A motorized cart retriever, which may be a cart pusher or a cart puller, can apply a force to a nest of human-propelled, wheeled carts to facilitate retrieval of the carts. The cart retriever can include a transceiver configured to wirelessly receive cart status information from cart transmitters of the wheeled carts and wirelessly report event data to a control unit. The cart status information may include an identification of the cart transmitter, a location of the cart, a lock or unlock status of a cart wheel, a misuse condition, etc. The event data can include the cart status information, a number of wheeled carts being retrieved, etc. The cart wheel may include a brake. The transceiver may communicate a message to the cart wheel to keep the brake unactuated during retrieval. The control unit may analyze the event data to detect traffic patterns of the carts.

A shopping cart foot wheel capable of warning the abrasion status of the braking block comprising an escalator wheel assembly; the escalator wheel assembly comprises an escalator wheel core and escalator wheel discs; the escalator wheel discs are located on the two sides of the escalator wheel core, and are coaxially arranged with the escalator wheel core; the escalator wheel assembly further comprises an escalator wheel support, wherein the escalator wheel support is provided with an escalator wheel axle; locking nuts are simultaneously arranged at the two ends of the escalator wheel axle; the escalator wheel core is connected with the escalator wheel axle in a sleeved mode.

A navigation system uses a dead reckoning method to estimate an object's present position relative to one or more prior positions. In some embodiments, the dead reckoning method determines a change in position from the object's heading and speed during an elapsed time interval. In embodiments suitable for use with wheeled objects, the dead reckoning method determines the change in position by measuring the heading and the amount of wheel rotation. Some or all of the components of the navigation system may be disposed within a wheel, such as a wheel of a shopping cart.

A system for monitoring shopping baskets (e.g., baskets on human-propelled carts, motorized carts, or hand-carried baskets) can include a computer vision unit that can image a surveillance region (e.g., an exit to a store), determine whether a basket is empty or loaded with merchandise, and assess a potential for theft of the merchandise. The computer vision unit can include a camera and an image processor programmed to execute a computer vision algorithm to identify shopping baskets and determine a load status of the basket. The computer vision algorithm can comprise a neural network. The system can identify an at least partially loaded shopping basket that is exiting the store, without indicia of having paid for the merchandise, and execute an anti-theft action, e.g., actuating an alarm, notifying store personnel, activating a store surveillance system, activating an anti-theft device associated with the basket (e.g., a locking shopping cart wheel), etc.

A system for monitoring shopping baskets (e.g., baskets on human-propelled carts, motorized carts, or hand-carried baskets) can include a computer vision unit that can image a surveillance region (e.g., an exit to a store), determine whether a basket is empty or loaded with merchandise, and assess a potential for theft of the merchandise. The computer vision unit can include a camera and an image processor programmed to execute a computer vision algorithm to identify shopping baskets and determine a load status of the basket. The computer vision algorithm can comprise a neural network. The system can identify an at least partially loaded shopping basket that is exiting the store, without indicia of having paid for the merchandise, and execute an anti-theft action, e.g., actuating an alarm, notifying store personnel, activating a store surveillance system, activating an anti-theft device associated with the basket (e.g., a locking shopping cart wheel), etc.

Examples of systems and methods for locating movable objects such as carts (e.g., shopping carts) are disclosed. Such systems and methods can use dead reckoning techniques to estimate the current position of the movable object. Various techniques for improving accuracy of position estimates are disclosed, including compensation for various error sources involving the use of magnetometer and accelerometer, and using vibration analysis to derive wheel rotation rates. Also disclosed are various techniques to utilize characteristics of the operating environment in conjunction with or in lieu of dead reckoning techniques, including characteristic of environment such as ground texture, availability of signals from radio frequency (RF) transmitters including precision fix sources. Such systems and methods can be applied in both indoor and outdoor settings and in retail or warehouse settings.

Disclosed is an apparatus, system, and/or method for optimizing usage of a cart within a perimeter, the cart having one or more casters. The apparatus, system and/or method includes one or more beacons positionable within the perimeter and operable for one-way transmission of beacon data. A brake assembly, mountable on the one or more casters of the cart, uses a motion sensor to detect movement of the cart, a brake for arresting the motion of the cart, a scanner for receiving the beacon data transmitted from the one or more beacons; and a brake assembly processor for: (i) automatically applying a beacon algorithm to decode the beacon data; (ii) using the decoded beacon data to identify a beacon sequence as a locking signature condition or an unlocking signature condition and/or determine the location of the cart within the perimeter; and/or (iii) electronically receive and/or transmit the beacon data. The apparatus, system and/or method is operable to optimize the usage of the cart by engaging the brake if the decoded beacon data is identified as the locking signature condition or disengaging the brake if the decoded beacon data is identified as the unlocking signature condition.

According to one embodiment a shopping cart includes an imaging unit configured to acquire images of a surface at a location of the shopping cart and a controller. The controller is configured to detect a boundary pattern in images acquired by the imaging unit. The boundary pattern is painted or drawn on the surface at a boundary between a first area and a second area. Based on the detected boundary pattern the controller determines whether the shopping cart has been moved from the first area to the second area or from the second area to the first area. The controller then controls mobility of the shopping cart based on whether the shopping cart has been moved from the first area to the second area or from the second area to the first area.

Exemplary embodiments are generally directed to shopping carts and associated systems and methods. Exemplary embodiments of the shopping cart include a shopping cart body and a plurality of wheels supporting the shopping cart body. Exemplary embodiments of the shopping cart include a receiver configured to detect a position of the shopping cart relative to a range of a network or an area defined by a geo-fence. Exemplary embodiments of the shopping cart include an electromagnetic generator operatively coupled to at least one of the plurality of wheels. A resistive load can be selectively connected between an input and an output of the electromagnetic generator to restrict rotation of the at least one of the plurality of wheels as the position of the shopping cart detected by the receiver varies between within or outside of the range of the network or the area defined by the geo-fence.

A system for monitoring shopping carts uses cameras to generate images of the carts moving in a store. In some implementations, cameras may additionally or alternatively be mounted to the shopping carts and configured to image cart contents. The system may use the collected image data, and/or other types of sensor data (such as the store location at which an item was added to the basket), to classify items detected in the shopping carts. For example, a trained machine learning model may classify item in a cart as non-merchandise, high theft risk merchandise, electronics merchandise, etc. When a shopping cart approaches a store exit without any indication of an associated payment transaction, the system may use the associated item classification data, optionally in combination with other data such as cart path data, to determine whether to execute an anti-theft action, such as locking a cart wheel or activating a store alarm. The system may also compare the classifications of cart contents to payment transaction records (or summaries thereof) to, e.g., detect underpayment events.