Abstract: A high-speed motorized camera-based barcode scanning system for autonomous material handling vehicles (pallet jacks, forklifts, AMRs, AGVs, retail robots) enables considerable cycle time reductions through predictive positioning, continuous scanning during motion, multi-stage machine learning region-of-interest prefiltering, multi-frame fusion, and fleet-wide learning. The camera mounts on a motorized platform with tilt-axis actuation and optional pan control via vehicle rotation. Target detection uses multi-modal perception: LIDAR for extended-distance detection enabling predictive camera pre-positioning using Bayesian spatial histograms, or vision-only operation using panoramic camera semantic segmentation with monocular depth estimation. Cross-camera coordination enables zero-shot positioning. Multi-stage ML cascade with geometric filtering identifies barcode- regions, reducing processing time. Multi-frame fusion improves damaged barcode recovery. Continuous scanning eliminates stop-and-scan cycles.

Abstract: Systems, apparatuses and methods for automated perineal care are provided. A system embodiment includes a user support element, a sensor configured to measure a cleanliness state of a user, a cleaning element configured to clean a user perineal area, and a controller configured to command operation of the cleaning element based on the sensor. An apparatus embodiment includes a containment structure, a sensor, a cleaning element assembly including a drive, actuator and a nozzle, and a controller configured to control the cleaning element assembly based on the sensor. A method embodiment includes positioning a user into a perineal cleaning position, scanning the user with a sensor, determining a cleanliness status, and activating a cleaning element based on the cleanliness status.

Abstract: A volume of contents in a container of a work vehicle can be estimated in various examples. One example involves a system with a 3D sensor on a work vehicle, where the sensor captures images of a material in a container of the work vehicle. A processor device that is in communication with the 3D sensor determines a volume of the material in the container using the images.

Abstract: A volume of contents in a container of a work vehicle can be estimated in various examples. One example involves a system with a 3D sensor on a work vehicle, where the sensor captures images of a material in a container of the work vehicle. A processor device that is in communication with the 3D sensor determines a volume of the material in the container using the images.

Abstract: A volume of contents in a container of a work vehicle can be estimated in various examples. One example involves a system with a 3D sensor on a work vehicle, where the 3D sensor captures images of the contents in a container of the work vehicle. The system also includes a sensor for detecting an angle of the container. A processor device that is in communication with the 3D sensor and the sensor determines a volume of the contents in the container using the images and the angle.

Abstract: A volume of contents in a container of a work vehicle can be estimates. For example, a system can include a 3D sensor on a work vehicle. The 3D sensor can capture multiple images of contents in a container of the work vehicle. The system can also include a sensor for detecting an angle of the 3D sensor with respect to a position of the container for at least some of the images. A processor device can be in communication with the 3D sensor and the sensor. A memory device can include instructions executable by the processor device for causing the processor device to determine a volume of the contents in the container using the images and the angle.

Abstract: This disclosure describes, according to some embodiments, a robot network comprising a plurality of robot units capable of providing navigational guidance to patrons requesting navigational assistance. In an example method, upon receiving a request from a first patron, the method selects an available robot unit from among a plurality of robot units comprising the robot network based on an estimated time to reach the first patron's location. The estimated time to reach the first patron's location is determined based on status updates of other of the robot units comprising the robot network. The method further assigns the available robot unit to the first patron, navigates the assigned robot unit to the first patron's location, engages the first patron using the assigned robot unit, and guides the first patron toward the requested destination using the assigned robot unit.

Abstract: This disclosure describes, according to some embodiments, a robot network comprising a plurality of robot units capable of providing navigational guidance to patrons requesting navigational assistance. In an example method, upon receiving a request from a first patron, the method selects an available robot unit from among a plurality of robot units comprising the robot network based on an estimated time to reach the first patron's location. The estimated time to reach the first patron's location is determined based on status updates of other of the robot units comprising the robot network. The method further assigns the available robot unit to the first patron, navigates the assigned robot unit to the first patron's location, engages the first patron using the assigned robot unit, and guides the first patron toward the requested destination using the assigned robot unit.

Abstract: The disclosure includes a system and method for determining posture data for a user based on observation of the user, generating user model data describing one or more user models, determining user preferences data describing the preferences of the user for receiving an object, generating two or more reach simulations that simulate the user reaching for different points inside a user environment to receive the object, each reach simulation resulting in a hand of the user arriving at a three-dimensional point in the user environment, analyzing the reach simulations to assign a value to each three-dimensional point in the user environment, grouping the three-dimensional points into one or more clusters of similar points to form candidate zones including one or more points inside the user environment where a robot can place the object for handover to the user, and ranking the candidate zones.

Abstract: The disclosure includes a system and method for determining posture data for a user based on observation of the user, generating user model data describing one or more user models, determining user preferences data describing the preferences of the user for receiving an object, generating two or more reach simulations that simulate the user reaching for different points inside a user environment to receive the object, each reach simulation resulting in a hand of the user arriving at a three-dimensional point in the user environment, analyzing the reach simulations to assign a value to each three-dimensional point in the user environment, grouping the three-dimensional points into one or more clusters of similar points to form candidate zones including one or more points inside the user environment where a robot can place the object for handover to the user, and ranking the candidate zones.