Journal Article

Human–robot collisions detection for safe human–robot interaction using one multi-input–output neural network

Sharkawy, Abdel-Nasser, Aspragathos, Nikos, Koustoumpardis, Panagiotis N.

2020

Overview

In this paper, a multilayer feedforward neural network-based approach is proposed for human–robot collision detection taking safety standards into consideration. One multi-output neural network is designed and trained using data from the coupled dynamics of the manipulator with and without external contacts to detect unwanted collisions and to identify the collided link using only the intrinsic joint position and torque sensors of the manipulator. The proposed method is applied to the collaborative robots, which will be very popular in the near future, and is implemented and evaluated in 3D space motion taking into account the effect of the gravity. KUKA LWR manipulator is an example of the collaborative robots, and it is used for doing the experiments. The experimental results prove that the developed system is considerably efficient and very fast in detecting the collisions in the safe region and identifying the collided link along the entire workspace of the three-joint motion of the manipulator. Separate/uncoupled neural networks, one for each joint, are also designed and trained using the same data, and their performance is compared with the coupled one.

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V
• Subject: Artificial Intelligence / Artificial neural networks / Collaboration / Collisions / Computational Intelligence / Control / Engineering / Manipulators / Mathematical Logic and Foundations / Mechatronics / Methodologies and Application / Multilayers / Neural networks / Position sensing / Robot arms / Robotics / Sensors / Three dimensional motion / Torque sensors (robotics)