The forecast of the AGV battery discharging via the machine learning methods

Pavliuk, Olena; Stęclik, Tomasz; Biernacki, Piotr

doi:10.1109/bigdata55660.2022.10020968

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…The studies reviewed cover a wide range of topics, including the optimal design of energy sources for photovoltaic/fuel cell extended-range agricultural mobile robots (Ghobadpour et al , 2023), forecasting AMR battery discharging using ML methods (Pavliuk et al , 2022) and reviewing lithium-ion batteries for autonomous mobile robots (Partovibakhsh and Liu, 2015). Other studies explore applying AI techniques, such as reinforcement learning for energy-constrained coverage with mobile robots (Lee and Jae Jang, 2022).…”

Section: Energy Optimization For Autonomous Mobile Robotsmentioning

confidence: 99%

A review on energy efficiency in autonomous mobile robots

Wu,

Yeong,

et al. 2023

RIA

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Purpose This paper aims to provide a comprehensive analysis of the state of the art in energy efficiency for autonomous mobile robots (AMRs), focusing on energy sources, consumption models, energy-efficient locomotion, hardware energy consumption, optimization in path planning and scheduling methods, and to suggest future research directions. Design/methodology/approach The systematic literature review (SLR) identified 244 papers for analysis. Research articles published from 2010 onwards were searched in databases including Google Scholar, ScienceDirect and Scopus using keywords and search criteria related to energy and power management in various robotic systems. Findings The review highlights the following key findings: batteries are the primary energy source for AMRs, with advances in battery management systems enhancing efficiency; hybrid models offer superior accuracy and robustness; locomotion contributes over 50% of a mobile robot’s total energy consumption, emphasizing the need for optimized control methods; factors such as the center of mass impact AMR energy consumption; path planning algorithms and scheduling methods are essential for energy optimization, with algorithm choice depending on specific requirements and constraints. Research limitations/implications The review concentrates on wheeled robots, excluding walking ones. Future work should improve consumption models, explore optimization methods, examine artificial intelligence/machine learning roles and assess energy efficiency trade-offs. Originality/value This paper provides a comprehensive analysis of energy efficiency in AMRs, highlighting the key findings from the SLR and suggests future research directions for further advancements in this field.

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Section: Energy Optimization For Autonomous Mobile Robotsmentioning

confidence: 99%

A review on energy efficiency in autonomous mobile robots

Wu,

Yeong,

et al. 2023

RIA

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“…The ANN was tested using control driving cycles. The authors of [55] developed a system for the short-term prediction of battery discharge using ML methods and data preprocessing. The proposed system was tested in real production conditions with an average absolute error of less than 1%.…”

Section: State-of-the-artmentioning

confidence: 99%

“…Publications [17,20,55] provide a detailed description of the developed OPC UA ML server, which provides access to the data that are generated, processed, and used by ANNs for ML algorithms. Due to the immutability of the data descriptor and the way in which the information is presented, the server ensures a stable connection if the internal data structure of the ANN changes.…”

Section: Collecting the Agv Datamentioning

confidence: 99%

A Novel Methodology Based on a Deep Neural Network and Data Mining for Predicting the Segmental Voltage Drop in Automated Guided Vehicle Battery Cells

Pavliuk,

Cupek,

Steclik

et al. 2023

Electronics

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AGVs are important elements of the Industry 4.0 automation process. The optimization of logistics transport in production environments depends on the economical use of battery power. In this study, we propose a novel deep neural network-based method and data mining for predicting segmented AGV battery voltage drop. The experiments were performed using data from the Formica 1 AGV of AIUT Ltd., Gliwice, Poland. The data were converted to a one-second resolution according to the OPCUA open standard. Pre-processing involved using an analysis of variance to detect any missing data. To do this, the standard deviation, variance, minimum and maximum values, range, linear deviation, and standard deviation were calculated for all of the permitted sigma values in one percent increments. Data with a sigma exceeding 1.5 were considered missing and replaced with a smoothed moving average. The correlation dependencies between the predicted signals were determined using the Pearson, Spearman, and Kendall correlation coefficients. Training, validation, and test sets were prepared by calculating additional parameters for each segment, including the count number, duration, delta voltage, quality, and initial segment voltage, which were classified into static and dynamic categories. The experiments were performed on the hidden layer using different numbers of neurons in order to select the best architecture. The length of the “time window” was also determined experimentally and was 12. The MAPE of the short-term forecast of seven segments and the medium-term forecast of nine segments were 0.09% and 0.18%, respectively. Each study duration was up to 1.96 min.

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