Implementing Sensor Signal Fusion for Accurate Positioning of Micro-Robotic Systems

Modern scanning microscopes and robotic scanning systems increasingly use visual recognition and machine learning technologies to extract complex data from acquired images. This study examined sensor data fusion in optical imaging to detect and control the deviation of the position of the tool during various micro-manipulations for biologic and microscale engineering. The sensor data fusion study was performed using a scanning micro-robotic system with an integrated optical microscope and a vision sensor providing an image of the object’s bottom. The bottom vision sensor is a typical complementary metal–oxide–semiconductor sensor that is used to observe micrometer-sized semi-transparent objects. The challenge for sensor fusion in such a study is not only data fusion, but also the trajectory deviation inherent in directing the manipulator in the X and Y directions according to the selected trajectory. The data fusion method was applied to estimate deviations from the given trajectory of the scanning microscope. The unique novelty of this work is that an additional vision sensor is used to increase the accuracy of positioning determination of a scanning micro-robotic system, placed under the semi-transparent object, using the fusion of the obtained data, thus additionally controlling the objective deviations. By testing several known data fusion methods, a unique solution was achieved. The proposed sensor fusion method achieved a positioning accuracy of less than 0.5 μm at speeds up to 5 mm/s. Experimental results demonstrate that the system maintains high stability. This quantitative performance proves the system’s suitability for high-precision biological micro-manipulation, where mechanical drift was previously a limiting factor.