Visual Tracking in Unknown Environments Using Fuzzy Logic and Dead Reckoning
Author(s) -
Mohamed Amine Mekhtiche,
Zoubir Abdeslem Benselama,
Mohamed A. Bencherif,
Mohammed Zakariah,
Mansour Alsulaiman,
Ramdane Hedjar,
Mohammed Faisal,
Mohammed Algabri,
Khalid AlMuteb
Publication year - 2016
Publication title -
international journal of advanced robotic systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 46
eISSN - 1729-8814
pISSN - 1729-8806
DOI - 10.5772/62120
Subject(s) - computer science , computer vision , obstacle avoidance , artificial intelligence , obstacle , dead reckoning , fuzzy logic , mobile robot , controller (irrigation) , path (computing) , motion planning , trajectory , robot , a priori and a posteriori , object (grammar) , global positioning system , telecommunications , agronomy , programming language , philosophy , physics , epistemology , astronomy , political science , law , biology
This paper addresses the problem of making a non-holonomic wheeled mobile robot (WMR) move to a target object using computer vision and obstacle-avoidance techniques. If a priori information about the obstacles is available, pre-planning the desired path can be a good candidate method. However, in so many cases, obstacles are dynamic. Therefore, our first challenge is to make the WMR move to a desired target while autonomously avoiding any obstacle along its path. The second challenge deals with visual-tracking loss; that is, when the target is lost from the camera scope, the robot should use Dead Reckoning (DR) to get back on its path towards the target. The Visual Tracking (VT) algorithm then takes the relay to reach the final destination, compensating for any errors due to DR by calculating the distance to the target when it is within the scope of the camera. The proposed system also uses two fuzzy-logic controllers; the first controller avoids objects while the second manages the path to the target. Different complex scenarios have been implemented, showing the validity of our multi-controller model
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