Abstract: To load and unload a trailer, an autonomous mobile robot determines its location and the location of objects within the trailer relative to the trailer itself, rather than relative to a warehouse. The autonomous mobile robot determines its location the location of objects within the trailer relative to the trailer. The autonomous mobile robot navigates within the trailer and manipulates objects within the trailer from the trailer's reference frame. Additionally, the autonomous mobile robot uses a centerline heuristic to compute a path for itself within the trailer. A centerline heuristic evaluates nodes within the trailer based on how far away those nodes are from the centerline. If the nodes are further away from the centerline, they are assigned a higher cost. Thus, when the autonomous mobile robot computes a path, the path is more likely to stay near the centerline of the trailer rather than get closer to the sides.

Abstract: Loading last few rows of pallets onto a trailer using an autonomous mobile robot. The robot determines that the next pallet to be loaded is a first in a row where the trailer does not have sufficient space to accommodate the robot. The robot identifies a pose of a previous pallet in an immediately prior row and determines a front plane of the immediately prior row. The robot navigates to a first goal position at least partially inside the trailer, determined by the pose of the previous pallet and the trailer. The robot then side-shifts the fork toward the trailer's side wall until contact is detected and subsequently adjust the fork back to prevent scraping. The robot proceeds to a second goal position, which is within a predetermined threshold distance of the drop position, before lowering and releasing the pallet.

Abstract: Unloading first few rows of pallets from a trailer using an autonomous mobile robot. The robot determines that a pallet is a first in a row where the trailer does not have sufficient space to accommodate the robot. The robot determines a pose of each observable pallet in the trailer and determines a front plane for the pallets in the same row as the target pallet. The robot navigates to a first goal position inside the trailer, based on the pallet and trailer poses, then picks up the pallet. The robot side-shifts toward an adjacent pallet until detecting contact, then adjusts back by a predetermined distance to maximize clearance between the pallet and a side wall of the trailer. The robot navigates backward in a straight line to a second goal position on a ramp, and then proceeds to drop off the pallet in the staging area.

Abstract: Loading a pallet into a trailer using an autonomous mobile robot. The robot determines a pose of the trailer based on sensor data and navigates to a first goal position inside the trailer, determined based on the pose of the trailer. The robot then side-shifts the fork toward the trailer's side wall until sensors detect contact between the pallet and the side wall, with the pallet positioned above a lip on the trailer wall. The robot retracts the fork by a distance corresponding to the lip's width to prevent the pallet and the side wall of the trailer from scraping each other. The robot then navigates in a straight line forward to a second goal position, which is within a predetermined threshold distance from the drop position. The robot releases the pallet at the second goal position.

Abstract: Unloading pallets from a trailer using an autonomous mobile robot. The robot determines a pose of the trailer and a pose of each observable pallet inside. The robot identifies a target pallet for retrieval based on the observed poses and determines a front plane for the pallets in the same row as the target pallet. The robot navigates to a first goal position, side-shifts its fork to align the fork with the target pallet's pockets, inserts the fork, and lifts the pallet. The robot then navigates in reverse to a second goal position, determined based on the front plane and trailer pose. From the second position, the robot proceeds to a drop-off point in a staging area, side-shifting the fork towards the center during transit.

Abstract: To load and unload a trailer, an autonomous mobile robot determines its location and the location of objects within the trailer relative to the trailer itself, rather than relative to a warehouse. The autonomous mobile robot determines its location the location of objects within the trailer relative to the trailer. The autonomous mobile robot navigates within the trailer and manipulates objects within the trailer from the trailer's reference frame. Additionally, the autonomous mobile robot uses a centerline heuristic to compute a path for itself within the trailer. A centerline heuristic evaluates nodes within the trailer based on how far away those nodes are from the centerline. If the nodes are further away from the centerline, they are assigned a higher cost. Thus, when the autonomous mobile robot computes a path, the path is more likely to stay near the centerline of the trailer rather than get closer to the sides.

Abstract: A system and a method are disclosed where an autonomous robot captures an image of an object to be transported from a source to a destination. The robot generates a bounding box within the image surrounding the object. The robot applies a machine-learned model to the image with the bounding box, the machine-learned model configured to identify an object type of the object, and to identify features of the object based on the identified object type and the image. The robot determines which of the identified features of the object are visible to the autonomous robot, and determines a three-dimensional pose of the object based on the features determined to be visible to the autonomous robot.

Abstract: A system and a method are disclosed where an autonomous robot captures an image of an object to be transported from a source to a destination. The robot generates a bounding box within the image surrounding the object. The robot applies a machine-learned model to the image with the bounding box, the machine-learned model configured to identify an object type of the object, and to identify features of the object based on the identified object type and the image. The robot determines which of the identified features of the object are visible to the autonomous robot, and determines a three-dimensional pose of the object based on the features determined to be visible to the autonomous robot.