Fuzzy evaluation method for environmental factors affecting a mobile robot's sensor system in view of design for logistics

• Autorzy: Giel Robert , Dąbrowska Alicja

Identyfikatory

DOI

10.17270/J.LOG.2023.823

• Języki publikacji: EN

Abstrakty

EN

Background: The paper is devoted to mobile robot design problems with a focus on exteroceptive sensor systems for operation in a mixed environment (indoor with outdoor possibility). With a view to the design for logistics, the important concerns are, among others, minimization of the number of parts, reduction of weight, and reduction of dimensions. One of the challenges that arise here is the consideration of environmental factors, which vary among different application systems. It is necessary to reach a compromise between operational requirements and costs involved. Therefore, the relevance of the environmental factors should be evaluated to divide them into those that should be addressed and those that can be ignored. This will translate into the selection of sensors in sufficient quantity to provide the requirements without excessiveness. Methods: We propose a novel three-stage method for assessing the relevance of environmental factors using fuzzy logic with occurrence, recovery, and impact level consideration. We take into account the impact level of each factor on the entire sensor system, restoration of functions lost completely or partially as a result of the factor (recovery), and the frequency of factor occurrence. Results: The identified environmental factors, evaluated in term of their relevance are hierarchized from the most to the least relevant. The application of the method is presented on the basis of an autonomous forklift for indoor and outdoor use. Conclusions: Based on the proposed method, it is possible to design a sensor system with consideration of any operation environment. The three-criteria method allows evaluation of any factor influencing sensor system on a five-point scale, both in terms of occurrence and severity (understood as impact level effect and recovery time). By evaluating the factors and thus prioritizing them using our method, only the most important factors from the designer's point of view can be taken into account. This can translate into minimizing the number of sensors and thus cost reduction and shorter implementation time.

Słowa kluczowe

EN

design for logistics; mobile robot; sensor system design; fuzzy set theory; mixed environment

PL

projekt dla logistyki; robot mobilny; projektowanie systemów czujników; teoria zbiorów rozmytych; środowisko mieszane

• Wydawca: Wyższa Szkoła Logistyki
• Czasopismo: LogForum
• Rocznik: 2023
• Tom: Vol. 19, no. 2
• Strony: 169--182
• Opis fizyczny: Bibliogr. 27 poz., rys., tab., wykr.

Twórcy

autor

Giel Robert

• Department of Technical Systems Operation and Maintenance, Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, Wroclaw, Poland

• https://orcid.org/0000-0002-7406-0044

autor

Dąbrowska Alicja

• Department of Technical Systems Operation and Maintenance, Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, Wroclaw, Poland

Bibliografia

• 1. Bielecki M., Galińska B., Polak-Sopińska A., 2021, Perfect Product in Perfect Industry - Design for Logistics.
• 2. Bijelic M., Gruber T., Ritter W., 2018, Benchmarking Image Sensors under Adverse Weather Conditions for Autonomous Driving, IEEE Intelligent Vehicles Symposium, Proceedings, 2018-June(Iv), 1773–1779. https://doi.org/10.1109/IVS.2018.8500659
• 3. Blanco-Mesa F., Merigó J. M., Gil-Lafuente A. M., 2017, Fuzzy decision making: A bibliometric-based review, Journal of Intelligent and Fuzzy Systems, 32(3), 2033–2050. https://doi.org/10.3233/JIFS-161640
• 4. Chen G., Pham T. T., (n.d.), Introduction to fuzzy sets, fuzzy logic and fuzzy control systems. CRC Press.
• 5. Dey J., Taylor W., Pasricha S., 2021, VESPA: A Framework for Optimizing Heterogeneous Sensor Placement and Orientation for Autonomous Vehicles, IEEE Consumer Electronics Magazine, 10(2), 16–26. https://doi.org/10.1109/MCE.2020.3002489
• 6. El-Hassan F. T., 2020, Experimenting with Sensors of a Low-Cost Prototype of an Autonomous Vehicle, IEEE Sensors Journal, 20(21), 13131–13138. https://doi.org/10.1109/JSEN.2020.3006086
• 7. Fayyad J., Jaradat M. A., Gruyer D., Najjaran H., 2020, Deep learning sensor fusion for autonomous vehicle perception and localization: A review, Sensors (Switzerland), 20(15), 1–34. https://doi.org/10.3390/s20154220
• 8. Freund L., Al-majeed S., 2021, Managing Industry 4.0 integration - the Industry 4.0 knowledge & technology framework, 17(4), 569–586.
• 9. Galar D., Kumar U., 2017, Sensors and Data Acquisition, In eMaintenance (pp. 1–72). https://doi.org/10.1016/b978-0-12-811153-6.00001-4
• 10. Hedenberg K., Åstrand B., 2016, 3D Sensors on Driverless Trucks for Detection of Overhanging Objects in the Pathway, Autonomous Industrial Vehicles: From the Laboratory to the Factory Floor, 41–56. https://doi.org/10.1520/stp159420150051
• 11. Heinold L., Barkanyi A., Abonyi J., 2021, Test plan for the verification of the robustness of sensors and automotive electronic products using scenario-based noise deployment (Snd), Sensors, 21(10). https://doi.org/10.3390/s21103359
• 12. Keyes B., Casey R., Yanco H. A., Maxwell B. A., Georgiev Y., 2006, Camera placement and multi-camera fusion for remote robot operation, IEEE International Workshop on Safety, Security and Rescue Robotics, (June), 22–24. Retrieved from http://fangorn.colby.edu/cs/maxwell/papers/pdfs/Keyes-etal-SSRR-2006.pdf
• 13. Kim T. H., Park T. H., 2020, Placement Optimization of Multiple Lidar Sensors for Autonomous Vehicles, IEEE Transactions on Intelligent Transportation Systems, 21(5), 2139–2145. https://doi.org/10.1109/TITS.2019.2915087
• 14. Kocić J., Jovičić N., Drndarević V., 2018, Sensors and sensor’s fusion in autonomous vehicles, 26th Telecommunications Forum TELFOR 2018, 21(19), 9–12. https://doi.org/10.3390/s21196586
• 15. Li Q., Queralta J. P., Gia T. N., Zou Z., Westerlund T., 2020, Multi-Sensor Fusion for Navigation and Mapping in Autonomous Vehicles: Accurate Localization in Urban Environments, Unmanned Systems, 8(3), 229–237. https://doi.org/10.1142/S2301385020500168
• 16. Li Y., Birchfield S. T., 2010, Image-based segmentation of indoor corridor floors for a mobile robot, IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings, 837–843. https://doi.org/10.1109/IROS.2010.5652818
• 17. Nikolaidis S., Ueda R., Hayashi A., Arai T., 2009, Optimal camera placement considering mobile robot trajectory, 2008 IEEE International Conference on Robotics and Biomimetics, ROBIO 2008, 1393–1396. https://doi.org/10.1109/ROBIO.2009.4913204
• 18. Norton A., Yanco H., 2016, Preliminary Development of a Test Method for Obstacle Detection and Avoidance in Industrial Environments, Autonomous Industrial Vehicles: From the Laboratory to the Factory Floor, 23–40. https://doi.org/10.1520/stp159420150059
• 19. Qu Y., Yang M., Zhang J., Xie W., Qiang B., Chen J., 2021, An outline of multi-sensor fusion methods for mobile agents indoor navigation, Sensors, 21(5), 1–26. https://doi.org/10.3390/s21051605
• 20. Rosique F., Navarro P. J., Fernández C., Padilla A., 2019, A systematic review of perception system and simulators for autonomous vehicles research, Sensors (Switzerland), 19(3). https://doi.org/10.3390/s19030648
• 21. Singh R., Nagla K. S., 2020, Comparative analysis of range sensors for the robust autonomous navigation – a review, Sensor Review, 40(1), 17–41. https://doi.org/10.1108/SR-01-2019-0029
• 22. Tang L., Shi Y., He Q., Sadek A. W., Qiao C., 2020, Performance Test of Autonomous Vehicle Lidar Sensors Under Different Weather Conditions, Transportation Research Record, 2674(1), 319–329. https://doi.org/10.1177/0361198120901681
• 23. Tulkoff C., Caswell G., 2021, Design for Excellence in Electronics Manufacturing. John Wiley & Sons Ltd.
• 24. Vargas J., Alsweiss S., Toker O., Razdan R., Santos J., 2021, An overview of autonomous vehicles sensors and their vulnerability to weather conditions, Sensors, 21(16), 1–22. https://doi.org/10.3390/s21165397
• 25. Ward C. C., Iagnemma K., 2008, A dynamic-model-based wheel slip detector for mobile robots on outdoor terrain, IEEE Transactions on Robotics, 24(4), 821–831. https://doi.org/10.1109/TRO.2008.924945
• 26. Yeong D. J., Velasco-hernandez G., Barry J., Walsh J., 2021, Sensor and sensor fusion technology in autonomous vehicles: A review, Sensors, 21(6), 1–37. https://doi.org/10.3390/s21062140
• 27. Żuchowski W., 2022, the Smart Warehouse Trend: Actual Level of Technology Availability, Logforum, 18(2), 227–235. https://doi.org/10.17270/J.LOG.2022.702