Behavioristic image-based pose control of mobile manipulators using an uncalibrated eye-in-hand vision system
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In the execution of material handling, the mobile manipulator is controlled to reach a station by its mobile base. This study adopts an uncalibrated eye-in-hand vision system to provide visual information for the manipulator to pick up a workpiece on the station. A novel vision-guided control strategy with a behavior-based look-and-move structure is proposed. This strategy is based on six image features, predefined by image moment method. In the designed neural-fuzzy controllers with varying learning rate, each image feature error is taken to generate intuitively one DOF motion command relative to the camera coordinate frame using fuzzy rules, which define a particular visual behavior. These behaviors are then fused to produce a final command action to perform grasping tasks using the proposed behavior fusion scheme. Finally, the proposed control strategy is experimentally applied to control the end-effector to approach and grasp a workpiece in various locations on a station.
KeywordsBehavior-based Behavior fusion Image moment Neural-fuzzy controller Vision-guided
The authors would like to thank the National Science Council of the Republic of China, for financially supporting this research under Contract No. NSC 102-2221-E-006-295.
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