AGV Trajectory Control Based on Laser Sensor Navigation

Bui, Thanh Luan; Doan, Phuc Thinh; Park, Soon Sil; Kim, Hak Kyeong; Kim, Sang Bong

doi:10.12777/ijse.4.1.16-20

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…The AGV robot wants to move automatically. Therefore, it needs to solve three main issues: positioning, trajectory planning, and motion control in navigation for AGV [4]- [6]. In particular, the motion control system of the AGV robot plays a crucial role in moving from the starting point to the destination [7].…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%

Experiment study of an automatic guided vehicle robot

Ha¹,

Thuong²,

2023

IJPEDS

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Development of Navigation Control Algorithm for AGV Using D* search Algorithm

Kim

Kim²,

Jeong³

et al. 2013

int. j. sci. eng.

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In this paper, we present a navigation control algorithm for Automatic Guided Vehicles (AGV) that move in industrial environments including static and moving obstacles using D* algorithm. This algorithm has ability to get paths planning in unknown, partially known and changing environments efficiently. To apply the D* search algorithm, the grid map represent the known environment is generated. By using the laser scanner LMS-151 and laser navigation sensor NAV-200, the grid map is updated according to the changing of environment and obstacles. When the AGV finds some new map information such as new unknown obstacles, it adds the information to its map and re-plans a new shortest path from its current coordinates to the given goal coordinates. It repeats the process until it reaches the goal coordinates. This algorithm is verified through simulation and experiment. The simulation and experimental results show that the algorithm can be used to move the AGV successfully to reach the goal position while it avoids unknown moving and static obstacles.[Keywords-navigation control algorithm; Automatic Guided Vehicles (AGV); D* search algorithm] I.

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Computer-Aided Design of the Robotic Platform Control System Using Adams and Matlab

Porkhalo

Rubanov

Bazhanov

et al. 2021

Proceedings of the SWSU

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Purpose of research. The purpose of this study is to assess the possibility of developing and simulating an optimal control system for the longitudinal movement of a robocars according to the criterion of maximum speed by using the MSC.Adams virtual modeling system and the MATLAB mathematical modeling package Methods. One of the research approaches is system modeling, while a virtual modeling system is used as a software tool for synthesizing a virtual physical model of a robot, and a package of application programs for solving technical computing problems MATLAB and a graphical environment for simulation Simulink are used to model a control system. The Pontryagin maximum principle is used as a synthesis method for the longitudinal displacement control system, and the maximum speed is used as an optimality criterion. Results: The structure of the control system for a robotic platform is presented; an optimal control algorithm is developed and implemented in the Simulink environment. The structure of the physical model management system with data transfer to Adams has been developed. The acceleration curves and the phase portrait of the model control system during the longitudinal movement of the robotic platform are presented and analyzed. Conclusion. As can be seen from the above simulation results, the optimal positional control law, which implements the maximum principle, fulfills the task with the required quality indicators. In this regard, the proposed algorithm can be used in the development of control systems for the longitudinal movement of mobile robots. Joint modeling of the virtual prototype and the object control system in the Matlab and Adams environment avoids the production of a fullscale model and makes it possible to take into account the physical properties of the object without creating an analytical model.

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AGV Trajectory Control Based on Laser Sensor Navigation

Cited by 3 publications

References 7 publications

Experiment study of an automatic guided vehicle robot

Experiment study of an automatic guided vehicle robot

Development of Navigation Control Algorithm for AGV Using D* search Algorithm

Computer-Aided Design of the Robotic Platform Control System Using Adams and Matlab

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