharging, to a disposal zone, a rivet or temporary fastener identified as non-compliant by the device for checking.

Provided is a fruit quality inspecting and sorting appliance. The fruit quality inspecting and sorting appliance includes: a conveying module; a weighing module, which cooperates with the conveying module to convey weighed fruits through the conveying module; an internal quality inspection module, which cooperates with the conveying module and performs an internal quality inspection to the weighed fruits; an external quality inspection module, which cooperates with the conveying module and performs an external quality inspection to the fruits after the internal quality inspection; a sorting module, which cooperates with the conveying module, and sorts the fruits passing through the weighing module, the internal quality inspection module and the external quality inspection module; and a control module electrically connected with the conveying module, the weighing module, the internal quality inspection module, the external quality inspection module and the sorting module.

Provided are a data collection method, a data collection system and a computer program for automatically collecting data in stages in classification. A data collection method is implemented by one or more processors connected to a sorting machine sorting articles to different sorting destinations. The method comprising: acquiring, by a processor, an identification code of an article from a reader provided on the sorting machine for identifying the article; acquiring, by a processor, image data of the article identified by the identification code from a camera that is attached so as to image the article after being read by the reader; storing, by a processor, the acquired image data so as to be associated with the acquired identification code.

Provided are a scrap determination system and method that can improve scrap determination techniques. A scrap determination system (1) comprises: a first scrap determination model (221) generated using teaching data including first learning images, and determines, based on a camera image, grade of scrap in the image and a ratio of the grade; a second scrap determination model (222) generated using teaching data including second learning images, and determines, based on the image, grade of scrap in the image and a ratio of the grade; a selection model (223) configured to determine which of the first scrap determination model (221) and the second scrap determination model (222) is to be used, based on the image; and an output section (24) configured to output information of each grade of scrap and a ratio of the grade determined based on the image using the model selected by the selection model (223).

A singulation conveyor for conveying and separating piece goods, in particular packages and/or consignments, along at least one transport track bearing the piece goods items is described, wherein in the transport track, in some sections, finger elements adjustable between a lower setting and an upper setting are provided, and wherein the free ends of the finger elements in the upper setting are arranged in an upper position above an adjoining region of the transport track in such a way as to manipulate the motional direction and/or the motional speed of respectively individual piece goods items independently of other piece goods items.