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Combining Hierarchical MILP-MPC and Artificial Potential Fields for Multi-robot Priority-Based Sanitization of Railway Stations

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Distributed Autonomous Robotic Systems (DARS 2022)

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 28))

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Abstract

We propose a distributed framework, driving a team of robots for the sanitization of very large dynamic indoor environment, as the railway station. A centralized server uses the Hierarchical Mixed Integer Linear Programming to coordinate the robots assigning different zones where the cleaning is a priority; thanks to the Model Predictive Control approach we use historical data about the distribution of people and the knowledge about the transportation service of the station, to predict the future dynamic evolution of the position of people in the environment and the spreading of the contaminants. Each robot navigates the large environment represented as a gridmap, exploiting the Artificial Potential Fields technique in order to reach and clean the assigned areas. We tested our solution considering real data collected by the WiFi network of the main Italian railway station, Roma Termini. We compared our results with a Decentralized Multirobot Deep Reinforcement Learning approach.

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References

  1. Andersson, O., Ljungqvist, O., Tiger, M., Axehill, D., Heintz, F.: Receding-horizon lattice-based motion planning with dynamic obstacle avoidance. In: 2018 IEEE Conference on Decision and Control (CDC), pp. 4467–4474 (2018)

    Google Scholar 

  2. Andersson, O., Wzorek, M., Rudol, P., Doherty, P.: Model-predictive control with stochastic collision avoidance using Bayesian policy optimization. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 4597–4604 (2016)

    Google Scholar 

  3. Branca, C., Fierro, R.: A hierarchical optimization algorithm for cooperative vehicle networks. In: 2006 American Control Conference, p. 6 (2006)

    Google Scholar 

  4. Cao, Z.L., Huang, Y., Hall, E.L.: Region filling operations with random obstacle avoidance for mobile robots. J. Field Robot. 5, 87–102 (1988)

    Google Scholar 

  5. Charitidou, M., Keviczky, T.: An milp approach for persistent coverage tasks with multiple robots and performance guarantees. Eur. J. Control. 64, 100610 (2022)

    Article  MathSciNet  Google Scholar 

  6. Ciuffini, F., Tengattini, S., Bigazzi, A.Y.: Mitigating increased driving after the Covid-19 pandemic: an analysis on mode share, travel demand, and public transport capacity. Transp. Res. Res. 03611981211037884 (2023)

    Google Scholar 

  7. Earl, M.G., D’Andrea, R.: A decomposition approach to multi-vehicle cooperative control. Robot. Auton. Syst. 55(4), 276–291 (2007)

    Article  Google Scholar 

  8. Hanna, S.: Transport and dispersion of tracers simulating Covid-19 aerosols in passenger aircraft. Indoor Air 32(1), e12974 (2022)

    Article  Google Scholar 

  9. Lakshmanan, A.K., et al.: Complete coverage path planning using reinforcement learning for tetromino based cleaning and maintenance robot. Autom. Constr. 112, 103078 (2020)

    Article  Google Scholar 

  10. Lee, T.-K., Baek, S., Oh, S.-Y.: Sector-based maximal online coverage of unknown environments for cleaning robots with limited sensing. Robot. Auton. Syst. 59(10), 698–710 (2011)

    Article  Google Scholar 

  11. Lee, T.-K., Baek, S.-H., Choi, Y.-H., Oh, S.-Y.: Smooth coverage path planning and control of mobile robots based on high-resolution grid map representation. Robot. Auton. Syst. 59(10), 801–812 (2011)

    Article  Google Scholar 

  12. Lee, T.-K., Baek, S.-H., Oh, S.-Y., Choi, Y.-H.: Complete coverage algorithm based on linked smooth spiral paths for mobile robots. In: 2010 11th International Conference on Control Automation Robotics Vision, pp. 609–614 (2010)

    Google Scholar 

  13. Mallya, D., Kandala, S., Vachhani, L., Sinha, A.: Priority patrolling using multiple agents. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 8692–8698 (2021)

    Google Scholar 

  14. Miao, X., Lee, J., Kang, B.-Y.: Scalable coverage path planning for cleaning robots using rectangular map decomposition on large environments. IEEE Access 6, 38200–38215 (2018)

    Article  Google Scholar 

  15. Murtaza, G., Kanhere, S., Jha, S.: Priority-based coverage path planning for aerial wireless sensor networks. In: 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, pp. 219–224 (2013)

    Google Scholar 

  16. Narang, M., et al.: Fighting Covid: an autonomous indoor cleaning robot (AICR) supported by artificial intelligence and vision for dynamic air disinfection. In: 2021 14th IEEE International Conference on Industry Applications (INDUSCON), pp. 1146–1153 (2021)

    Google Scholar 

  17. Nasirian, B., Mehrandezh, M., Janabi-Sharifi, F.: Efficient coverage path planning for mobile disinfecting robots using graph-based representation of environment. Front. Robot. AI 8, 4 (2021)

    Article  Google Scholar 

  18. Nasirian, B., Mehrandezh, M., Janabi-Sharifi, F.: Efficient coverage path planning for mobile disinfecting robots using graph-based representation of environment. Front. Robot. AI 8 (2021)

    Google Scholar 

  19. Madridano, Á., l-Kaff, A. , Martín, D., de la Escalera, A.: Trajectory planning for multi-robot systems: Methods and applications. Expert Syst. Appl. 173, 114660 (2021)

    Google Scholar 

  20. Oh, J.S., Choi, Y.H., Park, J.B., Zheng, Y.: Complete coverage navigation of cleaning robots using triangular-cell-based map. IEEE Trans. Industr. Electron. 51(3), 718–726 (2004)

    Article  Google Scholar 

  21. Pasqualetti, F., Durham, J.W., Bullo, F.: Cooperative patrolling via weighted tours: performance analysis and distributed algorithms. IEEE Trans. Robot. 28(5), 1181–1188 (2012)

    Article  Google Scholar 

  22. Qin, W., Zhuang, Z., Huang, Z., Huang, H.: A novel reinforcement learning-based hyper-heuristic for heterogeneous vehicle routing problem. Comput. Ind. Eng. 156, 107252 (2021)

    Article  Google Scholar 

  23. Rasekhipour, Y., Khajepour, A., Chen, S.-K., Litkouhi, B.: A potential field-based model predictive path-planning controller for autonomous road vehicles. IEEE Trans. Intell. Transp. Syst. 18(5), 1255–1267 (2017)

    Article  Google Scholar 

  24. Ren, Y., et al.: D-log: a WIFI log-based differential scheme for enhanced indoor localization with single RSSI source and infrequent sampling rate. Pervasive Mob. Comput. 37, 94–114 (2017)

    Article  Google Scholar 

  25. Caccavale, R., Calà, V., Ermini, M., Finzi, A., Lippiello, V., Tavano, F.: Multi-robot sanitization of railway stations based on deep q-learning. AIRO 2021: 8th Italian Workshop on Artificial Intelligence and Robotics of the 20th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2021) (2021)

    Google Scholar 

  26. Setti, L., et al. Airborne transmission route of Covid-19: why 2 meters/6 feet of inter-personal distance could not be enough, April 2020

    Google Scholar 

  27. Song, P., Kumar, V.: A potential field based approach to multi-robot manipulation. In: Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), vol. 2, pp. 1217–1222 (2002)

    Google Scholar 

  28. Stump, E., Michael, N.: Multi-robot persistent surveillance planning as a vehicle routing problem. In: 2011 IEEE International Conference on Automation Science and Engineering, pp. 569–575 (2011)

    Google Scholar 

  29. Tardivo, A., Zanuy, A.C., Martín, C.S.: Covid-19 impact on transport: a paper from the railways’ systems research perspective. Transp. Res. Rec. 2675(5), 367–378 (2021)

    Article  Google Scholar 

  30. Tavakoli, M., Carriere, J., Torabi, A.: Robotics, smart wearable technologies, and autonomous intelligent systems for healthcare during the Covid-19 pandemic: an analysis of the state of the art and future vision. Adv. Intell. Syst. 2(7), 2000071 (2020)

    Article  Google Scholar 

  31. Zhou, X., Wang, H., Ding, B., Hu, T., Shang, S.: Balanced connected task allocations for multi-robot systems: an exact flow-based integer program and an approximate tree-based genetic algorithm. Expert Syst. Appl. 116, 10–20 (2019)

    Article  Google Scholar 

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Acknowledgements

the research leading to these results has been supported by the Italian Infrastructure Manager Rete Ferroviaria Italiana S.p.A, and by the HARMONY project (Horizon 2020 Grant Agreement No. 101017008). The authors are solely responsible for its content.

Conflict of Interests. The authors declared that they have no conflict of interests to this work.

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Authors and Affiliations

  1. Università degli studi di Napoli “Federico II”, via Claudio 21, 80125, Naples, Italy

    Riccardo Caccavale, Alberto Finzi, Vincenzo Lippiello & Fabrizio Tavano

  2. ReteFerroviariaItaliana, Piazza della Croce Rossa 1, 00161, Rome, Italy

    Mirko Ermini, Eugenio Fedeli & Fabrizio Tavano

  3. ULB, Ecole polytechnique de Bruxelles, Campus du Solbosch - CP 165/55 Avenue F.D. Roosevelt, 50, Bruxelles, Belgium

    Emanuele Garone

Authors
  1. Riccardo Caccavale
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  2. Mirko Ermini
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  3. Eugenio Fedeli
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  4. Alberto Finzi
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  5. Emanuele Garone
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  6. Vincenzo Lippiello
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  7. Fabrizio Tavano
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Correspondence to Fabrizio Tavano .

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Editors and Affiliations

  1. University of Franche-Comté, Montbéliard, France

    Julien Bourgeois

  2. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Jamie Paik

  3. CNRS, University of Franche-Comté, Besancon, France

    Benoît Piranda

  4. Harvard University, Boston, MA, USA

    Justin Werfel

  5. University of Bristol, Bristol, UK

    Sabine Hauert

  6. Boston University, Boston, MA, USA

    Alyssa Pierson

  7. University of Lübeck, Lubeck, Germany

    Heiko Hamann

  8. The Chinese University of Hong Kong, Shenzhen, Shenzhen, China

    Tin Lun Lam

  9. Kyoto University, Kyoto, Japan

    Fumitoshi Matsuno

  10. University of Illinois System, Urbana, IL, USA

    Negar Mehr

  11. University of Franche-Comté, Montbéliard, France

    Abdallah Makhoul

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Caccavale, R. et al. (2024). Combining Hierarchical MILP-MPC and Artificial Potential Fields for Multi-robot Priority-Based Sanitization of Railway Stations. In: Bourgeois, J., et al. Distributed Autonomous Robotic Systems. DARS 2022. Springer Proceedings in Advanced Robotics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-031-51497-5_31

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