Linear Search by a Pair of Distinct-Speed Robots

  • Evangelos Bampas
  • Jurek Czyzowicz
  • Leszek Gąsieniec
  • David Ilcinkas
  • Ralf Klasing
  • Tomasz Kociumaka
  • Dominik Pająk
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9988)


Two mobile robots are initially placed at the same point on an infinite line. Each robot may move on the line in either direction not exceeding its maximal speed. The robots need to find a stationary target placed at an unknown location on the line. The search is completed when both robots arrive at the target point. The target is discovered at the moment when either robot arrives at its position. The robot knowing the placement of the target may communicate it to the other robot. We look for the algorithm with the shortest possible search time (i.e. the worst-case time at which both robots meet at the target) measured as a function of the target distance from the origin (i.e. the time required to travel directly from the starting point to the target at unit velocity).

We consider two standard models of communication between the robots, namely wireless communication and communication by meeting. In the case of communication by meeting, a robot learns about the target while sharing the same location with the robot possessing this knowledge. We propose here an optimal search strategy for two robots including the respective lower bound argument, for the full spectrum of their maximal speeds. This extends the main result of Chrobak et al. (SOFSEM 2015) referring to the exact complexity of the problem for the case when the speed of the slower robot is at least one third of the faster one. In addition, we consider also the wireless communication model, in which a message sent by one robot is instantly received by the other robot, regardless of their current positions on the line. In this model, we design an optimal strategy whenever the faster robot is at most 6 times faster than the slower one.


Mobile Robot Wireless Communication Search Time Competitive Ratio Online Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Evangelos Bampas
    • 1
  • Jurek Czyzowicz
    • 2
  • Leszek Gąsieniec
    • 3
  • David Ilcinkas
    • 4
  • Ralf Klasing
    • 4
  • Tomasz Kociumaka
    • 5
  • Dominik Pająk
    • 6
  1. 1.LIF, CNRS, Aix-Marseille UniversityMarseilleFrance
  2. 2.Département d’informatiqueUniversité du Québec en OutaouaisGatineauCanada
  3. 3.Department of Computer ScienceUniversity of LiverpoolLiverpoolUK
  4. 4.LaBRI, CNRS, University of BordeauxTalenceFrance
  5. 5.Institute of InformaticsUniversity of WarsawWarsawPoland
  6. 6.Institute of InformaticsWrocław University of TechnologyWrocławPoland

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