Investigating the Impact of Energy Source Level on the Self-Guided Vehicle System Performances, in the Industry 4.0 Context

Abstract

Automated industrial vehicles are taking an imposing place by transforming the industrial operations, and contributing to an efficient in-house transportation of goods. They are expected to bring a variety of benefits towards the Industry 4.0 transition. However, Self-Guided Vehicles (SGVs) are battery-powered, unmanned autonomous vehicles. While the operating durability depends on self-path design, planning energy-efficient paths become crucial. Thus, this paper has no concrete contribution but highlights the lack of energy consideration of SGV-system design in literature by presenting a review of energy-constrained global path planning. Then, an experimental investigation explores the long-term effect of battery level on navigation performance of a single vehicle. This experiment was conducted for several hours, a deviation between the global trajectory and the ground-true path executed by the SGV was observed as the battery depleted. The results show that the mean square error (MSE) increases significantly as the battery’s state-of-charge decreases below a certain value.

Criterios de desempeño para evaluar algoritmos de navegación de robots móviles: una revisión

“…Este indicador ha sido utilizado en (Graba et al, 2020) debido a su amplia aceptación como una buena métrica de desempeño cuando se comparan series de tiempo.…”

“…Criterio: índice, métrica, medición, procedimiento Referencia % éxito en alcanzar la meta (Gridnev et al, 2017) (Graba et al, 2020) Integral del valor absoluto del error -integral absolute error (IAE) (Fernandes et al, 2017); (Caruntu et al, 2019); (Suarin et al, 2019); (Rivera et al, 2020); (Farias et al, 2020) Integral del valor absoluto del error ponderado en el tiempo -Integral time-weighted absolute error (ITAE) (Fernandes et al, 2017); (Suarin et al, 2019); (Farias et al, 2020) Integral del valor absoluto de la señal de control -Integral absolute signal control (IASC) (Fernandes et al, 2017) Integral del cuadrado del error -integral square error (ISE) (Perez et al, 2018); (Suarin et al, 2019); (Farias et al, 2020) integral del cuadrado del error por el tiempo -Integral time square error (ITSE) (Farias et al, 2020) Esfuerzo de control (Rivera et al, 2020) Seguridad (Marvel and Bostelman, 2014); (Munoz et al, 2014) Energía (Munoz et al, 2014); (Fernandes et al, 2017); (Stefek et al, 2020); (Graba et al, 2020); (Wei and Isler, 2020); (Hrubý et al, 2021)…”

“…Se han realizado diferentes estudios basados en el modelo energético del robot y pruebas experimentales que permite examinar el efecto a largo plazo del nivel de carga de las baterías en el desempeño de la navegación. Pruebas reales presentados por (Graba et al, 2020) muestran que el error medio cuadrático MSE (9) en el seguimiento de trayectoria aumenta significativamente a medida que disminuye el estado de carga de la batería por debajo del 30%. Como el error es acumulativo, la desviación respecto a la trayectoria de referencia se hace mayor mientras la trayectoria sea más larga.…”

An Investigation into the Energy-Efficient Motion of Autonomous Wheeled Mobile Robots

“…The most widely reported path search methods for global planning are A*, D*, and artificial intelligence algorithms [17]. Designing a global path in consideration of the energy source level is important [21]. The modified Newton algorithm was used by Duleba and Sasiadek [22] for nonholonomic energy-efficient path planning.…”

“…In addition, a Self-Guided Vehicle (SGV) was selected to perform the simulations and experiments. Depending on the autonomy level in industrial robots, SGV is an improved model of an autonomous guided vehicle (AGV) because it has the ability to respond to changes in the mission's environment such as unforeseen obstacles [21,66].…”