A novel industrial AGV control strategy based on dual-wheel chassis model

Abstract

Purpose Automatic guided vehicles (AGVs) are widely used in industrial fields. But most control strategies merely take the lateral force into consideration. This will reduce the accuracy, stability and robustness and will pay additional costs. Therefore, this paper aims to design a control strategy that initially considers lateral force. Thereby, it will improve the accuracy, stability and robustness and reduce the overall cost of AGV. Design/methodology/approach To achieve the goal of comprehensively improving AGV operating performance, this paper presents a new scheme, combining the dual-wheeled chassis model (DCM) using proportional–integral–differential (PID) control and a supporting quick response (QR) code navigation technology. DCM is the core, which analyzes the deviation caused by lateral force. Then, DCM with PID control by the control law is combined to suppress the errors. Meanwhile, QR code navigation technology provides effective data support for the control strategy. Findings Most AGV experiments are carried out in a standard environment. However, this study prepares unfavorable scenarios and operating conditions for the experiments that generate detailed data to demonstrate this study’s strategy, which can make an accurate, stable and robust operation process of AGV under various adverse environmental and mechanical factors. Originality/value This study proposed DCM, fully considering lateral force and converting the force into velocity. Subsequently, PID controls the speed of two wheels to reduce the error. QR code provides an efficient and low – cost way to obtain information. The three are cleverly combined as a novel industrial AGV control strategy, which can comprehensively improve the operating performance while reducing overall costs.

An Accurate and Rapid Docking Algorithm for Four-Way Shuttle in High-Density 3D Warehousing Environment

“…This approach allows the robot to dock without a complete stop by aligning the direction and distance between the robot and the docking station. Tsiogas [19] introduced a vision-based approach that enables the safe and autonomous operation of pallet moving vehicles, and Ding [20] proposed a combination of the double-wheel chassis model (DCM) and PID control strategy to enhance the precision and stability during docking. Moreover, visual strategies are applied for accurate docking alignment.…”