سند علمی

Intelligent Control of Modular Robotic Welding Cell

چکیده

Although robotic machines are routinely used for welding, such machines do not normally incorporate intelligent capabilities. We are studying the general problem of formulating usable levels of intelligence into welding machines. From our perspective, an intelligent machine should: incorporate knowledge of the welding process, know if the process is operating correctly, know if the weld it is making is good or bad, have the ability to learn from its experience to perform welds, and be able to optimize its own performance. To this end, we are researching machine architecture, methods of knowledge representation, decision making and conflict resolution algorithms, methods of learning and optimization, human/machine interfaces, and various sensors. This paper presents work on the machine architecture and the human/machine interface specifically for a robotic, gas metal arc welding cell. Although the machine control problem is normally approached from the perspective of having a central body of control in the machine, we present a design using distributed agents. This new design is loosely based on biological models of social insects. For example, in an ant colony each ant functions according to local rules of behavior [Hölldobler and Wilson, 1990, see chapters 8 and 9]. There is no “king” or “queen”, although the latter name has been given to the reproducing ant. Following a similar approach, we present a modular machine architecture in which each machine element has local rules of behavior but no single element understands how to make a weld. A prime goal of this work is to develop an architecture for an intelligent machine that will support a modular, plug and play standard. A secondary goal of this work is to formulate a human/machine interface that treats the human as an active agent in the modular structure.

اسناد مشابه

  • Qualification of a collaborative human-robot welding cell

    سند علمی۲۰۱۶Dario Antonelli, Sergey Astanin

    This work is focused on evaluating performance of a collaborative robot welding cell developed in our previous research. Such a cell is based on an interactive cooperation between a human supervisor and a welding robot. This approach to organizing a workstation allows to employ r

  • Distributed Learning for Controlling Modular Robots

    سند علمی۲۰۰۴Paulina Varshavskaya, Daniela Rus, Leslie Kaelbling

    What: Recently there has been an important research effort into modular, distributed robotics and in particular, self-reconfiguring robotics [2, 5, 8]. Issues with designing controllers for such systems range from constructing motor control primitives to ensuring cooperation betw

  • Controller Driven VRML Animation of the NIST Robotic Welding Cell

    سند علمی۱۹۹۹Keith Stouffer

    AWMS Automated Welding Manufacturing System project EAI – External Authoring Interface ISO – International Organization for Standardization JPEG – Joint Photographic Experts Group NAMT – National Advanced Manufacturing Testbed NIST – National Institute of Standards and Technology

  • Special Issue on Intelligent Control and Robotics

    سند علمی۲۰۰۷Fuchen Sun, Yuehui Chen, Ajith Abraham

    The momentum of real industrial applications has accelerated the development of fuzzy control, neural-network based control, evolutionary control etc. During the last decade, several computational intelligence techniques haven been also incorporated into the intelligent robotics

  • Applications of Switching Control in Robot Locomotion Workshop on Intelligent Control in Robotics and Automation

    سند علمی۱۹۹۹Mark W. Spong

    In this paper we discuss the application of switching control to problems in intelligent robot control. We will show a connection between research issues in robot locomotion and issues in robot gymnastics, and robot air hockey. These problems are fundamentally related to the cont

  • Intelligent Control for Robot Manipulators by Learning

    سند علمی۲۰۰۴CHAE-WOOK CHUNG, TAE-YONG KUC

    An intelligent control method is proposed for control of rigid robot manipulators which achieves exponential tracking of repetitive robot trajectory under uncertain operating conditions such as parameter uncertainty and unknown deterministic disturbance. In the learning controlle