Design of adaptive moving-target tracking control for vision-based mobile robot

This study constructs an adaptive moving-target tracking control (AMTC) scheme via a dynamic Petri recurrent-fuzzy-neural-network (DPRFNN) for a vision-based mobile robot with a tilt camera. First, a continuously adaptive mean shift (CAMS) algorithm is adopted for the moving-object detection, and a...

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Bibliographic Details
Published inIEEE Symposium on Computational Intelligence in Control and Automation (Print) pp. 194 - 199
Main Authors You-Wei Lin, Rong-Jong Wai
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2013
Subjects
Cameras
Mobile robots
Robot kinematics
Robot vision systems
Vectors
Wheels
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ISSN2328-1448
DOI10.1109/CICA.2013.6611684

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Summary:This study constructs an adaptive moving-target tracking control (AMTC) scheme via a dynamic Petri recurrent-fuzzy-neural-network (DPRFNN) for a vision-based mobile robot with a tilt camera. First, a continuously adaptive mean shift (CAMS) algorithm is adopted for the moving-object detection, and a model-based conventional sliding-mode control (CSMC) strategy is introduced. Moreover, it further designs a model-free AMTC scheme with a DPRFNN for imitating the CSMC strategy for relaxing the control design dependent on detailed system information and alleviating chattering phenomena caused by the inappropriate selection of uncertainty bounds. In addition, a switching path-planning scheme plus the AMTC is designed without detailed environmental information, large memory size and heavy computation burden for the obstacle avoidance of a mobile robot. Furthermore, numerical simulations are given to verify the effectiveness of the proposed AMTC scheme under different target tracking, and its superiority is indiented in comparison with the CSMC System
ISSN:2328-1448
DOI:10.1109/CICA.2013.6611684