DOI: https://doi.org/10.36347/sjet.2026.v14i09.005

Pages: 519-525

Warehouse automation has become increasingly important as companies work to meet the growing demand for faster shipping and greater operational efficiency. Autonomous shelf-transport warehouse robots have become a key part of this transformation because they can move inventory without direct human involvement. However, these robots rely on limited battery power to operate multiple subsystems, including drive motors, sensors, onboard processors, and wireless communication devices. Most current warehouse robots use fixed power settings that do not adjust to changing operating conditions, leading to unnecessary energy consumption and reduced battery life. As warehouses continue expanding their robotic fleets, improving energy efficiency has become an important engineering challenge. This paper proposes a dynamic power management framework designed specifically for autonomous shelf-transport warehouse robots. Rather than relying on fixed operating parameters, the proposed system continuously monitors battery condition, shelf weight, warehouse traffic, travel distance, processor workload, and sensor activity. Using this information, the framework dynamically adjusts motor power, robot speed, sensing frequency, computing resources, route selection, and charging decisions to maximize battery efficiency while maintaining productivity. Artificial intelligence techniques, including predictive modeling and adaptive decision-making, are also discussed as methods for further improving system performance. Although the proposed framework has not been physically implemented, it is based on current research in robotics, battery management, and intelligent control systems. The design demonstrates how software-based optimization can reduce energy consumption, extend battery life, lower operating costs, and improve the long-term efficiency of autonomous warehouse robotics.