Optimizing Cleaning Path for Coal Dust Removal Using Dual Stage Tracking Method - Kumalasari, Ira; Ar Rosyid, Harits; Sendari , Siti; Mokhtar , Norrima Binti; Setumin , Samsul (Universitas Ahmad Dahlan, 2024)
Manual disaster mitigation at the Java Bali power plant, particularly related to fire risks from coal dust during electricity production, often requires halting operations, leading to significant revenue loss and power outages. This study aims to address this issue by proposing an automated solution to clean coal dust without interrupting production, utilizing a dual-stage tracking method for robot-assisted coal dust cleaning. The research contributes by developing a dual-stage A* algorithm that optimizes robot movements for cleaning tasks in power plant environments, outperforming single-stage BFS and single-stage A* algorithms. The research is divided into two phases: object detection and robot motion path selection. The dual-stage A* algorithm is compared against single-stage BFS and single-stage A* methods through a series of experiments evaluating their efficiency and effectiveness. The dual-stage A* method demonstrates superior performance in terms of path optimization, reducing cleaning time, and improving operational safety. Specifically, the dual-stage A* algorithm reduces energy consumption by 169 units and grid traversal by 84 units compared to single-stage methods, ensuring thorough dust removal while minimizing fire hazards. The dual-stage A* algorithm proves to be the optimal solution for coal dust cleaning in power plants, allowing for safe, continuous operation without the need for production halts. Future work should focus on addressing implementation costs and technical constraints to enhance real-world applicability.