FIELD OF THE DISCLOSURE

The present disclosure relates to automated manufacturing processes and, more specifically, to a system and method for grasping objects.

BACKGROUND OF THE DISCLOSURE

Certain manufacturing processes involve object picking, for example, from a pallet, an endless conveyor, or the like, for sorting and/or processing of the object. The picking itself may be carried out by a picker such as a magnetic or pneumatic device, a gripper, or similar structures. When a picking process is performed, care is taken to effect an efficient grip of the object so as to achieve a predicable object position and orientation, and also to avoid damage to the object.

Various systems and methods have been used in the past to improve the picking process for an object. For example, one previously proposed system includes using a data-driven methodologies, such as deep learning, to improve grasping performance. In such a system, an offline data collection or calibration session is used to determine optimal grasping locations on the object, which will provide a stable grasp without causing damage or deformation to the object being picked. However, there are at least three problems with such a methodology: (a) since each object can be grasped in multiple ways, manual labeling of grasp locations is exhaustive and time-consuming, and therefore, is not a trivial task; (b) human labeling is biased by semantics; and (c) the calibration or labelling achieved for a particular object is not scalable to different type of objects.

While there have been attempts to generate the grasp labels automatically by conducting trial-and-error experiments, either from real robot grasping or from a simulated environment, existing systems and methodology only use sensors to measure success/failure, which makes the learner prone to over-fitting and providing weak supervision at unstable grasping positions. Thus, while a pre-calibrated object grasping strategy may provide a marginal improvement over an open-ended picking strategy, it requires considerable work before it can be implemented and lacks flexibility to application on many different object shapes and sizes.

BRIEF SUMMARY OF THE DISCLOSURE

In one aspect, the present disclosure describes an object grasp system. The system includes a robotic arm assembly having a distal end, which is selectively moveable by the robotic arm with respect to a fixed frame. A gripper is disposed on the distal end and adapted to grasp an object located in an area. A fixed sensor is disposed to monitor the area and is positioned in a stationary fashion relative to the fixed frame. A first onboard sensor is disposed to move with the gripper on the distal end of the robotic arm, the first onboard sensor being disposed to monitor the area. A controller is operably associated with the robotic arm assembly and the gripper, and operates to control the operation of the robotic arm assembly and the gripper. The controller is also operably associated with the fixed sensor and the first onboard sensor, and is disposed to receive information therefrom indicative of a position of the object and the gripper with respect to the fixed frame, and also a position of the object relative to the gripper. The controller is programmed to operate the robotic arm assembly to bring the gripper into a grasp position adjacent the object based on information provided by the fixed sensor. The controller is also programmed to operate the gripper to grasp the object in response to information provided by the first onboard sensor.

In another aspect, the disclosure describes a method for grasping an object with a gripper disposed on a distal end of a robotic arm assembly. The method includes determining a spatial position of an object relative to a fixed frame of reference using a fixed sensor, determining a position of a gripper attached to a distal end of a robotic arm relative to the object using the fixed sensor, activating the robotic arm to move the gripper into a grasping position relative to a first object, grasping the first object with the gripper, evaluating a quality of the grasp using an onboard sensor associated with the robotic arm, and adjusting the grasping position relative to a second object based on the evaluation of the quality of the grasp of the first object. Adjusting the grasping location is performed automatically by a controller based on information provided by the fixed sensor and the onboard sensor.

In yet another aspect, the disclosure describes a robotic arm having a gripper disposed on a distal end thereof, the robotic arm operating to move the gripper relative to a fixed frame of reference, the gripper adapted to grasp an object disposed in a grasping area that is stationary relative to the fixed frame of reference. The robotic arm further includes a fixed camera disposed to monitor the area, the fixed camera disposed stationary relative to the fixed frame of reference, the fixed camera providing visual information indicative of a position of the object relative to the area. An onboard camera is disposed to move with the gripper on the distal end of the robotic arm. The onboard camera is disposed to monitor the area and provide visual information indicative of a position of the object relative to the gripper. A force/torque sensor is disposed on the gripper between the gripper and the distal end of the robotic arm assembly.

A controller is operably associated with the robotic arm assembly and the gripper. The controller operates to control an operation of the robotic arm and the gripper. The controller is also operably associated with the fixed camera, the onboard camera, and the force/torque sensor, and disposed to receive information therefrom. The information is indicative of a position of the object and the gripper with respect to the fixed frame from the fixed camera, a position of the object relative to the gripper from the onboard camera, and one or more forces and one or more moments present at the distal end by the gripper while the object is grasped by the gripper from the force/torque sensor.

In one embodiment, the controller is programmed to operate the robotic arm assembly to bring the gripper into a grasp position adjacent the object based on information provided by the fixed camera, operate the gripper to grasp the object in response to information provided by the onboard camera, automatically select a grasp location on the object, which is engaged by the gripper, based on the information provided to the controller by the force/torque sensor, and estimate a location of a center of gravity of the object based on the information provided to the controller by the force/torque sensor.

