Image-Guided Navigation Control for Automated Nanopositioning Based on Dense Photometric

Image-guided automated positioning of microobjects in dynamic environments is highly needed in robot micro–nano manipulation. In this article, rendering the problem of collision-free microobject nanopositioning problem as a visual servoing problem, an image-guided navigation control scheme based on dense photometric is proposed. Specifically, the finite convergence domain existed in classical dense photometric-based visual servoing is treated as a constraint and uniformly modeled together with obstacle constraints in the navigation process. Subsequently, an online constraint handling unit based on artificial potential fields is developed to generate collision-free poses while consistently constraining the image evolution to remain within the local convergence region. Additionally, an inverse compositional algorithm is adopted to accelerate control computation, thereby enabling real-time performance. Finally, a series of experiments are conducted to verify the performance of the proposed method. Experimental results demonstrate that the method achieves reliable automatic nano-positioning and effective collision avoidance in both static and dynamic obstacle environments, confirming its feasibility and robustness in practical applications.