Input Displacement Neuro-fuzzy Control and Object Recognition by Compliant Multi-fingered Passively Adaptive Robotic Gripper

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• 作者：Dalibor Petković ; Shahaboddin Shamshirband…
• 关键词：Multi ; fingered gripper ; Underactuated gripper ; Embedded sensors ; Object recognizing ; ANFIS controller
• 刊名：Journal of Intelligent and Robotic Systems
• 出版年：2016
• 出版时间：May 2016
• 年：2016
• 卷：82
• 期：2
• 页码：177-187
• 全文大小：2,147 KB
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• 作者单位：Dalibor Petković (1)
  Shahaboddin Shamshirband (2)
  Nor Badrul Anuar (2)
  Aznul Qalid Md Sabri (2)
  Zulkanain Bin Abdul Rahman (3)
  Nenad D. Pavlović (1)
  
  1. Faculty of Mechanical Engineering, Deparment for Mechatronics and Control, University of Niš, Aleksandra Medvedeva 14, 18000, Niš, Serbia
  2. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia
  3. Department of History, Faculty of Arts and Social Sciences Building, University of Malaya, 50603, Kuala Lumpur, Malaysia
  
• 刊物类别：Engineering
• 刊物主题：Automation and Robotics
  Electronic and Computer Engineering
  Artificial Intelligence and Robotics
  Mechanical Engineering
  
• 出版者：Springer Netherlands
• ISSN：1573-0409

文摘

The requirement for new flexible adaptive grippers is the ability to detect and recognize objects in their environments. It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult make decision strategies using conventional techniques. Here, an adaptive neuro fuzzy inference system (ANFIS) for controlling input displacement and object recognition of a new adaptive compliant gripper is presented. The grasping function of the proposed adaptive multi-fingered gripper relies on the physical contact of the finger with an object. This design of the each finger has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize particular shapes of the grasping objects. Fuzzy based controllers develop a control signal according to grasping object shape which yields on the firing of the rule base. The selection of the proper rule base depending on the situation can be achieved by using an ANFIS strategy, which becomes an integrated method of approach for the control purposes. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. The simulation results presented in this paper show the effectiveness of the developed method.