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Modeling and Prototype Implementation of an Automated Guided Vehicle for Smart Factories
Abstract
This paper overviews the modeling, implementation, and mechanical characterization of an Automated Guided Vehicle (AGV) devoted to transport materials and tools in smart factories. The design is based on a differential-drive mobile robot configuration for which the mathematical model was obtained. Such model allows the simulation of the AGV's motion and can be used to improve productivity, increase automation, and reduce transportation costs in industrial facilities. The prototype's implementation was conducted with highly resistant materials and automated manufacturing processes. A finite element analysis (FEA) shows that the prototype is capable of transporting 700 kg loads without suffering any structural damage and that critical cases involving collisions might cause only minimal harm. An IoT approach has been considered for the AGV's electronic architecture.
... [101], [103], [106], [107], [111], [116], [119], [122], [125] Arduino 16 [98], [99], [101]- [104], [108]- [110], [113], [117], [121], [124], [126], [130], [132] Others 6 [112], [114], [118], [123], [131], [133] C. TRO3: What are the prospective software architectures and features for future SDVs? 1) Software Architecture: Figure 3 illustrates the development of a generic software architecture for SDVs after reviewing all selected papers. It's worth noting that not every SDV model included all the functions and sensors depicted in this generic architecture; however, each model incorporated at least one feature or sensor. ...
... While OpenCV has machine learning functionalities, developers often prefer to build models in TensorFlow and then import them into OpenCV for application in SDVs. [4], [17], [67], [69], [71]- [73], [75]- [77], [79], [80], [82]- [86], [88], [91], [92], [101], [105], [111], [115]- [119], [123], [125] Camera 48 [3], [4], [17], [18], [68]- [73], [75]- [77], [82]- [86], [88], [91]- [93], [100]- [108], [111], [112], [115], [116], [118]- [122], [125], [127]- [133] Radar 14 [4], [68], [69], [72]- [77], [82], [83], [92], [93] Ultrasonic 21 [68], [73]- [75], [77], [82]- [84], [88], [98]- [100], [102], [105], [109]- [111], [126]- [128], [133] GNSS/RTK 27 [3], [4], [17], [18], [67], [68], [71]- [73], [75]- [77], [79], [80], [82]- [84], [86], [88], [93], [99], [102], [105], [117], [120] INS/IMU 26 [4], [17], [18], [69], [71]- [73], [75]- [77], [80], [82]- [84], [86], [88], [93], [105], [115], [116], [118]- [120], [123], [125] D. TRO4: Which sensor configurations are essential for achieving full functionality in an SDV? Figure 3 illustrates the six essential sensors required for constructing a SDV. While not all SDV models in the reviewed papers utilized every sensor, some full-size models incorporated the complete sensor suite [73], [75], [82], [83], [88]- [90]. ...
... However, the high cost of LiDAR, up to $75,000, is a barrier to widespread SDV adoption [12]. Researchers are exploring alternatives, such as using only cameras to perform SLAM tasks [3], [4], [17], [18], [68]- [73], [75]- [77], [82]- [86], [88], [91]- [93], [100]- [108], [111], [112], [115], [116], [118]- [122], [125], [127]- [133]. Developing alternative sensors or algorithms for image processing and machine learning to use camera data for SLAM is a crucial research area. ...
Self-driving vehicles (SDVs), also known as autonomous vehicles (AVs), are poised to revolutionize transportation by operating independently through the integration of machine learning algorithms, advanced processing units, and sensor networks. Many organizations around the world are developing their own SDV models, and for this purpose, this paper aims to identify emerging trends and patterns in SDV development by conducting a systematic scoping review (SSR). The research involved the selection of 85 relevant papers from an initial pool of 551 entries across multiple academic databases, using well-defined inclusion and exclusion criteria along with snowballing techniques. The results highlight the critical technical specifications necessary for both full-scale and miniature SDV models, emphasizing key software and hardware architectures, essential sensors, and their primary suppliers. Additionally, the analysis examines publication trends, including publisher and venue distribution, authors' affiliations, and the most active countries in SDV research. This work can guide researchers in designing their SDV models, identifying key challenges, and exploring opportunities that are expected to influence future research and development in autonomous vehicle technology.
... Similarly, Perciński, Marcinkiewicz, and Blaga et al. developed their models for the Carolo-Cup, another competition for 1:10 scale SDVs (Perciński & Marcinkiewicz, 2018;Blaga, Deac, Al-doori, Negru, & Dǎnescu, 2018;Braunschweig, 2020). (Sahu, Sahu, Choudhury, & Nag, 2019), (Wang, Liu, Zhang, & Shi, 2019), (Roestam & Hadisukmana, 2019), (Febbo, Flood, Halloy, & Others, 2020), (Fathy, Ashraf, Ismail, & Others, 2020), (El-Hassan, 2020), (Ullah, Asghar, Griffiths, & Others, 2022), (Ikhlayel, Iswara, Kurniawan, & Others, 2020), (Albin & Simske, 2021), (Mohammed, Abdullahi, & Ibrahim, 2021), (Hartono, Nizar, Robani, & Jatmiko, 2020), (Tabor, Dai, Sreenivasan, & Banerjee, 2022), (Sasamoto, Velázquez, Gutiérrez, & Others, 2021), (Hubert, Valdiero, Goergen, & Others, 2021), (Ponnan, Shelly, Hussain, & Others, 2022), (Ilié, Chaouche, & Pêcheux, 2020) RC vehicles 1:10 15 (Karaman, Anders, Boulet, & Others, 2017), (Aguilar-Gonzalez, Lozoya, Orona, & Others, 2017), (Perciński & Marcinkiewicz, 2018), (Fayjie, Hossain, Oualid, & Lee, 2018), (Blaga, Deac, Al-doori, Negru, & Dǎnescu, 2018), (Do, Duong, Dang, & Le, 2018), (Kwon, Seo, Lee, & Kim, 2018), (Gotlib, Łukojć, & Szczygielski, 2019), (Walambe, Nikte, Joshi, & Others, 2019), (Zhou, Li, & Cao, 2019), (Valocky, Orgon, & Fujdiak, 2019), (Sajjad, Irfan, Muhammad, & Others, 2021), (Pannu, Ansari, & Gupta, 2015), (Bechtel, Mcellhiney, Kim, & Yun, 2018), (Jain, 2018) Others 5 (Pannu, Ansari, & Gupta, 2015), (Bechtel, Mcellhiney, Kim, & Yun, 2018), (Jain, 2018), (Sun, Zheng, Qiao, Liu, Lin, & Bräunl, 2019), (Chaitra, Deepthi, Gautami, Suraj, & Kumar, 2020) Vol. 17, No. 1 2024 African Journal of Advances in Sciences and Technology Research www.afropolitanjournals.com ...
... Table 7 and 8 details the specific number of computers and hardware selection for mini SDV Models. (Sasamoto, Velázquez, Gutiérrez, & Others, 2021), (Hubert, Valdiero, Goergen, & Others, 2021), (Karaman, Anders, Boulet, & Others, 2017), (Fayjie, Hossain, Oualid, & Lee, 2018), (Blaga, Deac, Al-doori, Negru, & Dǎnescu, 2018), (Kwon, Seo, Lee, & Kim, 2018), (Zhou, Li, & Cao, 2019), (Sajjad, Irfan, Muhammad, & Others, 2021), (Pannu, Ansari, & Gupta, 2015) Two 18 (Al Suwaidi, AlHammadi, Buhumaid, & Others, 2018), (Wang, Liu, Zhang, & Shi, 2019), (Febbo, Flood, Halloy, & Others, 2020), (Fathy, Ashraf, Ismail, & Others, 2020), (Mohammed, Abdullahi, & Ibrahim, 2021), (Hartono, Nizar, Robani, & Jatmiko, 2020), (Ilié, Chaouche, & Pêcheux, 2020), (Aguilar-Gonzalez, Lozoya, Orona, & Others, 2017), (Perciński & Marcinkiewicz, 2018), (Do, Duong, Dang, & Le, 2018), (Gotlib, Łukojć, & Szczygielski, 2019), (Walambe, Nikte, Joshi, & Others, 2019), (Valocky, Orgon, & Fujdiak, 2019), (Bechtel, Mcellhiney, Kim, & Yun, 2018), (Jain, 2018), (Sun, Zheng, Qiao, Liu, Lin, & Bräunl, 2019), (Chaitra, Deepthi, Gautami, Suraj, & Kumar, 2020), (Pehlivan, Kahraman, Kurtel, Nakp, & Güzeliş, 2020) Not specified 1 (Ponnan, Shelly, Hussain, & others, 2022) 20 www.afropolitanjournals.com ...
... This approach was necessary because the Raspberry Pi 3, which is commonly used in these models, was insufficient for efficiently training neural networks (Do, Duong, Dang, & Le, 2018;Bechtel, Mcellhiney, Kim, & Yun, 2018;Pehlivan, Kahraman, Kurtel, Nakp, & Güzeliş, 2020). (Sajjad, Irfan, Muhammad, & Others, 2021), (Pannu, Ansari, & Gupta, 2015), (Bechtel, Mcellhiney, Kim, & Yun, 2018), (Jain, 2018), (Sun, Zheng, Qiao, Liu, Lin, & Bräunl, 2019), (Chaitra, Deepthi, Gautami, Suraj, & Kumar, 2020), (Pehlivan, Kahraman, Kurtel, Nakp, & Güzeliş, 2020) Nvidia Jetson Series 9 (Wang, Liu, Zhang, & Shi, 2019), (Febbo, Flood, Halloy, & Others, 2020), (Ullah, Asghar, Griffiths, & Others, 2022), (Ikhlayel, Iswara, Kurniawan, & Others, 2020), (Tabor, Dai, Sreenivasan, & Banerjee, 2022), (Karaman, Anders, Boulet, & Others, 2017), (Fayjie, Hossain, Oualid, & Lee, 2018), (Kwon, Seo, Lee, & Kim, 2018), (Zhou, Li, & Cao, 2019) Arduino 16 (Azid, Kumar, Lal, & Sharma, 2017), (Al Suwaidi, AlHammadi, Buhumaid, & Others, 2018), (Wang, Liu, Zhang, & Shi, 2019), (Febbo, Flood, Halloy, & Others, 2020), (Fathy, Ashraf, Ismail, & Others, 2020) (Valocky, Orgon, & Fujdiak, 2019), (Jain, 2018), (Chaitra, Deepthi, Gautami, Suraj, & Kumar, 2020) Others 6 (Sasamoto, Velázquez, Gutiérrez, & Others, 2021), (Ponnan, Shelly, Hussain, & Others, 2022), (Perciński & Marcinkiewicz, 2018), (Gotlib, Łukojć, & Szczygielski, 2019), (Sun, Zheng, Qiao, Liu, Lin, & Bräunl, 2019), (Pehlivan, Kahraman, Kurtel, Nakp, & Güzeliş, 2020) Vol. 17, No. 1 2024 African Journal of Advances in Sciences and Technology Research www.afropolitanjournals.com TRO3: What are the prospective software architectures and features for future SDVs? 1) Software Architecture: Figure 3 illustrates the development of a generic software architecture for SDVs after reviewing all selected papers. ...
Self-driving vehicles (SDVs), also known as autonomous vehicles (AVs), are anticipated to revolutionize transportation by operating independently through the integration of machine learning algorithms, advanced processing units, and sensor networks. Numerous organizations globally are actively developing SDV models, prompting this paper’s objective to identify emerging trends and patterns in SDV development through a comprehensive systematic scoping review (SSR). This research involved selecting 85 relevant studies from an initial set of 551 records across multiple academic databases, utilizing well-defined inclusion and exclusion criteria along with snowballing techniques to ensure a thorough analysis. The findings emphasize critical technical specifications required for both full-scale and miniature SDV models, focusing on key software and hardware architectures, essential sensors, and primary suppliers. Additionally, the analysis explores publication trends, including publisher and venue distribution, authors’ affiliations, and the most active countries in SDV research. This work aims to guide researchers in designing their SDV models by identifying key challenges and exploring opportunities likely to shape future research and development in autonomous vehicle technology.
... [101], [103], [106], [107], [111], [116], [119], [122], [125] Arduino 16 [98], [99], [101]- [104], [108]- [110], [113], [117], [121], [124], [126], [130], [132] Others 6 [112], [114], [118], [123], [131], [133] the Raspberry Pi or Arduino as a computing unit [98], [100], [102], [105], [120], [127], [128] would have fewer functions if compared with the prototypes implemented with the more powerful Nvidia Jetson series computers [116], [119], [122], [125]. However, the first choice made the whole prototype less expensive. ...
... Total Papers LiDAR 31 [3], [4], [17], [67], [69], [71]- [73], [75]- [77], [79], [80], [82]- [86], [88], [91], [92], [101], [105], [111], [115]- [119], [123], [125] Camera 48 [3], [4], [17], [18], [68]- [73], [75]- [77], [82]- [86], [88], [91]- [93], [100]- [108], [111], [112], [115], [116], [118]- [122], [125], [127]- [133] Radar 14 [4], [68], [69], [72]- [77], [82], [83], [92], [93] Ultrasonic 21 [68], [73]- [75], [77], [82]- [84], [88], [98]- [100], [102], [105], [109]- [111], [126]- [128], [133] GNSS/RTK 27 [3], [4], [17], [18], [67]- [69], [71]- [77], [79], [80], [82]- [84], [86], [88], [93], [99], [102], [105], [117], [120] INS/IMU 26 [4], [17], [18], [69], [71]- [73], [75]- [77], [80]- [84], [86], [88], [93], [105], [115], [116], [118]- [120], [123], [125] ...
... However, that sensor is the barrier to popularizing the ADV due to its high cost, which can get up to 75,000 dollars [12]. That makes prototype developers search for alternatives for that sensor, like using only cameras [81], also used in various surveys [3], [4], [17], [18], [68]- [73], [75]- [77], [82]- [86], [88], [91]- [93], [100]- [108], [111], [112], [115], [116], [118]- [122], [125], [127]- [133]. Thus, given this scenario, research needs to explore sensor alternatives to replace LiDAR or alternative algorithms for image processing and machine learning that aim to use the images provided by cameras and other sensors to perform the SLAM of the environment. ...
Autonomous driving vehicles (ADVs) are the next significant evolution in transportation systems. Using machine learning techniques supported by strong processing units and sensors, the driver would be removed and the vehicle would drive itself, bringing countless advantages to the commuting system. There are too many players developing their own ADV prototypes all over the world. For this reason, this paper aims to use a systematic mapping study (SMS) to identify trends and patterns in ADV development. From a set of academic databases, a total of 68 papers were selected from a total of 548 after applying inclusion and exclusion criteria and snowballing techniques. The results showed the technical characteristics of the platform required to develop real-size and mini ADV prototypes. In addition, the paper explains the detailed software and hardware architectures and their main aspects and requirements. Finally, the paper discusses the necessary set of sensors which are required and their characteristics and main suppliers. It was possible to map the publications distribution based on venues, year, authors' affiliation, and most prolific countries. This work can guide researchers to design their ADV prototype or identify development bottlenecks that could lead to narrow research and innovation possibilities in autonomous driving vehicle development.
... Jodejko-Pietruczuk and Werbińska-Wojciechowska address the reliability and maintenance issues of a multi-AGV system developing a Monte Carlo simulation model [28] The work by Sasamoto et al. overviews the modeling, implementation, and mechanical characterization of a differential-drive mobile robot configuration for which the mathematical model was obtained [29]. Such a model allows the simulation of the AGV's motion and can be used to improve productivity, increase automation, and reduce transportation costs in industrial facilities. ...
Automatic guidance vehicles (AGV) are industrial vehicles that play an important role in the development of smart manufacturing systems and Industry 4.0. To provide these autonomous systems with the flexibility that is required today in these industrial workspaces, AGV computational models are necessary in order to analyze their performance and design efficient planning and control strategies. To address these issues, in this work, the mathematical model and the algorithm that implement a computational control-oriented simulation model of a hybrid tricycle-differential AGV with multi-trailers have been developed. Physical factors, such as wheel-ground interaction and the effect of vertical and lateral loads on its dynamics, have been incorporated into the model. The model has been tested in simulation with two different controllers and three trajectories: a circumference, a square, and an s-shaped curve. Furthermore, it has been used to analyze extreme situations of slipping and capsizing and the influence of the number of trailers on the tracking error and the control effort. This way, the minimum lateral friction coefficient to avoid slipping and the minimum ratio between the lateral and height displacement of the center of gravity to avoid capsizing have been obtained. In addition, the effect of a change in the friction coefficient has also been simulated.
A Indústria 4.0 traz uma transformação nos processos industriais com tecnologias avançadas, como Internet das Coisas e Inteligência Artificial, que impulsionam a automação e a eficiência em operações de manufatura. Este trabalho apresenta uma solução baseada em Veículo Guiado Automatizado para aprimorar o transporte de materiais em ambientes industriais, minimizando a necessidade de intervenção humana e aumentando a segurança. O veículo proposto integra sensores de navegação e sistemas de identificação por tags, proporcionando precisão e autonomia na movimentação de cargas. Além do protótipo funcional do veículo, um aplicativo web será implementado para monitoramento e controle. A proposta será validada em um ambiente industrial simulado, avaliando sua eficácia e aplicabilidade operacional.
Industrial mobile robots are one of the most common handling equipment in manufacturing. It is a way of increasing production efficiency while significantly reducing operating coast and facilitating because of the inflexibility of the Automated Guided Vehicle (AGV) with regard to modifying the robot's route. Because the AGV commonly uses magnetic tape embedded under concrete to create a path for the robot. When changing the route or adding the production line, need to destroy the area to reengineer causing an increase in spending. Therefore, Autonomous mobile robots respond to the problem involved with Simultaneous Localization and Mapping (SLAM) techniques, it can be used to located and create Maps. And AMR moves according to maps and localization, it causes the main features is the flexibility to change routes. In this research will present various systems and usability of AMR for material handling in an industry.
Mobile robots and rovers play an important role in many industrial applications. Under certain constraints, they are suitable in harsh environments and conditions in which protracted human activity is not safe or permitted. In many circumstances, mechanical aspects and electrical consumption need to be optimized for autonomous and wheeled mobile robots. The paper illustrates the design of a semi-custom wheeled mobile robot with high-efficiency mono- or polycrystalline photovoltaic panel on the roof that supports the lithium ion batteries during particular tasks (e.g. navigating rough terrain, obstacles or steep paths) to extend the robot’s autonomy. An electronic controller was designed, and data acquisition related to power consumption performed using a specific experimental setup. The robot can detect parameters such as temperature, humidity, concentrations of toxic gas species and the presence of flames, making it particularly suitable for contaminated environments or industrial plants. For this aim, the mobile robot was equipped with a wide range of commercial sensors and a Global Positioning System receiver to track its position. In addition, using an HC-06 Bluetooth transceiver, the robot receives commands and instructions, and sends the acquired data to the developed IoTool smartphone application, where they are displayed to be analysed by user.
In this study, a Wireless Sensor Network (WSN) energy model is proposed by defining the energy consumption at each node. Such a model calculates the energy at each node by estimating the energy of the main functions developed at sensing and transmitting data when running the routing protocol. These functions are related to wireless communications and measured and compared to the most relevant impact on an energy standpoint and performance metrics. The energy model is validated using a Texas Instruments CC2530 system-on-chip (SoC), as a proof-of-concept. The proposed energy model is then used to calculate the energy consumption of a Multi-Parent Hierarchical (MPH) routing protocol and five widely known network sensors routing protocols: Ad-hoc On-demand Distance Vector (AODV), Dynamic Source Routing (DSR), ZigBee Tree Routing (ZTR), Low Energy Adaptive Clustering Hierarchy (LEACH), and Power Efficient Gathering in Sensor Information Systems (PEGASIS). Experimental test-bed simulations were performed on a random layout topology with two collector nodes. Each node was running under different wireless technologies: Zigbee, Bluetooth Low Energy, and LoRa by WiFi. The objective of this work is to analyze the performance of the proposed energy model in routing protocols of diverse nature: reactive, proactive, hybrid and energy-aware. Experimental results show that the MPH routing protocol consumes 16%, 13%, and 5% less energy when compared to AODV, DSR, and ZTR, respectively; and it presents only 2% and 3% of greater energy consumption with respect to the energy-aware PEGASIS and LEACH protocols, respectively. The proposed model achieves a 97% accuracy compared to the actual performance of a network. Tests are performed to analyze the consumption of the main tasks of a node in a network.
A Distribution Centre (DC) is considered a critical node in providing optimal customer service levels in a supply chain network. Therefore, improving order fulfilment time at DCs becomes critical to achieve world-class operations. One of the key processes involved in order fulfilment is that of picking, the activity that consumes most time when fulfilling an order. This article presents the analysis of an actual system that stores products in a random fashion and releases orders following FIFO rules. A simulation model is built, and two scenarios analysed. The first one (baseline) reflects the current DC operation. The second scenario (projected) includes the implementation of three picking strategies aimed at improving system performance: slotting, wave picking, and expedite picking (balancing picker’s workload). The following KPIs are used to compare both scenarios: order fulfilment lead-time, picks per man-hour, average picking time per order, average time to pack an order. Simulation results show that the systematic implementation of the proposed strategies achieves substantial improvements, not only in the order fulfilment time (54% reduction), but also in the number of orders completed in less than 8 hours, the number of orders picked per man hour and a reduction of pickers idle time.
The present study investigates the effect of the surface roughness of polymeric indoors floorings on the static friction of bare foot as well as foot wearing cotton and polymeric socks under dry, water, water + 5.0 vol. % soap, oil and water + 5.0 vol. % oil lubricated sliding conditions. Polymeric floor sheets of different roughness ranging from 0.05 and 11.0 µm were tested. The tested material is commonly used in entrance areas or corridors and in the sport halls. Cotton and polymeric socks as well as bare foot were frictionally tested. The results were compared to the friction caused by shoes as simulated by rubber test specimens of vee shape treads were prepared in the form of square sheets of 100 ´ 100 mm, 10.0 mm thickness. Experiments were carried out using a test rig designed and manufactured to measure the friction coefficient between the foot and the tested polymeric flooring materials. The experimental results showed that, at dry sliding, friction coefficient of rubber decreased with increasing surface roughness, while for bare foot and polymeric socks, friction coefficient decreased down to minimum then increased with increasing the surface roughness. Besides, friction coefficient decreased with increasing applied load. Minimum friction was observed at surface roughness ranging between 6-9 µm. In water lubricated sliding, friction coefficient of rubber increased up to maximum then decreased with increasing surface roughness. Maximum friction values were observed at surface roughness values ranging from 1.5 to 2.0 ?m Ra. Cotton socks showed the highest friction coefficient followed by rubber, bare foot then polymeric socks at 11 µm Ra. Friction coefficient drastically decreased with increasing surface roughness at water and detergent lubricated sliding. For the tested flooring materials lubricated by oil, friction coefficient of rubber increased up to maximum values then decreased with increasing the surface roughness of the flooring materials. The maximum friction values were noticed at 4.0 µm Ra. Bare foot displayed drastic reduction in friction coefficient, while cotton socks showed the highest values. When water was diluted by 5.0 wt. % oil, rubber smooth flooring surface displayed values of friction coefficient close to that observed for hydrodynamic lubrication where the two sliding surfaces are separated by the fluid film. As the roughness increased the fluid film was broken and friction increased. Cotton socks showed the highest friction compared to bare foot and polymeric socks. يهدف هذا البحث إلى اختيار الأرضيات البوليمرية ذات معامل احتكاك عال نسبيا لتقليل الحوادث الناجمة عن الانزلاق على هذه الأرضيات. يقوم هذا البحث يدرس تأثير خشونة السطح على معامل الاحتكاك الاستاتيكي الناتج من احتكاك القدم بدون جوارب مع الأرضيات البوليمرية للمبانى الداخلية، والقدم التي ترتدي جوارب بوليمرية أو قطنية تحت ظروف تزليق جافة كالحجرات والصالات والقاعات الرياضية، أو مبللة بالماء أو مبللة بالماء والصابون كالحمامات، أو بزيت الطعام المختلط بالماء كالمطابخ. أختبرت مواد الأرضيات المصنعة من البوليمرات والتى تتراوح خشونتهاالسطحية ما بين 0.5 إلى 11 ميكرون. قورنت هذه النتائج بحاله احتكاك حذاء مصنع من المطاط تمت محاكاته بعينات من المطاط مربعة الشكل (100 × 100 × 10 مم) ذات تعاريج مثل أسنان المنشار. أجريت التجارب باستخدام جهاز يقيس معامل الاحتكاك الناتج من احتكاك القدم ومواد الأرضيات عن طريق قياس قوتي الضغط والاحتكاك. وأظهرت النتائج المعملية أنه عند الانزلاق الجاف يقل معامل الاحتكاك للمطاط مع زياده خشونة السطح بينما يقل للقدم بالجوارب البوليمريه أو بدونها إلى نهاية صغري ثم يزيد بعدها بزيادة خشونة السطح. وعموما يقل معامل الاحتكاك مع زيادة الحمل المؤثر. ولوحظ أقل معامل احتكاك عند خشونة سطح ذات قيمة تتراوح ما بين 6 - 9 ميكرون. أما الأسطح المبللة بالمياه فقد زاد معامل احتكاك المطاط عند درجه خشونة تتراوح ما بين 1.5 - 2 ميكرون كما أظهرت الجوارب القطنية أعلى معامل احتكاك يليها الحذاء المطاط والقدم بدون جورب ثم الجوارب البوليمرية. كما قل كثيرا معامل الاحتكاك للأسطح المبللة بالماء والصابون. كذلك أظهرت القدم بدون جوارب معاملا للاحتكاك صغيرا للغاية عندما غطي زيت الطعام الأرضيات المختبرة بينما أعطت الجوارب القطنية أعلي احتكاك كذلك في الأسطح المبللة بالزيت المخلوط بالماء
A multirate adaptive inverse dynamic controller is presented in this study to provide the stability and reliable trajectory for robotic manipulators. The proposed controller has been implemented on the experimental Gryphon which is a five degrees of freedom industrial robot and has emulated the movement of human arm in take and place missions. The multirate structure leads to proper choosing of sampling periods and adequate refresh rates for actuators which apparently improve the tracking performance of the manipulator. By applying the proposed controller, the end-effector motion tracking error asymptotically converges to zero besides preserving the stability. Experimental results are performed to illustrate the performance of the proposed controller.
Internet of Things (IoT) is a term that has come to revolutionize the applications on the conventional use of the internet, and refers to any interaction that can be made between machines and objects. This article presents an explosive gas detection system using the Internet of things. The main goal of the system is to provide an efficient solutions to problems that occur in places with a large presence of people or even in homes, which for some reason or failure leaks toxic gases that are harmful to people’s health. For the development of the system, it is necessary to incorporate appropriate technology that helps the development of the system, being innovative and efficient.
In this paper, a study on detectability and readability of barcodes using omnidirectional vision system for automated guided vehicle is presented. Images from omnidirectional camera are known to be distorted against the height of the object. We present an algorithm for detecting and reading barcodes successfully without correcting the image distortion. Experiments were conducted both when the AGV was in motion and at rest. Three contributing factors were identified for successful barcodes detection and reading.
The research presented in this paper approaches the issue of navigation using an automated guided vehicle (AGV) in industrial environments. The work describes the navigation system of a flexible AGV intended for operation in partially structured warehouses and with frequent changes in the floor plant layout. This is achieved by incorporating a high degree of on-board autonomy and by decreasing the amount of manual work required by the operator when establishing the a priori knowledge of the environment. The AGV's autonomy consists of the set of automatic tasks, such as planner, perception, path planning and path tracking, that the industrial vehicle must perform to accomplish the task required by the operator. The integration of these techniques has been tested in a real AGV working on an industrial warehouse environment.
This paper reviews the mathematical models of four commonly encountered designs for wheeled mobile robots (WMR). These designs belong to two generic classes of wheeled robot structures: differential-drive and omnimobile. First, the two wheel differential-drive model is presented to show how zero turning radius is achieved with only bidirectional movement. Three particular designs are addressed: the popular two-active-fixed wheels and one-passive-caster wheel, a simple belt-drive, and sprocket-chain system. Next, the model for omnimoble robots with Swedish wheels is presented to illustrate holonomic omnidirectional motion. All four models are based on physical parameters easily measured and are useful to understand the internal dynamics of these WMR and to accurately visualize their motion in 2D environments. They can be therefore used as a practical reference to predict the accessibility of physical prototypes to selected places and to test different algorithms for control, path planning, guidance, and obstacle avoidance.
This paper presents the design, implementation and experimental platform of a low-cost intelligent wheelchair for the motor-disabled. The first prototype developed is an electric power wheelchair which cost is one third of the commercially available ones and is capable of both manual and autonomous operation. Manual operation relies on a joystick and is intended for people that, in spite of their disability, are well capable of maneuvering the wheelchair. Autonomous operation relies on a mobile robotics inspired computer vision system to navigate through corridors, halls, indoors/outdoors, among other structured environments and is indented for the severely impaired, such as the tetraplegics. Besides from being an affordable-to-everyone device, its current application is museum guidance: based on a location-aware interactive storytelling platform, the wheelchair guides the user among the museum's rooms while providing audio information.
A novel intelligent and agile warehouse system for energy meter storage
- C Liu
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AGV Deep Dive: How Amazon’s 2012 Acquisition Sparked a $10B Market
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Friction coefficient of rough indoor flooring materials
- F H Ezzat
- A T Hasouna
- W Ali
F.H. Ezzat, A.T. Hasouna and W. Ali, "Friction coefficient of rough
indoor flooring materials," JKAU: Eng. Sci., 19(2), pp. 53-70, 2008.
A novel intelligent and agile warehouse system for energy meter storage
- liu