Ira Kumalasari, Harits Ar Rosyid, Siti Sendari, Dyah Lestari, Achmad Safii
Excessive energy consumption remains a critical problem in autonomous cleaning robots, particularly when operating in large and complex environments where efficiency directly affects coverage and operational costs. This study aims to analyze how task scheduling strategies influence the robot's energy use under different dust distribution patterns. To achieve this, four spatial conditions were modeled: Start Small (SS), Start Large (SL), Center Small (CS), and Center Large (CL). Two scheduling strategies were applied: Stage 1 (small-tolarge) and Stage 2 (large-to-small). The simulations were conducted in a 94 × 56 grid environment using a hybrid A** algorithm that combines Manhattan and Octile heuristics for global navigation with a greedy method for local coverage. The results show that scheduling order significantly impacts efficiency. Stage 1 is more effective when dust is scattered in small clusters near the starting area or when large clusters are centrally located, while Stage 2 performs better when large clusters dominate the environment. For the CS condition, both strategies produce nearly identical performance. These findings demonstrate that appropriate scheduling, adapted to spatial dust patterns, can optimize robot energy use without modifying the underlying navigation algorithm. © 2025 IEEE.
Universitas Negeri Malang, Department of Electrical Engineering and Informatics, Malang, Indonesia
