Intelligent Path Generation for Robotic Navigation in Mechanical Based Manufacturing Systems Using AI-Driven Weighted Shortest Path Algorithms For Full-Fledged Automation

Abstract

This study presents an enhanced robot path planning approach based on Dijkstra’s algorithm augmented with path weighting to support intelligent navigation in complex mechanical and manufacturing environments. By incorporating weighted cost functions that account for factors such as distance, obstacle proximity, energy consumption, and motion smoothness, the proposed method enables robots to select more efficient and practical trajectories from the source to the target. The weighting mechanism allows the algorithm to adapt to different environmental constraints and operational priorities, making it suitable for dynamic and cluttered industrial settings. The proposed weighted Dijkstra-based path planning framework improves navigation performance by reducing unnecessary detours, minimizing traversal cost, and enhancing overall path optimality. This results in smoother robot motion, improved execution efficiency, and better utilization of mechanical systems within automated manufacturing processes. The approach is particularly effective in scenarios requiring precise movement planning, such as factory automation, material handling, and robotic assembly, where reliable and efficient navigation is critical for productivity and safety.