• DOI:10.1007/s10846-015-0182-6
• Corpus ID: 3134789

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

@article{Petkovi2016InputDN,
  title={Input Displacement Neuro-fuzzy Control and Object Recognition by Compliant Multi-fingered Passively Adaptive Robotic Gripper},
  author={Dalibor Petkovi{\'c} and Shahaboddin Shamshirband and Nor Badrul Anuar and Aznul Qalid Md Sabri and Zulkanain Abdul Rahman and Nenad D. Pavlovic},
  journal={Journal of Intelligent \& Robotic Systems},
  year={2016},
  volume={82},
  pages={177-187}
}

• Dalibor Petković, Shahaboddin Shamshirband, +3 authors N. D. Pavlovic
• Published 1 May 2016
• Computer Science
• Journal of Intelligent & Robotic Systems

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.