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A globally converging algorithm for adaptive manipulation and trajectory following for mobile robots with serial redundant arms

Published online by Cambridge University Press:  07 June 2013

Paul Moubarak
Robotics and Mechatronics Laboratory, Department of Mechanical and Aerospace Engineering, The George Washington University, Washington, DC 20052, USA
Pinhas Ben-Tzvi*
Robotics and Mechatronics Laboratory, Department of Mechanical and Aerospace Engineering, The George Washington University, Washington, DC 20052, USA
*Corresponding author. E-mail:


In this paper the tip-over stability of mobile robots during manipulation with redundant arms is investigated in real-time. A new fast-converging algorithm, called the Circles Of INitialization (COIN), is proposed to calculate globally optimal postures of redundant serial manipulators. The algorithm is capable of trajectory following, redundancy resolution, and tip-over prevention for mobile robots during eccentric manipulation tasks. The proposed algorithm employs a priori training data generated from an exhaustive resolution of the arm's redundancy along a single direction in the manipulator's workspace. This data is shown to provide educated initial guess that enables COIN to swiftly converge to the global optimum for any other task in the workspace. Simulations demonstrate the capabilities of COIN, and further highlight its convergence speed relative to existing global search algorithms.

Copyright © Cambridge University Press 2013 

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