Human safety in autonomous transport systems – review and case study

• Autorzy: Dąbrowska Alicja , Giel Robert , Werbińska-Wojciechowska Sylwia

Identyfikatory

DOI

10.2478/jok-2021-0005

Warianty tytułu

PL

Bezpieczeństwo człowieka w autonomicznych systemach transportowych – przegląd literatury i studium przypadku

• Języki publikacji: EN

Abstrakty

EN

During the robot's operational tasks, a key issue is its reliability in the aspect of human safety providing. Currently, there are a number of methods used to detect people, and their selection most often depends on the type of process carried out by robots. Therefore, the article is focused on the development of a comparative analysis of selected methods of human detection in the storage area. The main aspect in the context of which these systems were compared concerned the safety of robotic systems in the space of human occurrence. Main advantages and drawbacks of the methods in various applications were presented. The detailed analysis of the achievements in this area gives the possibility to identify research gaps and possible future research directions when using these tools in autonomous warehouses designing processes.

PL

Podczas wykonywania zadań operacyjnych przez robota, kluczowym zagadnieniem jest jego niezawodność w aspekcie bezpieczeństwa człowieka. Opracowano szereg metod służących do wykrywania człowieka, a ich wybór najczęściej uzależniony jest od rodzaju procesu realizowanego przez roboty. W związku z tym, celem artykułu jest przeprowadzenie analizy porównawczej wybranych metod wykrywania człowieka w strefie magazynowej. Główny aspekt, w kontekście którego dokonano porównania tych systemów dotyczył bezpieczeństwa pracy systemów robotycznych w przestrzeni występowania człowieka. Wskazano główne zalety i wady wybranych metod w różnych zastosowaniach. Szczegółowa analiza osiągnięć w tym obszarze dała możliwość zidentyfikowania luk badawczych i możliwych dalszych kierunków badań dotyczących wykorzystania tych narzędzi w projektowaniu procesów autonomicznych magazynów.

Słowa kluczowe

EN

object detection system; safety; human detection; autonomous transport; warehouse; review

PL

system wykrywania obiektów; bezpieczeństwo; wykrywanie człowieka; transport autonomiczny; magazyn; przegląd literatury

• Wydawca: Wydawnictwo Instytutu Technicznego Wojsk Lotniczych
• Czasopismo: Journal of KONBiN
• Rocznik: 2021
• Tom: Vol. 51, iss. 1
• Strony: 57--71
• Opis fizyczny: Bibliogr. 27 poz., rys., tab.

Twórcy

autor

Dąbrowska Alicja

• Wrocław University of Science and Technology (Politechnika Wrocławska)

autor

Giel Robert

• Wrocław University of Science and Technology (Politechnika Wrocławska)

autor

Werbińska-Wojciechowska Sylwia

• Wrocław University of Science and Technology (Politechnika Wrocławska)

Bibliografia

• 1. Aized T.: Modelling and performance maximization of an integrated automated guided vehicle system using coloured Petri net and response surface methods. Computers and Industrial Engineering, 2009, 57(3):822–831. DOI 10.1016/j.cie.2009.02.009.
• 2. Bechtsis D., Tsolakis N., Vlachos D., Iakovou E.: Sustainable supply chain management in the digitalisation era: The impact of Automated Guided Vehicles. Journal of Cleaner Production, 2017, 142. DOI 10.1016/j.jclepro.2016.10.057.
• 3. Bertozzi M., Bombini L., Broggi A., Coati A.: A Smart vision system for advanced LGV navigation and obstacle detection. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2012:508–513. DOI 10.1109/ITSC.2012.6338760.
• 4. Bostelman R., Hong T., Madhavan R.: Towards AGV safety and navigation advancement - Obstacle detection using a TOF range camera. 2005 International Conference on Advanced Robotics, ICAR ’05, Proceedings, 2005, 2005:460–467. DOI 10.1109/ICAR.2005.1507450.
• 5. Bostelman R., Hongm T.: Safety standard advancement toward mobile robot use near humans. Proc . of Intl. Conf. on safety of industrial automated systems, 2005, (January 2015):1–8.http://www.et.byu.edu/~ered/ME486/Professional_Jounal.pdfAccessed.
• 6. Bostelman R., Shackleford W., Cheok G.: Safe Control of Manufacturing Vehicles Research Towards Standard Test Methods. 2012.
• 7. Bostelman R.B., Hong T.H., Messina E.: Intelligence level performance standards research for autonomous Vehicles. CEUR Workshop Proceedings, 2015, 1484, January 2015.
• 8. Bostelman R., Shackleford W.: Performance measurements towards improved manufacturing vehicle safety. 2009:289, DOI 10.1145/1865909.1865967.
• 9. Bostelman R., Shackleford W., Cheok G., Norcross R.: Standard test procedures and metrics development for automated guided vehicle safety standards. Performance Metrics for Intelligent Systems (PerMIS) Workshop, 2012, DOI 10.1145/2393091.2393123.
• 10. Bozhinoski D., Di Ruscio D., Malavolta I., Pelliccione P., Crnkovic I.: Safety for mobile robotic system: A systematic mapping study from a software engineering perspective. Journal of Systems and Software, 2019, 151(2019):150–179. DOI 10.1016/j.jss.2019.02.021.
• 11. Culler D., Long J.: A Prototype Smart Materials Warehouse Application Implemented Using Custom Mobile Robots and Open Source Vision Technology Developed Using EmguCV. Procedia Manufacturing, 2016, 5:1092–1106. DOI 10.1016/j.promfg.2016.08.080.
• 12. Halme R.J., Lanz M., Kämäräinen J., Pieters R., Latokartano J., Hietanen A.: Review of vision-based safety systems for human-robot collaboration. Procedia CIRP, 2018, 72:111–116, DOI 10.1016/j.procir.2018.03.043.
• 13. Hedenberg K., Åstrand B.: 3D Sensors on Driverless Trucks for Detection of Overhanging Objects in the Pathway. Autonomous Industrial Vehicles: From the Laboratory to the Factory Floor, 2016:41–56. DOI 10.1520/stp159420150051.
• 14. Hentout A., Aouache M., Maoudj A., Akli I.: Human–robot interaction in industrial collaborative robotics: a literature review of the decade 2008–2017. Advanced Robotics, 2019, 33(15–16):764–799. DOI 10.1080/01691864.2019.1636714.
• 15. Lasota P.A., Fong T., Shah J.A.: A Survey of Methods for Safe Human-Robot Interaction. Foundations and Trends in Robotics, 2017, 5(3):261–349. DOI 10.1561/2300000052.
• 16. Le-Anh T., De Koster M.B.M.: A review of design and control of automated guided vehicle systems. European Journal of Operational Research, 2006, 171(1):1–23. DOI 10.1016/j.ejor.2005.01.036.
• 17. Liu S.B., Roehm H., Heinzemann C., Lutkebohle I., Oehlerking J., Althoff M.: Provably safe motion of mobile robots in human environments. IEEE International Conference on Intelligent Robots and Systems, 2017, 2017-Septe:1351–1357. DOI 10.1109/IROS.2017.8202313.
• 18. Markis A., Papa M., Kaselautzke D., Rathmair M., Sattinger V., Brandstötter M.: Safety of Mobile Robot Systems in Industrial Applications. Proceedings of the ARW & OAGM Workshop, 2019:26–31. DOI 10.3217/978-3-85125-663-5-04.
• 19. Missala T.: Safety integrity of turn-wrist robot constructions (in Polish). Tchoń, K. (ed). Robotics Advances, Robot systems and collaboration. Transport and Communication Publishers, 2006:139–148.
• 20. Norton A., Gavriel P., Yanco H.: A Standard Test Method for Evaluating Navigation and Obstacle Avoidance Capabilities of AGVs and AMRs. Smart and Sustainable Manufacturing Systems, 2019, 3(2):20190028. DOI 10.1520/ssms20190028.
• 21. Norton A., Yanco H.: Preliminary Development of a Test Method for Obstacle Detection and Avoidance in Industrial Environments. Autonomous Industrial Vehicles: From the Laboratory to the Factory Floor, 2016:23–40. DOI 10.1520/stp159420150059.
• 22. Pratama P.S., Kwun Jeong S., Sil Park S., Bong Kim S.: Moving Object Tracking and Avoidance Algorithm for Differential Driving AGV Based on Laser Measurement Technology. International Journal of Science and Engineering, 2012, 4(1):11–15. DOI 10.12777/ijse.4.1.11-15.
• 23. Sabattini L., Cardarelli E., Digani V., Secchi C., Fantuzzi C., Fuerstenberg K.: Advanced sensing and control techniques for multi AGV systems in shared industrial environments. IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 2015, 2015-Octob:1–7. DOI 10.1109/ETFA.2015.7301488.
• 24. Shneier M.O., Hong T., Cheok G., Saidi K., Shackleford W.: Performance Evaluation Methods for Human Detection and Tracking Systems for Robotic Applications. 2014. DOI 10.6028/NIST.IR.8045.
• 25. Tanha S.D.N., Dehkordi S.F., Korayem A.H.: Control a mobile robot in Social environments by considering human as a moving obstacle. Proceedings of the 6th RSI International Conference on Robotics and Mechatronics, IcRoM 2018, 2019, (IcRoM):256–260. DOI 10.1109/ICRoM.2018.8657641.
• 26. Vatavu A., Costea A.D., Nedevschi S.: Modeling and tracking of dynamic obstacles for logistic plants using omnidirectional stereo vision. IEEE International Conference on Intelligent Robots and Systems, 2015, 2015-Decem:3552–3558. DOI 10.1109/IROS.2015.7353873.
• 27. Yoon S., Bostelman R.: Analysis of Automatic through Autonomous – Unmanned Ground Vehicles (A-UGVs) towards Performance Standards. IEEE International Symposium on Robotic and Sensors Environments, ROSE 2019 - Proceedings, 2019. DOI 10.1109/ROSE.2019.8790421.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).