Intelligent mobile manipulator navigation using adaptive neuro-fuzzy systems

Authors:
Jean Bosco Mbede;Pierre Ele;Chantal-Marguerite Mveh-Abia;Youssoufi Toure;Volker Graefe;Shugen Ma
Affiliations:
Fac. of Eng., Ibaraki Univ., Hitachi, Ibaraki 316-8511, Japan and Ecole Nationale Supérieure Polytechnique, Université de Yaoundé Cameroun and Laboratoire de Vision et Robotique, Un ...;Ecole Nationale Supérieure Polytechnique, Université de Yaoundé I, BP 8390 Yaounde, Cameroun;Ecole Nationale Supérieure Polytechnique, Université de Yaoundé I, BP 8390 Yaounde, Cameroun;Laboratoire de Vision et Robotique, Université d'Orléans, 18020 Bourges, France;Faculty of Aerospace Engineering, The University of the Armed Forces of Munich, 85577 Neubiberg, Germany;Faculty of Engineering, Ibaraki University, Hitachi, Ibaraki 316-8511, Japan
Venue:
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Intelligent embedded agents
Year:
2005

Citing 5
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Abstract

The work presented in this paper deals with the problem of autonomous and intelligent navigation of mobile manipulator, where the unavailability of a complete mathematical model of robot systems and uncertainties of sensor data make the used of approximate reasoning to the design of autonomous motion control very attractive.A modular fuzzy navigation method in changing and dynamic unstructured environments has been developed. For a manipulator arm, we apply the robust adaptive fuzzy reactive motion planning developed in [J.B. Mbede, X. Huang, M. Wang, Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators, IEEE Transactions on Fuzzy Systems 11 (2) (2003) 249-261]. But for the vehicle platform, we combine the advantages of probabilistic roadmap as global planner and fuzzy reactive based on idea of elastic band. This fuzzy local planner based on a computational efficient processing scheme maintains a permanent flexible path between two nodes in network generated by a probabilistic roadmap approach. In order to consider the compatibility of stabilization, mobilization and manipulation, we add the input of system stability in vehicle fuzzy navigation so that the mobile manipulator can avoid stably unknown and/or dynamic obstacles. The purpose of an integration of robust controller and modified Elman neural network (MENN) is to deal with uncertainties, which can be translated in the output membership functions of fuzzy systems.