Research on Truck AGV Control System

Xu, Qian; Ai, Changsheng; Geng, Dunyang; Ren, Gangchang; Wang, Zhiyong

doi:10.1109/iccsnt50940.2020.9305011

Cited by 5 publications

(6 citation statements)

References 4 publications

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“…Thus, the acceleration component for the AGV according to the coordinate system is calculated by (5):…”

Section: Figure 3 the Modeling Of An Agv Robot Systemmentioning

confidence: 99%

Experiment study of an automatic guided vehicle robot

Ha¹,

Thuong²,

2023

IJPEDS

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<p>This paper presents the design and control of an autonomous robot-automation guided vehicle (AGV) with a load of 50 kg and a DC motor in theory and experiment. This robot has the function of transporting tools and equipment in the factory. This robot is designed and built control system including hardware and software. In which microcontroller type STM32F407VG is selected to control the primary system. The AGV robot is controlled to move according to the required trajectory. A PID controller controls the DC motor. The AGV robot moves precisely according to the routes set in the factory. At the same time, the AGV has good obstacle avoidance. The results of the proposed solution are proven through simulation and experiment.</p>

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“…Thus, the acceleration component for the AGV according to the coordinate system is calculated by (5):…”

Section: Figure 3 the Modeling Of An Agv Robot Systemmentioning

confidence: 99%

Experiment study of an automatic guided vehicle robot

Ha¹,

Thuong²,

2023

IJPEDS

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“…The trajectory and navigation setup for self-propelled robots involves using linear, intelligent, and hybrid control methods. A common approach includes employing two control loop structures, such as an external kinematic loop using the Lyapunov function to synthesize the position and speed controller [6]. Furthermore, various dynamic loop control techniques like proportional integral derivative (PID) control [7], slip control [8]- [10], and backstepping command [11] have been applied.…”

Section: Introductionmentioning

confidence: 99%

Trajectory tracking control based on genetic algorithm and proportional integral derivative controller for two-wheel mobile robot

Ha,

Thi Thuong,

Ngoc Truc

2024

Bulletin EEI

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This paper uses the genetic algorithm (GA) to optimize the proportional integral derivative (PID) controller parameters to present the motion control design for a two-wheeled mobile robot autonomous system. The GA algorithm determines a collision-free travel curve for a robot with a tangential velocity restriction constraint. A trajectory-tracking controller based on the PID control structure is developed to monitor the calculated route curves for the mobile robot. Simulation results show the effectiveness of the GA-PID controller compared to the PID controller. The GA-PID controller demonstrates improved performance in trajectory tracking and collision avoidance, making it suitable for controlling the motion of two-wheeled mobile robots. The GA's optimization process allows for better tuning of the PID controller parameters, resulting in more efficient and accurate robot motion control. The results suggest that the proposed GA-PID controller is a promising approach for enhancing mobile robots' autonomous navigation capabilities.

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“…On the other hand, PID algorithms assist vehicles in making real-time adjustments, enabling them to maintain stability in various environments and conditions [ 15 , 16 , 18 , 22 ]. Based on the literature [ 1 , 2 , 7 , 9 , 11 , 12 , 13 , 14 , 15 , 16 , 18 , 21 , 22 , 23 , 24 ], it is evident that PID control algorithms are primarily applied in linear systems, while AGV motion control systems are nonlinear. PID algorithms mainly involve cumulative error calculations, making them susceptible to saturation.…”

Section: Introductionmentioning

confidence: 99%

“…With the emerging market of advanced Autonomous Ground Vehicles (AGVs) and mobile robots, the development of vehicle lateral and longitudinal control technology has become an important research topic. Research on AGVs primarily involves planning decisions, motion control, and environmental awareness, with motion control being the fundamental and crucial task for achieving precise AGV motion [1,2]. AGV motion control encompasses two key aspects: longitudinal control and lateral control [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Research on Accurate Motion Trajectory Control Method of Four-Wheel Steering AGV Based on Stanley-PID Control

Fu,

Liu,

Zhang

2023

Sensors

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With the continuous progress and application of robotics technology, the importance of mobile robots capable of adapting to specialized work environments is gaining prominence. Among them, achieving precise and stable control of AGVs (Automated Guided Vehicles) stands as a paramount task propelling the advancement of mobile robotics. Consequently, this study devises a control system that enables AGVs to attain stable and accurate motion through equipment connection and debugging, kinematic modeling of the four-wheel steering AGV, and a selection and comparative analysis of motion control algorithms. The effectiveness of the Stanley-PID control algorithm in guiding the motion of a four-wheel steering AGV is validated through MATLAB 2021a simulation software. The simulation results illustrate the outstanding stability and precise control capabilities of the Stanley-PID algorithm.

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Research on Truck AGV Control System

Cited by 5 publications

References 4 publications

Experiment study of an automatic guided vehicle robot

Experiment study of an automatic guided vehicle robot

Trajectory tracking control based on genetic algorithm and proportional integral derivative controller for two-wheel mobile robot

Research on Accurate Motion Trajectory Control Method of Four-Wheel Steering AGV Based on Stanley-PID Control

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