Published Paper Details:

Robotic manipulators equipped with redundant degrees of freedom offer increased flexibility and dexterity but are prone to encountering singular configurations that hinder performance and control. This paper introduces novel strategies for singularity avoidance in redundant manipulators, focusing on optimizing their performance. Drawing inspiration from existing research, an innovative approach based on the Monte Carlo method for singularity analysis is proposed. By randomly mapping the robot's workspace and identifying points closest to a given trajectory, detection and characterization of singularity states is determined. Unlike traditional inverse calculation methods, this approach reduces computational demands while providing a clear graphical representation of singularity occurrences. Through rigorous mathematical analysis and illustrative graph ,the effectiveness of this method in singularity avoidance is presented. Furthermore, exploring practical applications through case studies involving various robot configurations is possible. This research contributes valuable insights and tools for improving the operational efficiency and adaptability of redundant manipulators in diverse robotic applications.