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TECHNICAL PAPERS

Real-Time Tip-Over Prevention and Path Following Control for Redundant Nonholonomic Mobile Modular Manipulators via Fuzzy and Neural-Fuzzy Approaches

[+] Author and Article Information
Yangmin Li

Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Av. Padre Tomás Pereira S.J., Taipa, Macao S.A.R., P.R.C.ymli@umac.mo

Yugang Liu

Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Av. Padre Tomás Pereira S.J., Taipa, Macao S.A.R., P.R.C.ya27401@umac.mo

J. Dyn. Sys., Meas., Control 128(4), 753-764 (Dec 05, 2005) (12 pages) doi:10.1115/1.2229253 History: Received January 19, 2005; Revised December 05, 2005

This paper presents a practical method for automatic tip-over prevention and path following control of a redundant nonholonomic mobile modular manipulator. According to modular robot concept, the mobile platform is treated as a special module attached to the base of the modular manipulator, then an integrated structure is constructed and its dynamic modeling is performed. A new tip-over stability criterion based on the supporting forces is proposed in consideration of inertia, gravity, and acceleration. An online fuzzy logic (FL) self-motion planner and an adaptive neural-fuzzy controller (ANFC) are presented: The former is used to generate desired self-motions in a real-time manner, and the latter is used to prevent the robot from tipping over and to control the end-effector to follow a desired spacial trajectory at the same time. The proposed algorithm does not need any a priori knowledge of dynamic parameters and can suppress bounded external disturbances effectively. Simulation results for a real robot validate the dynamic modeling method and the controller design algorithm.

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Copyright © 2006 by American Society of Mechanical Engineers
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Figures

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Figure 1

Motion analysis for the mobile modular manipulator on a slope

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Figure 2

Tip-over stability analysis for a mobile manipulator on a slope

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Figure 3

Fuzzy sets for the input or output variable

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Figure 4

A multiple input single output neural-fuzzy system

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Figure 5

An adaptive neural-fuzzy controller

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Figure 7

Simulation results for case 1

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Figure 8

Simulation results for case 2

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Figure 9

Simulation results for case 3—part 1

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Figure 10

Simulation results for case 3—part 2

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Figure 6

A real robot and external disturbances introduced in case 2

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