Path Optimization and Singularity Avoidance in Robotic Manipulators Using Hybrid Aco-FLC Techniques

The rapid advancements in robotics have necessitated the development of innovative techniques for efficient path optimization and singularity avoidance in robotic manipulators. This study explores the integration of ant colony optimization (ACO) and fuzzy logic control (FLC) to address these challenges in manipulators with six degrees of freedom (6-DOF). ACO is utilized for global path planning, ensuring optimal trajectories while avoiding obstacles and singularity zones. Simultaneously, FLC provides local adaptability, refining the path for smoothness and stability in dynamic environments. The hybrid ACO-FLC approach demonstrates significant improvements in trajectory efficiency, singularity avoidance, and computational performance. The ACO methodology demonstrates an average improvement of 18% in path efficiency compared to conventional methods. Furthermore, the integration of FLC enhances trajectory smoothness by 25%, ensuring accurate and stable motion. This hybrid ACO-FLC framework achieves an exceptional 98% singularity avoidance rate and reduces computational overhead by 15%, facilitating faster and more efficient performance in dynamic and complex robotic environments. Comparative analysis with other techniques highlights the superiority of the proposed method in achieving robust and adaptive control.