Accelerometer-based control of an industrial robotic arm

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.

[1]  Aude Billard,et al.  Active Teaching in Robot Programming by Demonstration , 2007, RO-MAN 2007 - The 16th IEEE International Symposium on Robot and Human Interactive Communication.

[2]  Doheon Lee,et al.  Speed Estimation From a Tri-axial Accelerometer Using Neural Networks , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  A. Kazi,et al.  The MORPHA style guide for icon-based programming , 2002, Proceedings. 11th IEEE International Workshop on Robot and Human Interactive Communication.

[4]  Kouichi Murakami,et al.  Gesture recognition using recurrent neural networks , 1991, CHI.

[5]  Rüdiger Dillmann,et al.  Teaching and learning of robot tasks via observation of human performance , 2004, Robotics Auton. Syst..

[6]  Miwako Doi,et al.  A real-time vision-based interface using Motion Processor and applications to robotics , 2003, Systems and Computers in Japan.

[7]  Tom Duckett,et al.  Position teaching of a robot arm by demonstration with a wearable input device , 2004 .

[8]  Masatoshi Ishikawa,et al.  Gesture recognition using laser-based tracking system , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[9]  Germano Veiga,et al.  Programming-by-demonstration in the coworker scenario for SMEs , 2009, Ind. Robot.

[10]  G. Hirzinger,et al.  The DLR-KUKA success story: robotics research improves industrial robots , 2005, IEEE Robotics & Automation Magazine.

[11]  J. Norberto Pires.  Robot-by-voice: experiments on commanding an industrial robot using the human voice , 2005, Ind. Robot.

[12]  Jing Yang,et al.  A 3D Hand-drawn Gesture Input Device Using Fuzzy ARTMAP-based Recognizer , 2006 .

[13]  Sebastian Thrun,et al.  A Gesture Based Interface for Human-Robot Interaction , 2000, Auton. Robots.