Disclosed are various embodiments of a three-dimensional perception and object manipulation robot gripper configured for connection to and operation in conjunction with a robot arm. In some embodiments, the gripper comprises a palm, a plurality of motors or actuators operably connected to the palm, a mechanical manipulation system operably connected to the palm, a plurality of fingers operably connected to the motors or actuators and configured to manipulate one or more objects located within a workspace or target volume that can be accessed by the fingers. A depth camera system is also operably connected to the palm. One or more computing devices are operably connected to the depth camera and are configured and programmed to process images provided by the depth camera system to determine the location and orientation of the one or more objects within a workspace, and in accordance therewith, provide as outputs therefrom control signals or instructions configured to be employed by the motors or actuators to control movement and operation of the plurality of fingers so as to permit the fingers to manipulate the one or more objects located within the workspace or target volume. The gripper can also be configured to vary controllably at least one of a force, a torque, a stiffness, and a compliance applied by one or more of the plurality of fingers to the one or more objects.

An actuator and end effector are controlled according to images from cameras having a surface in their field of view. Vessels (cups, bowls, etc.) and other objects are identified in the images and their configuration is assigned to a finite set of categories by a classifier that does not output a 3D bounding box or determine a 6D pose. For objects assigned to a first subset of categories, grasping parameters for controlling the actuator and end effector are determined using only 2D bounding boxes, such as oriented 2D bounding boxes. For objects not assigned to the first subset, a righting operation may be performed using only 2D bounding boxes. Objects that are still not in the first set may then be grasped by estimating a 3D bounding box and 6D pose.

One variation of a method for monitoring growth of plants within a facility includes: aggregating global ambient data recorded by a suite of fixed sensors, arranged proximal a grow area within the facility, at a first frequency during a grow period; extracting interim outcomes of a set of plants, occupying a module in the grow area, from module-level images recorded by a mover at a second frequency less than the first frequency while interfacing with the module during the period of time; dispatching the mover to autonomously deliver the module to a transfer station; extracting interim outcomes of the set of plants from plant-level images recorded by the transfer station while sequentially transferring plants out of the module at the conclusion of the grow period; and deriving relationships between ambient conditions, interim outcomes, and final outcomes from a corpus of plant records associated with plants grown in the facility.

One variation of method for monitoring growth of plants within a facility includes: aggregating global ambient data recorded by a suite of fixed sensors, arranged proximal a grow area within the facility, at a first frequency during a grow period; extracting interim outcomes of a set of plants, occupying a module in the grow area, from module-level images recorded by a mover at a second frequency less than the first frequency while interfacing with the module during the period of time; dispatching the mover to autonomously deliver the module to a transfer station; extracting interim outcomes of the set of plants from plant-level images recorded by the transfer station while sequentially transferring plants out of the module at the conclusion of the grow period; and deriving relationships between ambient conditions, interim outcomes, and final outcomes from a corpus of plant records associated with plants grown in the facility.

An actuator and end effector are controlled according to images from cameras having a surface in their field of view. Vessels (cups, bowls, etc.) and other objects are identified in the images and their configuration is assigned to a finite set of categories by a classifier that does not output a 3D bounding box or determine a 6D pose. For objects assigned to a first subset of categories, grasping parameters for controlling the actuator and end effector are determined using only 2D bounding boxes, such as oriented 2D bounding boxes. For objects not assigned to the first subset, a righting operation may be performed using only 2D bounding boxes. Objects that are still not in the first set may then be grasped by estimating a 3D bounding box and 6D pose.

Disclosed are various embodiments of a three-dimensional perception and object manipulation robot gripper configured for connection to and operation in conjunction with a robot arm. In some embodiments, the gripper comprises a palm, a plurality of motors or actuators operably connected to the palm, a mechanical manipulation system operably connected to the palm, a plurality of fingers operably connected to the motors or actuators and configured to manipulate one or more objects located within a workspace or target volume that can be accessed by the fingers. A depth camera system is also operably connected to the palm. One or more computing devices are operably connected to the depth camera and are configured and programmed to process images provided by the depth camera system to determine the location and orientation of the one or more objects within a workspace, and in accordance therewith, provide as outputs therefrom control signals or instructions configured to be employed by the motors or actuators to control movement and operation of the plurality of fingers so as to permit the fingers to manipulate the one or more objects located within the workspace or target volume. The gripper can also be configured to vary controllably at least one of a force, a torque, a stiffness, and a compliance applied by one or more of the plurality of fingers to the one or more objects.

The present invention discloses robotic process automation (RPA) of supply chain management (SCM) operations. In example embodiments the invention discloses RPA method and systems for supply chain management. The RPA systems include IOT devices and server as part of a sub network for secured communication relating to SCM operations like procurement operations. The system and method of the present invention utilizes AI based processing logic using a bot for automation of SCM operations thereby reducing the processing times and increasing efficiency.

A control device includes a first statistical processing unit a second statistical processing unit and a movement control unit. The first statistical processing unit acquires relative positions of the object calculated by the visual sensor and performs statistical processing on the acquired relative positions of the object. The second statistical processing unit acquires from the position sensor relative positions of the holding device corresponding to each of the relative positions of the object calculated by the visual sensor, and performs statistical processing on the acquired relative positions of the holding device. The movement control unit performs feedback control of the moving device based on the relative positions of the object and the relative positions of the holding, device and performs alignment of the object with the target position while moving the object closer to the target position.

The present invention discloses robotic process automation (RPA) of supply chain management (SCM) operations. In example embodiments the invention discloses RPA method and systems for supply chain management. The RPA systems include IOT devices and server as part of a sub network for secured communication relating to SCM operations like procurement operations. The system and method of the present invention utilizes AI based processing logic using a bot for automation of SCM operations thereby reducing the processing times and increasing efficiency.

One variation of method for monitoring growth of plants within a facility includes: aggregating global ambient data recorded by a suite of fixed sensors, arranged proximal a grow area within the facility, at a first frequency during a grow period; extracting interim outcomes of a set of plants, occupying a module in the grow area, from module-level images recorded by a mover at a second frequency less than the first frequency while interfacing with the module during the period of time; dispatching the mover to autonomously deliver the module to a transfer station; extracting interim outcomes of the set of plants from plant-level images recorded by the transfer station while sequentially transferring plants out of the module at the conclusion of the grow period; and deriving relationships between ambient conditions, interim outcomes, and final outcomes from a corpus of plant records associated with plants grown in the facility.