Automated Transportation Setting using Hybrid Powered Unmanned Vehicle
Keywords:
AGV robot car, ESP8266, INA219 current sensor, L298N motor driver, OV2640 camera module, Real-time video monitoring, 12 V 3000 mAh LiPo battery, 12 V 300 RPM DC motorAbstract
become a key solution for automating material transportation. However, traditional AGV systems, which rely on fixed infrastructure and static path planning algorithms, often struggle in dynamic environments, leading to inefficiencies, delays, and potential collisions. This project focuses on improving AGV path planning using a hybrid approach that combines the strengths of the D* algorithm and the Dynamic Window Approach (DWA). The D* algorithm is used for global path planning due to its strong adaptability in dynamic environments, while an improved DWA handles local obstacle avoidance with smoother navigation and lower computational complexity. A grid-based map is used to model the workshop layout, enabling real-time adaptability and route flexibility. The AGV is designed to maintain uniform speed and optimal turning radius, enhancing energy efficiency and reducing wear and tear. Simulation results demonstrate that the proposed fusion algorithm reduces transport time by approximately 45% and path length by 17% compared to the traditional D* algorithm, proving its effectiveness and practicality in real-world scenarios. This improved AGV system offers significant advantages in terms of operational efficiency, energy savings, and safety. It is highly suitable for applications in automated factories, warehouses, hospitals, and research labs, where dynamic conditions and efficient routing are critical. The integration of adaptive path planning, sensor-based obstacle detection, and intelligent decision-making makes this solution a step forward in achieving flexible, autonomous, and sustainable transportation in Industry 4.0 environments.
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