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      An Augmented Reality Based Human-Robot Interaction Interface Using Kalman Filter Sensor Fusion

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          Abstract

          In this paper, the application of Augmented Reality (AR) for the control and adjustment of robots has been developed, with the aim of making interaction and adjustment of robots easier and more accurate from a remote location. A LeapMotion sensor based controller has been investigated to track the movement of the operator hands. The data from the controller allows gestures and the position of the hand palm’s central point to be detected and tracked. A Kinect V2 camera is able to measure the corresponding motion velocities in x, y, z directions after our investigated post-processing algorithm is fulfilled. Unreal Engine 4 is used to create an AR environment for the user to monitor the control process immersively. Kalman filtering (KF) algorithm is employed to fuse the position signals from the LeapMotion sensor with the velocity signals from the Kinect camera sensor, respectively. The fused/optimal data are sent to teleoperate a Baxter robot in real-time by User Datagram Protocol (UDP). Several experiments have been conducted to test the validation of the proposed method.

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          Most cited references 31

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          Human-Robot Interaction: A Survey

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            Distributed weighted robust Kalman filter fusion for uncertain systems with autocorrelated and cross-correlated noises

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              Dynamic Hand Gesture Recognition With Leap Motion Controller

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                22 October 2019
                October 2019
                : 19
                : 20
                Affiliations
                [1 ]ASTUTE 2020 (Advanced Sustainable Manufacturing Technologies), Swansea University, Swansea SA1 8EN, UK; A.A.Fahmy@ 123456Swansea.ac.uk (A.F.); J.Sienz@ 123456Swansea.ac.uk (J.S.)
                [2 ]School of Automation and Electrical Engineering, Qingdao University of Science and Technology, Qingdao 266042, China
                [3 ]Department of Electrical Power and Machines, Helwan University, Helwan 11795, Egypt
                Author notes
                [* ]Correspondence: chunxu.li@ 123456swansea.ac.uk ; Tel.: +44-7592888041
                Article
                sensors-19-04586
                10.3390/s19204586
                6832171
                31652544
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

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