Obstacle avoidance for redundant nonholonomic mobile modular manipulators via neural fuzzy approaches

Yangmin Li, Yugang Liu

Research output: Journal article publicationConference articleAcademic researchpeer-review

2 Citations (Scopus)


This paper addresses an obstacle avoidance issue for redundant nonholonomic mobile modular manipulators. On the basis of modular robot concept, an integrated dynamic modeling method is proposed, which takes both the mobile platform and the onboard modular manipulator as an integrated structure. A new obstacle avoidance algorithm is proposed which is mainly composed of two parts: a self-motion planner (SMP) and a robust adaptive neural fuzzy controller (RANFC). One important feature of this algorithm lies in that obstacles are avoided by online adjusting self-motions so that the end-effector task will not be affected unless the obstacles are just on the desired trajectory. The RANFC does not rely on exact aprior dynamic parameters and can suppress bounded external disturbance effectively. The effectiveness of the proposed algorithm is verified by simulations.
Original languageEnglish
Pages (from-to)1109-1118
Number of pages10
JournalLecture Notes in Computer Science
Issue numberPART III
Publication statusPublished - 24 Oct 2005
Externally publishedYes
EventFirst International Conference on Natural Computation, ICNC 2005 - Changsha, China
Duration: 27 Aug 200529 Aug 2005

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

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