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Using a logic branching weighted algorithm to train robots for splined shaft-hole assembly

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3 Author(s)
Jaura, A.K. ; Sci. Res. Lab., Ford Motor Co., Dearborn, MI, USA ; Krouglicof, N. ; Osman, M.O.M.

The article presents a logic branching weighted algorithm (LBWA) to train a robot to perform splined shaft and hole assembly in a robotic cell. The LBWA uses angular and linear positional changes and assigns weights to each of these based on the force sensing information from an assembly path and evolves a best move strategy for the robot to complete the task. The machine learning capability of the robot depends on the discretization of the force-torque information that is monitored and mapped for each position. Prior to commencing the move, the LBWA compares the evaluating functions. A trade-off is to be made between the information space and the learning time for the robot in a real-life situation. Experimental results are presented to establish the effectiveness of the LBWA in training the robot

Published in:

Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on  (Volume:29 ,  Issue: 3 )