Human–manipulator interface using hybrid sensors with Kalman filters and adaptive multi-space transformation

Abstract

This paper utilizes a human–robot interface system which incorporates Kalman filter (KF) and Adaptive Multi-space Transformation (AMT) to track the pose of the human hand for controlling the robot manipulator. This system employs a 3D camera (Kinect) to determine the orientation and the translation of the human hand. We use Camshift algorithm to track the hand. KF is used to estimate the translation of the human hand. Although a KF is used for estimating the translation, the translation error increases in a short period of time when the sensors fail to detect the hand motion. Therefore, a methodology to correct the translation error is required. What is more, to be subject to the perceptive limitations and the motor limitations, human operator is hard to carry out the high precision operation. This paper proposes an Adaptive Multi-space Transformation (AMT) method to assist the operator to improve the accuracy and reliability in determining the pose of the robot. The human–robot interface system was experimentally tested in a lab environment, and the results indicate that such a system can successfully control a robot manipulator.