System for automatically manipulating primary packaging in secondary packaging, comprising a robot having at least one robot arm with a clamping gripper installed at a tool centre point, wherein each tool centre point has a force-torque sensor, an image recording module for recording images of at least the upper segment of the primary packaging, comprising at least two stereo cameras for recording 3-D images, and one or more processors for providing a three-dimensional point cloud, controlling the image recording module and controlling the robot on the basis of the analysis of the three-dimensional point cloud and the measurements from the force-torque sensors.

The present disclosure discloses an autonomous mobile grabbing method for a mechanical arm based on visual-haptic fusion under a complex illumination condition, which mainly includes approaching control over a target position and feedback control over environment information.

According to the method, under the complex illumination condition, weighted fusion is conducted on visible light and depth images of a preselected region, identification and positioning of a target object are completed based on a deep neural network, and a mobile mechanical arm is driven to continuously approach the target object; in addition, the pose of the mechanical arm is adjusted according to contact force information of a sensor module, the external environment and the target object; and meanwhile, visual information and haptic information of the target object are fused, and the optimal grabbing pose and the appropriate grabbing force of the target object are selected.

By adopting the method, the object positioning precision and the grabbing accuracy are improved, the collision damage and instability of the mechanical arm are effectively prevented, and the harmful deformation of the grabbed object is reduced.

Provided is a program generation device capable of automatically generating a route program which takes into account the amount of bending when the tip of a robot abuts against a workpiece. This program generation device is provided with: an acquisition unit that acquires route data indicating a route to be followed by the tip of the robot with respect to an object; a detection unit that detects a pressing force for pressing the tip of the robot to the object; a calculation unit that calculates the amount of misalignment of the followed route caused by bending of the tip of the robot, on the basis of the pressing force detected by the detection unit and a prescribed constant; and a generation unit that automatically generates a route program for controlling a moving route of the tip of the robot, on the basis of the route data acquired by the acquisition unit and the amount of misalignment calculated by the calculation unit.

A system for assembling a vehicle platform includes a robotic assembly system having at least two robotic arms, a vision system capturing images of an assembly frame, and a control system configured to control the robotic assembly system to assemble the vehicle platform based on images from the vision system, force feedback from the at least two robotic arms, and a component location model. The control system is further configured to identify assembly features of a first component and a second component of the vehicle platform from the images, operate the robotic arms to orient the first component and the second component to respective nominal positions based on the images and the component location model, and operate the robotic arms to assemble the first component to the second component based on the force feedback.

Techniques are disclosed to use a robotic arm to palletize or depalletize diverse items. In various embodiments, data associated with a plurality of items to be stacked on or in a destination location is received. A plan to stack the items on or in the destination location is generated based at least in part on the received data. The plan is implemented at least in part by controlling a robotic arm of the robot to pick up the items and stack them on or in the receptacle according to the plan, including by for each item: using one or more first order sensors to move the item to a first approximation of a destination position for that item at the destination location; and using one or more second order sensors to snug the item into a final position.

A system for assembling a vehicle platform includes a robotic assembly system having at least two robotic arms, a vision system capturing images of an assembly frame, and a control system configured to control the robotic assembly system to assemble the vehicle platform based on images from the vision system, force feedback from the at least two robotic arms, and a component location model. The control system is further configured to identify assembly features of a first component and a second component of the vehicle platform from the images, operate the robotic arms to orient the first component and the second component to respective nominal positions based on the images and the component location model, and operate the robotic arms to assemble the first component to the second component based on the force feedback.

Techniques are disclosed to use a robotic arm to palletize or depalletize diverse items. In various embodiments, data associated with a plurality of items to be stacked on or in a destination location is received. A plan to stack the items on or in the destination location is generated based at least in part on the received data. The plan is implemented at least in part by controlling a robotic arm of the robot to pick up the items and stack them on or in the receptacle according to the plan, including by for each item: using one or more first order sensors to move the item to a first approximation of a destination position for that item at the destination location; and using one or more second order sensors to snug the item into a final position.

A docking station is provided that includes at least one component configured to couple to a robot and an identification surface. The identification surface includes a first curvature that varies at a first substantially constant rate of change along a first dimension the identification includes a second curvature that varies at a second substantially constant rate of change along a second dimension. The second dimension is orthogonal to the first dimension. The identification surface includes a third curvature that varies at a third substantially constant rate of change along a third dimension. The third dimension is orthogonal to the first dimension and the second dimension.

An estimation apparatus includes: an acquisition section that acquires a measurement result of a measurement unit that measures an object to be an estimation target of a contact sense in a contactless manner; a determination section that makes a determination as to an aspect of the object or a measurement condition of the object on a basis of the measurement result of the measurement unit; a selection section that selects, on a basis of a result of the determination, an estimation scheme to be used for estimation of the contact sense of the object from among a plurality of estimation schemes; and an estimation section that estimates the contact sense of the object using the selected estimation scheme.

A docking station is provided that includes at least one component configured to couple to a robot and an identification surface. The identification surface includes a first curvature that varies at a first substantially constant rate of change along a first dimension the identification includes a second curvature that varies at a second substantially constant rate of change along a second dimension. The second dimension is orthogonal to the first dimension. The identification surface includes a third curvature that varies at a third substantially constant rate of change along a third dimension. The third dimension is orthogonal to the first dimension and the second dimension.