Crown Equipment Corporation

Justin Forbes Thode

Grace Da-In Lee

Ryan Michaels Estep

Timothy William Fanselow

A materials handling vehicle includes a camera, odometry module, processor, and drive mechanism. The camera captures images of an identifier for a racking system aisle and a rack leg portion in the aisle. The processor uses the identifier to generate information indicative of an initial rack leg position and rack leg spacing in the aisle, generate an initial vehicle position using the initial rack leg position, generate a vehicle odometry-based position using odometry data and the initial vehicle position, detect a subsequent rack leg using a captured image, correlate the detected subsequent rack leg with an expected vehicle position using rack leg spacing, generate an odometry error signal based on a difference between the positions, and update the vehicle odometry-based position using the odometry error signal and/or generated mast sway compensation to use for end of aisle protection and/or in/out of aisle localization.

Systems and methods for out of aisle localization and vehicle position calibration using rack leg identification

Systems and methods for vehicle position calibration using rack leg identification and mast sway compensation

A materials handling vehicle comprising a path validation tool and a drive unit, steering unit, localization module, and navigation module that cooperate to navigate the vehicle along a warehouse travel path. The tool comprises warehouse layout data, a proposed travel path, vehicle kinematics, and a dynamic vehicle boundary that approximates the vehicle physical periphery. The tool executes path validation logic to determine vehicle pose along the proposed travel path, update the dynamic vehicle boundary to account for changes in vehicle speed and steering angle, determine whether the dynamic vehicle boundary is likely to intersect obstacles represented in the layout data based on the determined vehicle pose at candidate positions along the proposed travel path, determine a degree of potential impingement at the candidate positions by referring to the dynamic vehicle boundary and obstacle data, and modify the proposed travel path to mitigate the degree of potential impingement.

Materials handling vehicle path validation and dynamic path modification

Systems and methods for end of aisle protection and vehicle position calibration using rack leg identification

A materials handling vehicle includes a camera, an odometry module to generate odometry data, a processor, and a drive mechanism. The camera captures images of an identifier for a racking system aisle and at least a rack leg portion positioned in the aisle. The processor uses the identifier to generate information indicative of an initial rack leg position and rack leg spacing in the aisle, generate an initial vehicle position using the initial rack leg position, generate a vehicle odometry-based position in the aisle using odometry data and the initial vehicle position, detect a subsequent rack leg using a captured image, correlate the detected subsequent rack leg with an expected vehicle position using rack leg spacing, generate an odometry error signal based on a difference between the expected vehicle position and the vehicle odometry-based position, and update the vehicle odometry-based position using the odometry error signal.

Systems and methods for vehicle position calibration using rack leg identification

Systems and methods for calibrating odometry of a materials handling vehicle. One embodiment of a method includes determining a current location of the materials handling vehicle, determining an odometry distance from the current location to a destination based on a calculation of a determined number of rotations of a wheel and a circumference of the wheel, and determining a positioning system distance from the current location to the destination. Some embodiments include comparing the odometry distance with data from the positioning system distance to calculate a scaling factor, applying the scaling factor to a fast alpha filter to achieve a fast filter result, and applying the scaling factor to a slow alpha filter to achieve a slow filter result. Similarly, some embodiments include applying the fast alpha filter to the scaling factor to smooth noise, calculating an updated odometry distance utilizing the scaling factor, and utilizing the updated odometry distance.

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Auckland, New Zealand

Justin Forbes Thode