Intelligrated Headquarters, LLC

Abhijit Makhal

Dikshya Swain

Matthew R Wicks

Ramdas M Pai

Dominic George Clucas

Sagar Lakhmani

Jagtar Singh

Ayush Jhalani

Darshan Shah

Various embodiments are directed to methods, apparatuses, systems, computing devices, computing entities, and the like for robotically depalletizing objects. In various embodiments, a controller-implemented method for robotic depalletization may comprise detecting a first object of a plurality of palletized objects arranged in a plurality of pallet layers, the first object defining at least a portion of a top pallet layer; identifying an initial pallet height defined by the first object; identifying a second pallet height defined by a second object of the plurality of palletized objects; detecting a pallet height difference based at least in part on the second pallet height; and determining that the top pallet layer has been depalletized based at least in part on a comparison of the pallet height difference to a threshold pallet layer height.

Methods, systems, and computer program products for executing partial depalletization operations in robotic depalletization

A method for controlling a robotic item handler is described. The method includes obtaining first point cloud data related to a first three-dimensional (3D) image and second point cloud data related to a second 3D image, captured by a first sensor device and a second sensor device respectively. Further, the method can include transforming the first point cloud data and the second point cloud data to combined point cloud data that is used as an input to a convolutional neural network, to construct a machine learning model. The machine learning model can output a decision classification indicative of a first probability associated with a first operating mode and a second probability associated with a second operating mode. Furthermore, the method can include operating the robotic item handler according to the first operating mode or the second operating mode based on a comparison of the first probability and the second probability.

Machine learning based decision making for robotic item handling

Methods, apparatuses, systems, and computer program products for an improved anti-sway control system and adjustable end effector for a robotic arm are provided. An example method includes determining at least one of a size, shape or orientation of a package to be picked up by an end effector of a robotic arm and facilitating adjusting a position of a suction cup on the end effector, wherein the position is determined based on the at least one determined size, shape, or orientation of the package. The method further includes facilitating grasping the package with the end effector and facilitating movement of the end effector via a robotic joint to reduce force on the suction cup by the package due to an acceleration of the package due to movement of the robotic arm.

Anti-sway control for a robotic arm with adaptive grasping

Various embodiments described herein relate to techniques for reinforcement learning based conveyoring control. In this regard, a conveyor system is configured to transport one or more objects via a conveyor belt. Furthermore, a vision system comprises one or more sensors configured to scan the one or more objects associated with the conveyor system. A processing device is configured to employ a machine learning model to determine object pose data associated with the one or more objects. The processing device is further configured to generate speed control data for the conveyor belt of the conveyor system based on a set of control policies associated with the object pose data.

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Maryland Heights, MO, United States of America

Abhijit Makhal