Abstract
Data fusion methods enable the precision of measurements based on information from individual systems as well as many different subsystems to be increased. Besides, the data obtained in this way enables additional conclusions drawn from their work, e.g., detecting degradation of the work of subsystems. The article focuses on the possibilities of using data fusion to create Autonomous Guided Vehicles solutions in increasing precise positioning, navigation, and cooperation with the production environment, including docking. For this purpose, it was proposed that information from other manufacturing subsystems be used. This paper aims to review the current implementation possibilities and to identify the relationship between various research sub-areas.
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Wang, S., Wan, J., Li, D., Zhang, C.: Implementing smart factory of Industrie 4.0: an outlook. Int. J. Distrib. Sens. Netw. 12(1), 3159805 (2016)
European Commission. A Manufacturing Industry Vision 2025, European Commission (Joint Research Centre) Foresight study (2013)
Shafiq, S.I., Sanin, C., Szczerbicki, E., Toro, C.: Virtual engineering object/ virtual engineering process: a specialized form of cyber physical system for Industrie 4.0. Procedia Comput. Sci. 60, 1146–1155 (2015)
Botta, A., de Donato, W., Persico, V., Pescapé, A.: Integration of cloud computing and internet of things: a survey. Future Gener. Comput. Syst. 56, 684–700 (2016)
Shi, D., Mi, H., Collins, E.G., Wu, J.: An indoor low-cost and high-accuracy localization approach for AGVs. IEEE Access 8, 50085–50090 (2020)
Realyvásquez-Vargas, A., et al.: Introduction and configuration of a collaborative robot in an assembly task as a means to decrease occupational risks and increase efficiency in a manufacturing company. Robot. Comput.-Integr. Manuf. 57, 315–328 (2019)
Kuc, M., Sułek, W., Kania, D.: FPGA-oriented LDPC decoder for cyber-physical systems. Mathematics 8, 723 (2020)
Mrozek, D., Tokarz, K., Pankowski, D., Małysiak-Mrozek, B.: A hopping umbrella for fuzzy joining data streams from IoT devices in the cloud and on the edge. IEEE Trans. Fuzzy Syst. 28, 916–928 (2019)
Ji, Z., Ganchev, I., O’Droma, M., Zhao, L., Zhang, X.: A cloud-based car parking middleware for IoT-based smart cities: design and implementation. Sensors 14, 22372–22393 (2014)
Opara, A., Kubica, M., Kania, D.: Methods of improving time efficiency of decomposition dedicated at FPGA structures and using BDD in the process of cyber-physical synthesis. IEEE Access 7, 20619–20631 (2019)
Grzechca, D., et al.: How accurate can UWB and dead reckoning positioning systems be? comparison to SLAM using the RPLidar system. Sensors 20, 3761 (2020)
Paszek, K., Grzechca, D., Tomczyk, M., Marciniak, A.: UWB positioning system with the support of MEMS sensors for indoor and outdoor environment. Journal of Communications, vol. 15 (2020)
Grzechca, D.E., Pelczar, P., Chruszczyk, L.: Analysis of object location accuracy for ibeacon technology based on the RSSI path loss model and fingerprint map. Int. J. Electron. Telecommun. 62(4), 371–378 (2016). https://doi.org/10.1515/eletel-2016-0051
Grzechca, D., Paszek, K.: Short-term positioning accuracy based on mems sensors for smart city solutions (2019). https://doi.org/10.24425/MMS.2019.126325
Roth, H., Schilling, K.: Navigation and docking manoeuvres of mobile robots in industrial environments. In: IECON 1998 Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No. 98CH36200), pp. 2458–2462. IEEE, Aachen, Germany (1998)
Thota, P., Kim, Y.: Implementation and comparison of M2M protocols for internet of things. In: 2016 International Conference ACIT-CSII-BCD, pp. 43–48. IEEE, Las Vegas, NV, USA (2016)
Hanzel, K., Paszek, K., Grzechca, D.: The influence of the data packet size on positioning parameters of UWB system for the purpose of tagging smart city infrastructure. Bulletin of the Polish Academy of Sciences. Technical Sciences, vol. 68 (2020)
Tokarz, K., Czekalski, P., Sieczkowski, W.: Integration of ultrasonic and inertial methods in indoor navigation system. Theor. Appl. Inform. 26, 107–117 (2015)
Ziebinski, A., Cupek, R., Nalepa, M.: Obstacle avoidance by a mobile platform using an ultrasound sensor. In: Nguyen, N.T., Papadopoulos, G.A., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds.) ICCCI 2017. LNCS (LNAI), vol. 10449, pp. 238–248. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67077-5_23
Han, Y., Wei, C., Li, R., Wang, J., Yu, H.: A novel cooperative localization method based on IMU and UWB. Sensors 20, 467 (2020)
Ziebinski, A., Bregulla, M., Fojcik, M., Kłak, S.: Monitoring and controlling speed for an autonomous mobile platform based on the hall sensor. In: Nguyen, N.T., Papadopoulos, G.A., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds.) ICCCI 2017. LNCS (LNAI), vol. 10449, pp. 249–259. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67077-5_24
Guan, H., Li, L., Jia, X.: Multisensor fusion vehicle positioning based on Kalman Filter, pp. 296–299. IEEE (2013)
Wen, S., Othman, K., Rad, A., Zhang, Y., Zhao, Y.: Indoor SLAM using laser and camera with closed-loop controller for NAO humanoid robot. Abstr. Appl. Anal. 2014, 1–8 (2014)
Fang, B.T.: Trilateration and extension to Global Positioning System navigation. J. Guid. Control Dyn. 9, 715–717 (1986)
Grzechca, D., Hanzel, K., Paszek, K.: Accuracy analysis for object positioning on a circular trajectory based on the UWB location system. In: 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET), pp. 69–74. IEEE, Lviv, Ukraine (2018)
Sidek, O., Quadri, S.A.: A review of data fusion models and systems. Int. J. Image Data Fusion 3, 3–21 (2012)
Liggins II, M., Hall, D., Llinas, J.: Handbook of Multisensor Data Fusion: Theory and Practice. CRC Press, Boca Raton (2017)
Budzan, S., Kasprzyk, J.: Fusion of 3D laser scanner and depth images for obstacle recognition in mobile applications. Opt. Lasers Eng. 77, 230–240 (2016)
Bjerkeng, M., Pettersen, K.Y., Kyrkjebø, E.: Stereographic projection for industrial manipulator tasks: theory and experiments, pp. 4676–4683. IEEE (2011)
Błachuta, M., Czyba, R., Janusz, W., Szafrański, G.: Data fusion algorithm for the altitude and vertical speed estimation of the VTOL platform. J. Intell. Robot. Syst. 74, 413–420 (2014)
Liu, L., Kuo, S.M., Zhou, M.: Virtual sensing techniques and their applications. In: 2009 International Conference on Networking, Sensing and Control, pp. 31–36. IEEE, Okayama, Japan (2009)
Lee, M.C., Park, M.C.: Artificial potential field based path planning for mobile robots using a virtual obstacle concept. In: Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics AIM 2003, pp. 735–740. IEEE, Kobe, Japan (2003)
Ziebinski, A., Cupek, R., Piech, A.: Distributed control architecture for the autonomous mobile platform. Thessaloniki, Greece, p. 080012 (2018)
Weyrich, M., Schmidt, J.-P., Ebert, C.: Machine-to-Machine communication. IEEE Softw. 31, 19–23 (2014)
Cupek, R., Ziebinski, A., Fojcik, M.: An ontology model for communicating with an autonomous mobile platform. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2017. CCIS, vol. 716, pp. 480–493. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58274-0_38
Kohlbrecher, S., von Stryk, O., Meyer, J., Klingauf, U.: A flexible and scalable SLAM system with full 3D motion estimation. In: 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics, pp. 155–160. IEEE, Kyoto, Japan (2011)
Hankel, M., Rexroth, B.: The reference architectural model industrie 4.0 (rami 4.0). ZVEI (2015)
Cupek, R., Drewniak, M., Ziebinski, A.: Information models for a new generation of manufacturing systems - a case study of automated guided vehicle. In: 2019 IEEE International Conference on Systems, Man and Cybernetics SMC, pp. 858–864. IEEE, Bari, Italy (2019)
Lang, J., Iwanitz, F., Burke, T.: OPC from Data Access to Unified Architecture. OPC Found. Softing (2010)
Cupek, R., Drewniak, M., Ziebinski, A., Fojcik, M.: Digital twins for highly customized electronic devices – case study on a rework operation. IEEE Access 7, 164127–164143 (2019)
Varghese, A., Tandur, D.: Wireless requirements and challenges in Industry 4.0. In: 2014 International Conference on Contemporary Computing and Informatics (IC3I), pp. 634–638. IEEE, Mysore, India (2014)
Elgazzar, M.H.: Perspectives on M2M protocols. In: 2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS), pp. 501–505. IEEE, Cairo, Abbassia, Egypt (2015)
Fadlullah, Z.M., Fouda, M.M., Kato, N., Takeuchi, A., Iwasaki, N., Nozaki, Y.: Toward intelligent machine-to-machine communications in smart grid. IEEE Commun. Mag. 49, 60–65 (2011)
Cheng, Y., Tao, F., Xu, L., Zhao, D.: Advanced manufacturing systems: supply–demand matching of manufacturing resource based on complex networks and Internet of Things. Enterp. Inf. Syst. 12(7), 1–18 (2016)
Trawiński, B., Smętek, M., Lasota, T., Trawiński, G.: Evaluation of fuzzy system ensemble approach to predict from a data stream. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds.) ACIIDS 2014. LNCS (LNAI), vol. 8398, pp. 137–146. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05458-2_15
Golab, L., Özsu, M.T.: Processing sliding window multi-joins in continuous queries over data streams. Elsevier, pp. 500–511 (2003)
Hammad, M.A., Aref, W.G., Elmagarmid, A.K.: Stream window join: tracking moving objects in sensor-network databases. In: 15th International Conference on Scientific and Statistical Database Management 2003, pp. 75–84. IEEE Computer Society, Cambridge, MA, USA (2003)
Gomes, J., Choi, H.-A.: Adaptive optimization of join trees for multi-join queries over sensor streams. Inf. Fusion 9, 412–424 (2008)
Ji, Y., Liu, S., Lu, L., Lang, X., Yao, H., Wang, R.: VC-TWJoin: a stream join algorithm based on variable update cycle time window. In: 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 178–183. IEEE, Nanjing, China (2018)
Zhuang, Z., Feng, T., Pan, Y., Ramachandra, H., Sridharan, B.: Effective multi-stream joining in apache samza framework. In: 2016 IEEE International Congress on Big Data, pp. 267–274. IEEE, San Francisco, CA, USA (2016)
Malysiak-Mrozek, B., Lipinska, A., Mrozek, D.: Fuzzy join for flexible combining big data lakes in cyber-physical systems. IEEE Access 6, 69545–69558 (2018)
Wachowicz, A., Małysiak-Mrozek, B., Mrozek, D.: Combining data from fitness trackers with meteorological sensor measurements for enhanced monitoring of sports performance. In: Rodrigues, J.M.F., Cardoso, P.J.S., Monteiro, J., Lam, R., Krzhizhanovskaya, V.V., Lees, M.H., Dongarra, J.J., Sloot, P.M.A. (eds.) ICCS 2019. LNCS, vol. 11538, pp. 692–705. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22744-9_54
Acknowledgments
The research leading to these results received funding from the Norway Grants 2014–2021, which is operated by the National Centre for Research and Development under the project “Automated Guided Vehicles integrated with Collaborative Robots for Smart Industry Perspective” (Project Contract no.: NOR/POLNOR/CoBotAGV/0027/2019 -00) and partially by the Polish Ministry of Science and Higher Education Funds for Statutory Research.
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Ziebinski, A. et al. (2021). Challenges Associated with Sensors and Data Fusion for AGV-Driven Smart Manufacturing. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12745. Springer, Cham. https://doi.org/10.1007/978-3-030-77970-2_45
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