Chauffeuring a Crashed Robot from a Disk

  • Debasish PattanayakEmail author
  • H. Ramesh
  • Partha Sarathi Mandal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11931)


Evacuation of robots from a disk has recently attained a lot of attention. We visit the problem from the perspective of fault-tolerance. We consider two robots trying to evacuate from a disk via a single hidden exit on the perimeter of the disk. The robots communicate wirelessly. The robots are susceptible to crash faults, after which they stop moving and communicating. We design the algorithms for tolerating one fault. The objective is to minimize the worst-case time required to evacuate both the robots from the disk. When the non-faulty robot chauffeurs the crashed robot, it takes \(\alpha \ge 1\) amount of time to travel unit distance. With this, we also provide a lower bound for the evacuation time. Further, we evaluate the worst-case of the algorithms for different values of \(\alpha \) and the crash time.


Evacuation Mobile robots Crash faults Distributed algorithms 


  1. 1.
    Brandt, S., Foerster, K., Richner, B., Wattenhofer, R.: Wireless evacuation on m rays with k searchers. In: SIROCCO, Porquerolles, France, pp. 140–157 (2017). Scholar
  2. 2.
    Brandt, S., Laufenberg, F., Lv, Y., Stolz, D., Wattenhofer, R.: Collaboration without communication: Evacuating two robots from a disk. In: CIAC, Athens, pp. 104–115 (2017). Scholar
  3. 3.
    Chuangpishit, H., Georgiou, K., Sharma, P.: Average case - worst case tradeoffs for evacuating 2 robots from the disk in the face-to-face model. In: Algorithms for Sensor Systems - 14th International Symposium on Algorithms and Experiments for Wireless Sensor Networks, ALGOSENSORS 2018, Helsinki, Finland, August 23–24, 2018, Revised Selected Papers, pp. 62–82 (2018). Scholar
  4. 4.
    Chuangpishit, H., Mehrabi, S., Narayanan, L., Opatrny, J.: Evacuating an equilateral triangle in the face-to-face model. In: OPODIS, Lisbon, Portugal, pp. 11:1–11:16 (2017).
  5. 5.
    Czyzowicz, J., Gasieniec, L., Gorry, T., Kranakis, E., Martin, R., Pajak, D.: Evacuating robots via unknown exit in a disk. In: DISC, USA, 12–15 October 2014, pp. 122–136 (2014). Scholar
  6. 6.
    Czyzowicz, J., et al.: Evacuation from a disc in the presence of a faulty robot. In: SIROCCO, Porquerolles, France, pp. 158–173 (2017). Scholar
  7. 7.
    Czyzowicz, J., et al.: God save the queen. In: 9th International Conference on Fun with Algorithms, FUN 2018, 13–15 June 2018, La Maddalena, Italy, pp. 16:1–16:20 (2018).
  8. 8.
    Czyzowicz, J., et al.: Priority evacuation from a disk using mobile robots - (extended abstract). In: Structural Information and Communication Complexity - 25th International Colloquium, SIROCCO 2018, Ma’ale HaHamisha, Israel, 18–21 June 2018, Revised Selected Papers, pp. 392–407 (2018). Scholar
  9. 9.
    Czyzowicz, J., et al.: Search on a line by byzantine robots. In: ISAAC, Sydney, Australia, pp. 27:1–27:12 (2016).
  10. 10.
    Czyzowicz, J., Georgiou, K., Kranakis, E., Narayanan, L., Opatrny, J., Vogtenhuber, B.: Evacuating robots from a disk using face-to-face communication (extended abstract). In: CIAC, Paris, France, pp. 140–152 (2015). Scholar
  11. 11.
    Czyzowicz, J., Kranakis, E., Krizanc, D., Narayanan, L., Opatrny, J.: Search on a line with faulty robots. In: PODC, Chicago, IL, USA, pp. 405–414 (2016).
  12. 12.
    Disser, Y., Schmitt, S.: Evacuating two robots from a disk: a second cut. In: Proceedings of the Structural Information and Communication Complexity - 26th International Colloquium, SIROCCO 2019, L’Aquila, Italy, 1–4 July 2019, pp. 200–214 (2019). Scholar
  13. 13.
    Kupavskii, A., Welzl, E.: Lower bounds for searching robots, some faulty. In: Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing, PODC 2018, Egham, United Kingdom, 23–27 July 2018, pp. 447–453 (2018).

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Indian Institute of Technology GuwahatiGuwahatiIndia

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