Sensor Fusion and Control Algorithm Design for Precision Mechatronic Arms in Medical Robotics

Abstract

 Mechatronic systems are becoming more essential in applications, necessitating great accuracy. 
Although a robot functions with fundamental sensor input in controlled settings, attaining micro-level precision 
necessitates more extensive data integration, particularly in dynamic situations. Integrating data from several 
sensors is essential for enhancing a robot's positioning precision, since the precision of a single sensor type is 
inadequate. The domain of micro-positioning introduces novel issues and tasks that have been progressively 
examined in the literature released between 2015 and 2025. Micro-positioning is a sophisticated process 
encompassing physical drift, ambient influences, and sensor signal inaccuracies. Hybrid fusion is an imaging 
fusion methodology that integrates components at several levels. Effective robot deployments in these 
circumstances necessitate integrating various sensors and ensuring accurate data fusion. This requires 
advanced fusion computations, multiple sensors, exact calibration techniques using sensor fusion, and 
sophisticated data processing methods. Sensor data fusion methods for precise robot micro-positioning are 
investigated in this work based on the literature. The focus is on the investigated detectors, the applied 
synthesis methods, developed computations, and how to find current shortcomings for possible system 
improvement. The results of the collected ideas and the exchanges are presented.