NARX Neural Network for Safe Human–Robot Collaboration Using Only Joint Position Sensor

Abdel-Nasser Sharkawy () and Mustafa M. Ali
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Abdel-Nasser Sharkawy: Mechatronics Engineering, Mechanical Engineering Department, Faculty of Engineering, South Valley University, Qena 83523, Egypt
Mustafa M. Ali: Mechatronics Engineering, Mechanical Engineering Department, Faculty of Engineering, South Valley University, Qena 83523, Egypt

Logistics, 2022, vol. 6, issue 4, 1-16

Abstract: Background : Safety is the very necessary issue that must be considered during human-robot collaboration in the same workspace or area. Methods : In this manuscript, a nonlinear autoregressive model with an exog-enous inputs neural network (NARXNN) is developed for the detection of collisions between a manipulator and human. The design of the NARXNN depends on the dynamics of the manipulator’s joints and considers only the signals of the position sensors that are intrinsic to the manipulator’s joints. Therefore, this network could be applied and used with any conventional robot. The data used for training the designed NARXNN are generated by two experiments considering the sinusoidal joint motion of the manipulator. The first experiment is executed using a free-of-contact motion, whereas in the second experiment, random collisions by human hands are performed with the robot. The training process of the NARXNN is carried out using the Levenberg–Marquardt algorithm in MATLAB. The evaluation and the effectiveness (%) of the developed method are investigated taking into account different data and conditions from the used data for training. The experiments are executed using the KUKA LWR IV manipulator. Results : The results prove that the trained method is efficient in estimating the external joint torque and in correctly detecting the collisions. Conclusions : Eventually, a comparison is presented between the proposed NARXNN and the other NN architectures presented in our previous work.

Keywords: human–manipulator collaboration; collision detection; NARX neural network; joint position sensor; training and testing; evaluation and effectiveness (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2022
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