Sensor referenced guidance and control for robotic nanomanipulation

Abstract

Atomic force microscope (AFM) has been used as a manipulation tool for a decade. The problem of lacking real time visual feedback still limits its efficiency and hinders its wide application. Although the model based visual feedback can partly solve this problem, due to the complexity of nano environment, it is difficult to use a model to accurately describe the object's behavior. The modeling error will give the operator a false feedback and lead to a failed manipulation. In this paper, a strategy for visual feedback error on-line detection and correction is proposed to solve this problem. As the real time force information is a key factor for this strategy, an adaptable end effector is employed to accurately measure the interaction force between the probe and the nano-objects, and the system error is also compensated to improve the accuracy of interaction force measurement. Based on the true real time force information, an extended Kalman filter is developed to online detect whether there is a false feedback. Once a false feedback is detected, an optimal searching pattern is generated to get the real manipulation result in a short time. With the assistance of this strategy, the false visual feedback can be realtime detected and corrected without interrupting manipulation. Complex manipulation task can be finished without being interrupted by a new image scan. Experiments of manipulating nano-particles are performed to verify the effectiveness of this strategy, which demonstrated the improved efficiency of the AFM based nano-assembly system.