Virtual Prototyping of a Flexure-based RCC Device for Automated Assembly

The actual use of Industrial Robots (IR) for assembly systems requires the exertion of suitable strategies allowing to overcome shortcomings about IR poor precision and repeatability. In this paper, the practical issues that emerge during common \ue2\u80\u9cpeg-in-hole\ue2\u80\u9d assembly procedures are discussed. In particular, the use of passive Remote Center of Compliance (RCC) devices, capable of compensating the IR non-optimal performance in terms of repeatability, is investigated. The focus of the paper is the design and simulation of a flexure-based RCC that allows the prevention of jamming, due to possible positioning inaccuracies during peg insertion. The proposed RCC architecture comprises a set of flexural hinges, whose behavior is simulated via a CAE tool that provides built-in functions for modelling the motion of compliant members. For given friction coefficients of the contact surfaces, these numerical simulations allow to determine the maximum lateral and angular misalignments effectively manageable by the RCC device

The technology base for agile manufacturing

The effective use of information is a critical problem faced by manufacturing organizations that must respond quickly to market changes. As product runs become shorter, rapid and efficient development of product manufacturing facilities becomes crucial to commercial success. Effective information utilization is a key element to successfully meeting these requirements. This paper reviews opportunities for developing technical solutions to information utilization problems within a manufacturing enterprise and outlines a research agenda for solving these problems

A tactile feedback insertion strategy for peg-in-hole tasks

The Peg-In-Hole (PiH) task performed under un-certain conditions still represents a challenge for autonomous robots. When the peg is not rigidly connected to the robot end-effector, the external forces generated by peg-environment interactions can change the in-hand pose of the peg. This aspect must be taken into account when performing the insertion. This paper deals with this problem and proposes an insertion strategy driven by tactile feedback. In particular, we consider holding the peg using a parallel gripper equipped with tactile sensors, whose measurements are processed to capture in-hand rotations of the peg pose. This information is fed back to the robot controller and used to compensate for changes in the peg orientation and end-point position occurring during the task execution. The approach is validated on a real robot using a two-finger gripper equipped with two capacitive-based tactile sensor arrays hosting 20 tactile elements each. We show that the proposed method achieves an insertion success rate of 38/40 with a 0.1 mm clearance between the peg and hole

Teaching Accommodation Task Skills: from Human Demonstration to Robot Control via Artificial Neural Networks

A simple edge-mating task, performed automatically by accommodation control, was used to study the feasibility of using data collected during a human demonstration to train an artificial neural network (ANN) to control a common robot manipulator to complete similar tasks. The 2-dimensional (planar) edge-mating task which aligns a peg normal to a fiat table served as the basis for the investigation. A simple multi-layered perceptron (MLP) ANN with a single hidden layer and linear output nodes was trained using the back-propagation algorithm with momentum. The inputs to the ANN were the planar components of the contact force between the peg and the table. The outputs from the ANN were the planar components of a commanded velocity. The controller was architected so the ANN could learn a configuration-independent solution by operating in the tool-frame coordinates. As a baseline of performance, a simple accommodation matrix capable of completing the edge- mating task was determined and implemented in simulation and on the PUMA manipulator. The accommodation matrix was also used to synthesize various forms of training data which were used to gain insights into the function and vulnerabilities of the proposed control scheme. Human demonstration data were collected using a gravity-compensated PUMA 562 manipulator and using a custom-built planar, low-impedance motion measurement system (PLIMMS)

A Visual Velocity Impedance Controller

Successful object insertion systems allow the object to translate and rotate to accommodate contact forces. Compliant controllers are used in robotics to provide this accommodation. The impedance compliant controller is one of the more researched and well known compliant controllers used for assembly. The velocity filtered visual impedance controller is introduced as a compliant controller to improve upon the impedance controller. The velocity filtered impedance controller introduces a filter of the velocity impedance and a gain from the stiffness. The velocity impedance controller was found to be more stable over larger ranges of stiffness values than the position based impedance controller. This led to the velocity impedance controller being more accurate and stable with respect to external forces. The velocity impedance controller was also found to have a better compliant response when tested on various insertion geometries in various configurations, including a key insertion acting against gravity. Finally, a novel kinetic friction cone compliance model is introduced for the velocity impedance controller. It was determined that the new compliance model provided a more reliable insertion than the standard insertion model by increasing the error tolerance for failure