Modelado de un AGV híbrido triciclo-diferencial

Sánchez, Roberto Tapia; Sierra-García, J. Enrique; Santos, Matilde

doi:10.4995/riai.2021.14622

Cited by 15 publications

(7 citation statements)

References 24 publications

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“…The main contributions of this work can be summarized as follows: Proposal of a hybrid control architecture that combines RL and PI regulators for tracking and speed control of AGVs Validation of the proposal in simulation using the mathematical model of a hybrid AGV (Sánchez et al, 2022). This vehicle, quite common in industry, has a complex dynamics because it is made up of a differential mobile robot and a tricycle one.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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 proposed solution attempts to predict in advance the position of a remote controlled AGV that is connected to a virtualized programmable logic controller (PLC) via a 5G network. This specific AGV is a magnetic-guided hybrid differential-tricycle manufactured by ABB with a unique kinematic configuration in which the traction unit operates as a differential robot, and the body follows the movement of a tricycle mobile robot [ 22 ].…”

Section: Proposed Deep-learning Architecturesmentioning

confidence: 99%

Transformers for Multi-Horizon Forecasting in an Industry 4.0 Use Case

Vakaruk

Karamchandani

Sierra-García

et al. 2023

Sensors

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Recently, a novel approach in the field of Industry 4.0 factory operations was proposed for a new generation of automated guided vehicles (AGVs) that are connected to a virtualized programmable logic controller (PLC) via a 5G multi-access edge-computing (MEC) platform to enable remote control. However, this approach faces a critical challenge as the 5G network may encounter communication disruptions that can lead to AGV deviations and, with this, potential safety risks and workplace issues. To mitigate this problem, several works have proposed the use of fixed-horizon forecasting techniques based on deep-learning models that can anticipate AGV trajectory deviations and take corrective maneuvers accordingly. However, these methods have limited prediction flexibility for the AGV operator and are not robust against network instability. To address this limitation, this study proposes a novel approach based on multi-horizon forecasting techniques to predict the deviation of remotely controlled AGVs. As its primary contribution, the work presents two new versions of the state-of-the-art transformer architecture that are well-suited to the multi-horizon prediction problem. We conduct a comprehensive comparison between the proposed models and traditional deep-learning models, such as the long short-term memory (LSTM) neural network, to evaluate the performance and capabilities of the proposed models in relation to traditional deep-learning architectures. The results indicate that (i) the transformer-based models outperform LSTM in both multi-horizon and fixed-horizon scenarios, (ii) the prediction accuracy at a specific time-step of the best multi-horizon forecasting model is very close to that obtained by the best fixed-horizon forecasting model at the same step, (iii) models that use a time-sequence structure in their inputs tend to perform better in multi-horizon scenarios compared to their fixed horizon counterparts and other multi-horizon models that do not consider a time topology in their inputs, and (iv) our experiments showed that the proposed models can perform inference within the required time constraints for real-time decision making.

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“…Howeve the AGV used in the experiments, there is an embedded controller for the wheel sp and a control interface such that the external references are the longitudinal velocity and the direction, 𝛾 𝑟𝑒𝑓 , as shown in Figure 1. These external references (𝑣 𝑟𝑒𝑓 , 𝛾 𝑟𝑒𝑓 ) internally transformed into wheel speed references by ( 4)- (6). Normally, in these types of AGV, the control references are v L and v R .…”

Section: Model Of the Systemmentioning

confidence: 99%

“…The approach can be adapted for any type of AGV or any autonomous robot, no matter the kinematics. In this paper, the control proposal has been experimentally validated on a real hybrid differential tricycle AGV [6] of the ASTI Mobile Robotics company.…”

Section: Introductionmentioning

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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Modelado de un AGV híbrido triciclo-diferencial

Cited by 15 publications

References 24 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

Transformers for Multi-Horizon Forecasting in an Industry 4.0 Use Case

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

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