General Algorithm of n-Multiple Coil Shape for Variable Links Hyper-Redundant Robotic Manipulator

The ‗S‘ shape formation, termed as multiple coil shape in hyper-redundant manipulator is essential for negotiating cluttered environment that exists inside the human body, engine compartment or within a collapsed building. The flexibility introduced by the multiple coil shape can be further enhanced...

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Bibliographic Details
Main Authors: Jamali, Annisa, Khan, Md. Raisuddin, Rahman , Md Mozasser
Format: Conference or Workshop Item
Language:English
Published: 2011
Subjects:
Online Access:http://irep.iium.edu.my/7041/
http://irep.iium.edu.my/7041/
http://irep.iium.edu.my/7041/1/General_Algorithm_of_m-Multiple_Coil_Shape_for_Variable_Links_Hyper-Redundant_Robotic_Manipulator.pdf
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Summary:The ‗S‘ shape formation, termed as multiple coil shape in hyper-redundant manipulator is essential for negotiating cluttered environment that exists inside the human body, engine compartment or within a collapsed building. The flexibility introduced by the multiple coil shape can be further enhanced by having extensible links. In this paper, a general algorithm for n-links hyper-redundant robot manipulator to produce n-multiple coil shape to reach final desired position with variable links is presented. The coil shape produced from a new inverse kinematics solution is based on Virtual Layer Approach. Meanwhile, the multiple coil shape is arranged sequentially in an elbow up and elbow down manner. An algorithm is then developed to control the links of the robot along the path so that the robot will travel in such a way that each of the follower links will take place of the earlier link‘s position. The main advantage of this method is it involves simple computation to each adjoining coil shape. Thus, it eliminates the mathematical complexity in computing m-multiple coil shape. Further, the method allows planar manipulator to reach the same desired position by different paths. Numerical simulations for planar models are presented in order to illustrate the competency of the model.