In this paper, we propose a vision-based recognition approach to control the posture of a robotic arm with three degrees of freedom (DOF) using static and dynamic human hand gestures. Two different methods are investigated to intuitively control a robotic arm posture in real-time using depth data collected by a Kinect sensor. In the first method, the user’s right index fingertip position is mapped to compute the inverse kinematics on the robot. Using the Forward And Backward Reaching Inverse Kinematics (FABRIK) algorithm, the inverse kinematics (IK) solutions are displayed in a graphical interface. Using this interface and his left hand, the user can intuitively browse and select a desired robotic arm posture. In the second method, the user’s left index position and direction are respectively used to determine the end-effector position and an attraction point position. The latter enables the control of the robotic arm posture. The performance of these real-time natural human control approaches is evaluated for precision and speed against static and dynamic obstacles.
展开▼
