Stability recovery of eye-in-hand manipulator by inter-sample feature estimation

This paper considers a robot motion control problem with visual information. A robust control law has been proposed for the feature-based feedback control based on the passivity of the robot dynamics. The control law, however, is implemented by digital computer with two different sampling periods, namely, periods for angle sampling (short) and image sampling (large). In general, the performance of the system is degraded by the digital implementation. In this paper, we propose to estimate inter-sample value of the image feature by using the updated angles to recover the performance. The effectiveness of the proposed method is demonstrated by experimental results.