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.

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.

A system for monitoring and controlling shopping cart usage comprises a wheel assembly that attaches to a shopping cart. The wheel assembly includes a wheel, a brake that can be activated to inhibit rotation of the wheel, a controller that controls the brake, a VLF receiver, and an RF transceiver. The RF transceiver may, for example, operate in a 2.4 GHz frequency band. In some implementations, the RF transceiver may be used to detect entry of the shopping cart into a checkout area of the store, and the VLF receiver may be used to detect that the shopping cart is exiting the store. The controller may activate the brake if the shopping cart attempts to exit the store without first passing through a checkout area.

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 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.

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.

An apparatus includes a support frame structure for supporting a walking aid. A seating device supports a toddler in a sitting or standing position. A tether implement secures the seating device to the support frame structure, wherein the frame members stabilize the apparatus on a supporting surface. A plurality of wheels disposed at a base of each of the plurality of frame members provides mobility on the supporting surface. L-shaped extensions are configured to extend each of the plurality of wheels further out than the support frame structure. A braking mechanism disposed on an underside of each L-shaped extension is configured to generally stop a movement of the apparatus or a rotation of the wheel when the wheel is no longer supported by the supporting surface. A handle implement disposed on the support frame structure is configured to be operable for steering the apparatus.

The automated robotic shopping cart is a shopping cart with a controller and locomotion subsystem. The controller may provide shopping assistance to the shopper and may play audio/visual programming to entertain a child riding in the cart. The controller may wirelessly pair with a phone carried by the shopper and may use battery powered motors to propel and steer the cart such that the cart follows the shopper through a store. The battery may be recharged from a solar panel when the cart is outdoors. A GPS receiver in the controller may determine the physical location of the cart and may activate a brake if an attempt is made to remove the cart from the retail property. The invention may further comprise a reading light to illuminate products and a distress button to request assistance.

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.

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.