Mechatronic Modelling of Industrial AGVs: A Complex System Architecture

Sierra-García, J. Enrique; Santos, Matilde

doi:10.1155/2020/6687816

Cited by 26 publications

(9 citation statements)

References 21 publications

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“…The electromechanical model of the AGV is described by Equations (1–12). These equations are explained in more detail in (Sierra‐García & Santos, 2020a).…”

Section: Mathematical Model Of the Agvmentioning

confidence: 99%

Combining reinforcement learning and conventional control to improve automatic guided vehicles tracking of complex trajectories

Sierra-García

Santos

2022

Expert Systems

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“…The electromechanical model of the AGV is described by Equations (1–12). These equations are explained in more detail in (Sierra‐García & Santos, 2020a).…”

Section: Mathematical Model Of the Agvmentioning

confidence: 99%

Combining reinforcement learning and conventional control to improve automatic guided vehicles tracking of complex trajectories

Sierra-García

Santos

2022

Expert Systems

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“…The kinematic model of the AGV is described by Equations (1)-( 3). These equations are explained in more detail in [33,34].…”

Section: Model Of the Systemmentioning

confidence: 99%

Development and Experimental Validation of Control Algorithm for Person-Following Autonomous Robots

et al. 2023

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Automatic guided vehicles, in particular, and industrial autonomous mobile robots, in general, are commonly used to automate intralogistics processes. However, there are certain logistic tasks, such as picking objects of variable sizes, shapes, and physical characteristics, that are very difficult to handle fully automatically. In these cases, the collaboration between humans and autonomous robots has been proven key for the efficiency of industrial processes and other applications. To this aim, it is necessary to develop person-following robot solutions. In this work, we propose a fully autonomously controlling autonomous robotic interaction for environments with unknown objects based on real experiments. To do so, we have developed an active tracking system and a control algorithm to implement the person-following strategy on a real industrial automatic-guided vehicle. The algorithm analyzes the cloud of points measured by light detection and ranging (LIDAR) sensor to detect and track the target. From this scan, it estimates the speed of the target to obtain the speed reference value and calculates the direction of the reference by a pure-pursuit algorithm. In addition, to enhance the robustness of the solution, spatial and temporal filters have been implemented to discard obstacles and detect crossings between humans and the automatic industrial vehicle. Static and dynamic test campaigns have been carried out to experimentally validate this approach with the real industrial autonomous-guided vehicle and a safety LIDAR.

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“…Tey provide more fexibility than other types of transportation for logistics and production applications. Tey are safer and more efcient than manned industrial vehicles, and they improve the quality of the processes where they are implemented [1]. Te digitalization of processes and the industry 4.0 paradigm have driven its deployment in the industrial sector [2].…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking Nonlinear Hybrid Control of Automated Guided Vehicles

Sánchez-Rodríguez,

Bayona,

Sierra-García

et al. 2024

Complexity

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Automated guided vehicles (AGVs), so necessary in industrial environments, require precise control of trajectory tracking to make accurate stops at logistics stations, such as loading stations, or to pick up or drop off trolleys, pallets, or racks. This paper proposes a hybrid control architecture for trajectory tracking of a hybrid tricycle-differential AGV. The control strategy combines conventional proportional integral derivative (PID) control with advanced nonlinear Lyapunov control (LPC). The LPC is used for trajectory tracking while the PID is used for speed control of the robot. The stability of the controller is demonstrated for any differentiable trajectory. When a PID optimized with genetic algorithms is compared with the proposed controller for several trajectories, the LPC outperforms it in all cases.

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Mechatronic Modelling of Industrial AGVs: A Complex System Architecture

Cited by 26 publications

References 21 publications

Combining reinforcement learning and conventional control to improve automatic guided vehicles tracking of complex trajectories

Combining reinforcement learning and conventional control to improve automatic guided vehicles tracking of complex trajectories

Development and Experimental Validation of Control Algorithm for Person-Following Autonomous Robots

Trajectory Tracking Nonlinear Hybrid Control of Automated Guided Vehicles

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