This paper presents a distributed control architecture to perform part recognition and closed-loop control of a distributed manipulation device. This architecture is based on decentralized cells able to communicate with their four neighbors thanks to peer-to-peer links. Various original algorithms are proposed to reconstruct, recognize and convey the object levitating on a new contactless distributed manipulation device. Experimental results show that each algorithm does a good job for itself and that all the algorithms together succeed in sorting and conveying the objects to their final destination. In the future, this architecture may be used to control MEMS-arrayed manipulation surfaces in order to develop Smart Surfaces, for conveying, fine positioning and sorting of very small parts for micro-systems assembly lines.

The Smart Surface 1 project aims at designing an integrated micro-manipulator based on an array of micromodules connected with a 2D array topology network. Each micromodule comprises a sensor, an actuator and a processing unit. One of the aims of the processing unit is to differentiate the shape of the part that is put on top of the Smart Surface. From a set of shapes this differentiation is done through a distributed algorithm that we call a criterion. The article presents Sensor Network Calibrator (SNC), a calibrator which allows to parametrize the Smart Surface and to determine the necessary number of sensors required by our Smart Surface. The tests will show that SNC is of great importance for choosing the number of sensors, and therefore to determine the size of the sensors grid.

International audienceDistributed parts differentiation in a smart surface is considered. Synchronous and asynchronous distributed discrete state acquisition algorithms are proposed; their convergence is studied and implementation models are given. A distributed part differentiation method is proposed. A multithreaded Java Smart Surface Simulator (SSS) which runs on multi-core machines is presented. A series of computational results obtained with SSS is given and analyzed

If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to present a calibrator to determine the best grid size of a Smart Surface. The Smart Surface is a micro-electro mechanical systems (MEMS) whose goal is to sort micro-parts. Design/methodology/approach -The possible micro-parts are rotated and translated, and their characteristics are stored in a database. Afterwards, when such a micro-part is laid off the Smart Surface, its characteristics are compared to database values. Simulations show that some grid sizes are better than others in terms of success in part recognition. Findings -The tests performed on all groups of three out of four models show that a sensors grid of (35, 35) is an appropriate parameter for the Smart Surface.Research limitations/implications -The authors plan to work on a more general case, using any kinds of parts. Practical implications -The work allows the automation of the process of sorting micro-parts, in assembly lines for example. Originality/value -Few works exist for part recognition on very small parts and for choosing the best discretization scale.

A distributed smart surface based on MEMS technologies is considered. We lay down the mathematical foundations of distributed discrete state acquisition. Distributed state acquisition algorithms and concurrent pattern recognition methods are proposed. A multithreaded Java smart surface simulator which runs on multicore machines is presented. A first series of computational results is displayed and analyzed.