“In-hand manipulation” is the ability to reposition an object in the hand, for example when adjusting the grasp of a hammer before hammering a nail. The common approach to in-hand manipulation with robotic hands, known as dexterous manipulation [1], is to hold an object within the fingertips of the hand and wiggle the fingers, or walk them along the object's surface. Dexterous manipulation, however, is just one of the many techniques available to the robot. The robot can also roll the object in the hand by using gravity, or adjust the object's pose by pressing it against a surface, or if fast enough, it can even toss the object in the air and catch it in a different pose. All these techniques have one thing in common: they rely on resources extrinsic to the hand, either gravity, external contacts or dynamic arm motions. We refer to them as “extrinsic dexterity”. In this paper we study extrinsic dexterity in the context of regrasp operations, for example when switching from a power to a precision grasp, and we demonstrate that even simple grippers are capable of ample in-hand manipulation. We develop twelve regrasp actions, all open-loop and hand-scripted, and evaluate their effectiveness with over 1200 trials of regrasps and sequences of regrasps, for three different objects (see video [2]). The long-term goal of this work is to develop a general repertoire of these behaviors, and to understand how such a repertoire might eventually constitute a general-purpose in-hand manipulation capability.

This video presents the application of Extrinsic Dexterity to change the pose of an object in the hand, i.e., to regrasp the object.

The paper aims to develop an automated wheel loading system in the trim-and-final assembly in automotive manufacturing. Currently most of the trim-and-final assemblies are still done manually since the production lines are typically moving randomly. Industrial robots are hardly used to perform any assembly tasks on the randomly changing environments because it is difficult for conventional industrial robots to adjust to any sort of change. Therefore, more intelligent industrial robotic system has to be developed to adopt the random motion of the moving production line for the wheel loading process. This paper presents an intelligent robotics system that performs the wheel loading process while the car is moving randomly with the production line using a synergic combination of visual servoing and force control technology. The developed intelligent robotic technology has been successfully implemented to assembly the wheel onto the car. This practical solution of performing wheel loading on the moving production line, which is not available on the current industrial robot market, can save a lot of money and increase the assembly quality for automotive manufacturing. Since the developed platform is based on the synergic combination of visual servoing and force control technology, it can be used in other areas, such as seam tracking, battery loading and seat loading etc.

This paper describes an industrial robot joint offset calibration method called the virtual line-based single-point constraint approach. Previous methods such as using CMM, laser trackers or cameras are limited by the cost or the resolution. The proposed method relies mainly upon a laser pointer attached on the end-effector and single position-sensitive detector (PSD) arbitrarily located on the workcell. The automated calibration procedure (about three minutes) involves aiming the laser lines loaded by the robot towards the center of the PSD surface from various robot positions and orientations. The intersections of each pair of laser lines eventually should converge to the same point after compensating the joint offsets. An optimization model and algorithm have been formulated to identify the robot offset. For the highly precise feedback, a segmented PSD with a position resolution of better than 0.1 ¿m is employed. The mean accuracy of robot localization is up to 0.02 mm, and the mean error of the parameter identification is less than 0.08 degrees. Both simulations and experiments implemented on an ABB industrial robot verify the feasibility of the proposed method and demonstrated the effectiveness of the developed calibration system. The goal of fast, automated, low-cost, and high precision offset calibration are achieved.

Industrial robots offer a cheaper yet more flexible alternative to the CNC machines in the cleaning and pre-machining applications of automotive aluminum castings. But the low stiffness has limited the application of industrial robots to the machining tasks with very low precision requirement. This paper presents a practical method to compensate the robot deformation caused by the machining force. A constant joint stiffness model based feed forward compensation scheme is implemented in the robot controller. The compensation scheme is shown to be able to reduce the position error by more than 60%. Application test in milling a standard aluminum block has demonstrated the effectiveness of the proposed deformation compensation method. The surface error is reduced from 0.5 mm to 0.1 mm.

The authors first review the concept of time-delay control, then present a decentralized version. The application of such a decentralized controller to robot manipulators is then discussed. Successful experimental results demonstrate the effectiveness of the time-delay concept. The controller functions despite any system parameter uncertainty, and it is not necessary that the parameters be estimated. The practical use is verified.>