Accelerometer-based control of an industrial robotic arm

TL;DR

This paper presents a low-cost, accelerometer-based gesture recognition system using neural networks to intuitively control industrial robots with a focus on low response time and practical implementation.

Contribution

It introduces a novel gesture-based control system for industrial robots using wireless accelerometers and neural networks, emphasizing low latency and user-friendly operation.

Findings

Achieved 92% gesture recognition accuracy

System response time is approximately 160 milliseconds

Demonstrated practical control of an industrial robot

Abstract

Most of industrial robots are still programmed using the typical teaching process, through the use of the robot teach pendant. In this paper is proposed an accelerometer-based system to control an industrial robot using two low-cost and small 3-axis wireless accelerometers. These accelerometers are attached to the human arms, capturing its behavior (gestures and postures). An Artificial Neural Network (ANN) trained with a back-propagation algorithm was used to recognize arm gestures and postures, which then will be used as input in the control of the robot. The aim is that the robot starts the movement almost at the same time as the user starts to perform a gesture or posture (low response time). The results show that the system allows the control of an industrial robot in an intuitive way. However, the achieved recognition rate of gestures and postures (92%) should be improved in future, keeping the compromise with the system response time (160 milliseconds). Finally, the results of some tests performed with an industrial robot are presented and discussed.