A novel human-robot interface using hybrid sensors with Kalman filters

Abstract

Purpose – The aim of this paper is to present a novel methodology which incorporates Camshift, Kalman filter (KFs) and adaptive multi-space transformation (AMT) for a human-robot interface, which perfects human intelligence and teleoperation. Design/methodology/approach – In the proposed method, an inertial measurement unit is used to measure the orientation of the human hand, and a Camshift algorithm is used to track the human hand using a three-dimensional camera. Although the location and the orientation of the human can be obtained from the two sensors, the measurement error increases over time due to the noise of the devices and the tracking errors. KFs are used to estimate the location and the orientation of the human hand. Moreover, to be subject to the perceptive limitations and the motor limitations, human operator is hard to carry out the high precision operation. An AMT method is proposed to assist the operator to improve accuracy and reliability in determining the pose of the robot. Findings – The experimental results show that this method would not hinder most natural human-limb motion and allows the operator to concentrate on his/her own task. Compared with the non-contacting marker-less method (Kofman et al., 2007), this method proves more accurate and stable. Originality/value – The human-robot interface system was experimentally verified in a laboratory environment, and the results indicate that such a system can complete high-precision manipulation efficiently.