Performance Analysis of a Light Weight Ground Robotic Vehicle by Implementing Adaptive Neuro-Fuzzy Inference System (ANFIS)

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Big Data Pub Date : 2023-08-03 DOI:10.1109/icABCD59051.2023.10220494
M. Okwu, I. Emovon, O. J. Oyejide, Kingsley C. Ezekiel, Olaye Messiah, Perpetua C. Jones-Iwuagwu
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Abstract

Automated Guided Vehicles (AGVs) are widely used as delivery agents and for material transportation in factories, hospital environment, and other facilities. Conducting performance tests on AGVs has the potential to ratify and improve the efficiency, and reliability of the system. However, published studies on performance analysis focused on classical metrics for such evaluation. In this study, the emphasis is on the performance evaluation of a developed lightweight AGV using the Adaptive Neuro-fuzzy Inference System (ANFIS). The developed line following AGV is flexible, intelligent, and nifty, and can be accessed wirelessly, and controlled by an operator. It was programmed to avoid collision with the help of a proximity sensor attached. The performance test was conducted by drawing black lines on a plain surface for easy navigation of the AGV. A series of experiments was carried out by using realistic test variables like the navigation pattern of AGV, test accuracy, energy efficiency, obstacle avoidance, task accomplishment, and others. Sensitivity analysis was done using the ANFIS surface plot. The total system intelligence (TSI) obtained for the different trials are 76%; 79%; 80%; 81%; 79% and 81 %, for the first, second, third, fourth, fifth, and final trials respectively. The preeminent observable performance was the fourth and sixth trials, obtained at 81 %. The outcome of the investigation reveals that the ANFIS model is an efficient soft computing technique capable of performing TSI tests of AGVs with a high degree of accuracy. The model is also recommended in AGV platooning.
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基于自适应神经模糊推理系统(ANFIS)的轻型地面机器人车辆性能分析
自动导引车(agv)被广泛应用于工厂、医院和其他设施的递送代理和物料运输。在agv上进行性能测试有可能验证和提高系统的效率和可靠性。然而,已发表的关于绩效分析的研究主要集中在此类评估的经典指标上。在本研究中,重点研究了一种基于自适应神经模糊推理系统(ANFIS)的轻型AGV的性能评估。开发的线路跟踪AGV灵活、智能、美观,可以无线接入,由操作员控制。它被编程为在附加的接近传感器的帮助下避免碰撞。为了便于AGV导航,在平面上绘制黑线进行性能测试。采用AGV导航模式、测试精度、能效、避障、任务完成等现实测试变量进行了一系列实验。采用ANFIS地形图进行敏感性分析。不同试验获得的总系统智能(TSI)为76%;79%;80%;81%;分别为第一、第二、第三、第四、第五和最后一次试验的79%和81%。最显著的观察表现是在第四和第六次试验中,达到81%。研究结果表明,ANFIS模型是一种高效的软计算技术,能够对agv进行高精度的TSI测试。该模型也适用于AGV队列调度。
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来源期刊
Big Data
Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍: Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.
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