Modular Distributed Manipulator System - Robotics Institute Carnegie Mellon University
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Modular Distributed Manipulator System
Contact: Howie Choset

This work will develop algorithms for a novel materials transport and manipulation system which will have applications ranging from flexible manufacturing to package handling. This new system, termed the Modular Distributed Manipulator System (MDMS), comprises an array of actuators each of which is capable of inducing a directed force to an object resting on it. Each cell has its own microprocessor allowing for completely distributed control via a network that allows neighboring cells to communicate.

The MDMS combines the benefits of conveyor and robotic transfer system technologies because it can both transport large heavy objects for long distances and precisely position and orient them. Since sensing and manipulation are distributed, each of many parcels can be manipulated independently, appearing as if each parcel were carried by a separate vehicle.

Current micro-electromechanical distributed manipulation algorithms are insufficient for the MDMS because the latter operates at a macroscopic scale where consideration of mass and friction are critical. Previous MEMS manipulation research has not explicitly dealt with these issues because the approaches were geared towards microscopic applications. The proposed work not only incorporates mass and friction — it exploits them.

Initially, the proposed algorithms will be tested on an existing eighteen cell prototype at Carnegie Mellon. However, this system will not adequately demonstrate the new theory because it does not have ample cells nor the appropriate suspension to effect all motions and manipulations. Furthermore, the computers in each cell are burdened with too much low level control, and thus auxiliary circuitry must be added to free the computer to perform higher-level tasks. A new prototype will be developed to address these drawbacks. Finally, a web-based interface will be developed to demonstrate the proposed algorithms and to enable other researchers to use the MDMS.

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  • William Messner

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  • Mark Bedillion
  • Jon Luntz
  • Daniel M O'Halloran
  • Elie A Shammas