The present disclosure provides methods and systems for autonomous data collection and system control of a material recovery facility (MRF). A control system receives data from set of sensors that reflect a status of the MRF. The control system determines an operating status of various MRF components (e.g., material handling units (MHUs), sensors, and/or other elements) and/or a composition of a waste stream being processed by the MRF. The control system controls MHU(s) based on the data to optimize the recovery and/or purity of recoverable materials from the waste stream. The control system may rearranging MHUs within the MRF and/or re-tasking individual MHUs to perform different handling functions. Machine learning (ML) and/or artificial intelligence (AI) mechanisms can be used to optimize the operation of the MRF in an autonomous fashion. Other aspects are also disclosed.

Methods and apparatuses are provided for use in monitoring product placement within a shopping facility. Some embodiments provide an apparatus configured to determine product placement conditions within a shopping facility, comprising: a transceiver configured to wirelessly receive communications; a product monitoring control circuit coupled with the transceiver; a memory coupled with the control circuit and storing computer instructions that when executed by the control circuit cause the control circuit to: obtain a composite three-dimensional (3D) scan mapping corresponding to at least a select area of the shopping facility and based on a series of 3D scan data; evaluate the 3D scan mapping to identify multiple product depth distances; and identify, from the evaluation of the 3D scan mapping, when one or more of the multiple product depth distances is greater than a predefined depth distance threshold from the reference offset distance of the product support structure.

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.

The present invention discloses an automated segregation unit including one or more feeders, one or more optical decision makers, one or more optical sorters, a plurality of storage units. A plurality of transport means operationally couples the said components. The optical decision maker is integrated with a first vision system configured to categorize one or more materials present in a stream of mixed objects on the basis of one or more identity parameters. The optical sorter configured to physically segregate the categorized one or more materials from the stream of mixed objects. The one or more optical decision makers instructs the one or more optical sorters to eject one or more category of segregated objects to its respective storage unit. A method of operating the automated segregation unit is also disclosed in the present invention.

A separation apparatus, comprising an identifier arranged to identify the particles in a group of particles that have a specific property, an affinity modifier arranged to modify an affinity of the identified particles relative to that affinity of non-identified particles in an group, and a separator arranged to separate the particles in the group based on their difference in the affinity.

A recycling system for recycling reusable objects comprises one or more separating and receiving apparatuses and at least one recyclable object. The separating and receiving apparatus for separating reusable objects and residual waste comprises: a first receiving container for receiving residual waste; a second receiving container for receiving labeled objects; and a sorting device. The sorting device is designed to detect labeled objects, transfer detected objects to the second receiving container, and transfer other objects to the first receiving container.

A trash sorting and recycling method, a trash sorting device and a trash sorting and recycling system are provided. The trash sorting and recycling method includes: acquiring a detection image of trash to be sorted; processing the detection image with a deep learning neural network to judge whether or not the trash to be sorted belongs to recyclable trash; if yes, sending a first control signal, to control to deliver the trash to be sorted into a recycling region; if no, sending a second control signal, to control to deliver the trash to be sorted into a non-recycling region.

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.

The present disclosure relates to an automatic metal sorting system using laser induced breakdown spectroscopy. The system may include: a conveyer configured to move waste metals at a constant speed; a shape measurer configured to measure a position and a shape of at least one waste metal on the conveyer; a laser induced breakdown spectroscopic device configured to determine the kind of the waste metal by emitting a laser to the waste metal and receiving and analyzing a plasma spectrum signal generated by the emitted laser; and a discharger configured to separate and discharge the waste metal in accordance with the determined kind of the waste metal.

Systems, apparatuses, and methods for determining item availability are provided. A computer implemented method for determining item availability in a shopping space comprising: receiving a request for an item for purchase from a customer, querying an inventory database to determine whether the item for purchase is in stock, in an event that the item for purchase is not in stock according to the inventory database: determining an out of stock response to present to the customer, in an event that the item for purchase is in stock according the inventory database: instructing a motorized transport unit to travel to a display space in the shopping space corresponding to the item for the purchase, determining whether the item is available in the display space based on information captured by one or more sensors of the motorized transport unit, and in an event that the item for purchase is not available in the display space: determining an item unavailable response to present to the customer.