Simulation of a trajectory control for an automated guided tricycle type vehicle with front wheel drive
Keywords:
Trajectories generation based on vectors, Trajectory control, Automated guided vehicles (AGV’s), Proportional-Integral- Derivative (PID) control, Control systemsAbstract
The first stage is presented of a new methodology of
trajectories generation based on vectors for Automated Guided
Vehicles (AGV's). The aim is to minimize the use of various
sensors and reduce computational processing. It is possible to
recreate operative environments trajectories by vector designs.
Trajectories were created with N vectors, with different magnitudes
and angles, by using Matlab. The engine transfer function
that governs the direction of the AGV was calculated and a
Proportional-Integral-Derivative (PID) controller was implemented
for the engine to execute the trajectories. Each vector represents
a reference (setpoint) that the control must achieve.
With the PID control, the AGV effectively followed the generated
trajectories giving certainty of any generated trajectory with
this method can be followed. The PID control allows to implement
more sophisticated control algorithms and evaluate more
complex conditions.
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References
Ferguson, D., & Stentz, A. (2006). Using interpolation to improve path planning: The Field D* algorithm. Journal of Field Robotics, 79-101.
Fernández, J. M. (2019). Control de servomotores de baja tensión mediante la implementación de control adaptativo. Santiago de Querétaro: CIDESI.
Heinemann, T., Riedel, O., & Lechler, A. (2019). Generating Smooth Trajectories in Local Path Planning for Automated Guided Vehicles in Production. Procedia Manufacturing, 98-105.
Mercy, T., Pipeleers, G., & Parys, R. (2017). Spline-based motion planning for autonomous guided vehicles in a dynamic environment. IEEE Transactions on Control Systems Technology, 2182-2189.
Nise, N. (2020). Control systems engenering. John Wiley & Sons.
Qing, G., Zheng, Z., & Yue, X. (2017). Path-planning of Automated Guided Vehicle based on Improved Dijkstra. 2017 29th Chinese control and decision conference (CCDC), 7138-7143.
Wu, X., Sun, C., Zou, T., Li, L., Wang, L., & Liu, H. (2020). SVM-based image partitioning for vision recognition of AGV guide paths under complex illumination conditions. Robotics and Computer-Integrated Manufacturing, 101856.
Zheng, T., Xu, Y., & Zheng, D. (2019). AGV Path Planning based on Improved A-star. 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), 1534-1538.
