An Integrated Navigation Strategy for Obstacle Avoidance and Limited Charge Storage
Shizhe Chen, Feifei Wu, Jingyin Yao, Yunlong Chen, Yin Hu, Zhigang Xing, Cunle Wang, Changqing Hu · Advances in transdisciplinary engineering · 2024
This study presents a comprehensive optimization strategy to address the challenges of obstacle avoidance and low battery-induced stranding faced by automated guided vehicles (AGVs) in complex industrial environments. Initially, considering the variety of obstacles AGVs may encounter while navigating, the system creates a virtual leader and employs an enhanced dynamic window approach (DWA) to integrate real-time data on AGV’s current position, speed, battery status, and surrounding obstacle information. This integration facilitates the planning of obstacle avoidance paths to minimize speed fluctuations. Furthermore, the strategy involves real-time monitoring of the AGV’s battery status and predicting its consumption trends. When the battery level falls below a predefined threshold, the system automatically recalibrates the AGV’s path, guiding it to the nearest charging station for recharging, thus ensuring task continuity and preventing stranding due to battery depletion. This approach not only improves AGV’s operational efficiency but also significantly reduces the risk of stranding due to collisions or being too far from charging stations. Through simulation testing, it is demonstrated that the proposed strategy effectively enhances AGV’s battery management efficiency and reduces incidents of stranding caused by obstacles and battery issues. This comprehensive optimization strategy provides an effective solution for the energy and safety management of AGVs, significantly contributing to the enhancement of AGV operational stability and task completion rates.