Multi-robot task allocation for exploration

  • Gao Ping-an Email author
  • Cai Zi-xing 


The problem of allocating a number of exploration tasks to a team of mobile robots in dynamic environments was studied. The team mission is to visit several distributed targets. The path cost of target is proportional to the distance that a robot has to move to visit the target. The team objective is to minimize the average path cost of target over all targets. Finding an optimal allocation is strongly NP-hard. The proposed algorithm can produce a near-optimal solution to it. The allocation can be cast in terms of a multi-round single-item auction by which robots bid on targets. In each auction round, one target is assigned to a robot that produces the lowest path cost of the target. The allocated targets form a forest where each tree corresponds a robot’s exploring targets set. Each robot constructs an exploring path through depth-first search in its target tree. The time complexity of the proposed algorithm is polynomial. Simulation experiments show that the allocating method is valid.

Key words

multi-robot systems task allocation average path cost multi-round single-item auction target tree 

CLC number



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  1. [1]
    Mishkin A, Morrison J, Nguyen T, et al. Experiences with operations and autonomy of the mars pathfinder microrover [C]// 1998 IEEE Aerospace Conference Proceedings. IEEE, Piscataway, 1998: 337–351.CrossRefGoogle Scholar
  2. [2]
    Thrun S, Burgard W, Fox D. A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping[C]// Proceedings of IEEE International Conference on Robotics and Automation (ICRA). San Francisco, 2000: 321–328.Google Scholar
  3. [3]
    Murphy R. Rescue robotics for homeland security[J]. Communications of the ACM, Special Issue on Homeland Security, 2004, 27(3): 66–69.MathSciNetGoogle Scholar
  4. [4]
    Hougen D. A miniature robotic system for reconnaissance and surveillance[C]// Proceedings of IEEE International Conference on Robotics and Automation (ICRA). San Francisco, 2000: 501–507.Google Scholar
  5. [5]
    Kalra N, Ferguson D, Stentz A. Hoplites: a marketbased framework for planned tight coordination in multirobot teams[C]// Proceedings of the International Conference on Robotics and Automation. Barcelona, 2005: 1182–1189.Google Scholar
  6. [6]
    Parker L. Lifelong adaptation in heterogeneous multirobot teams: response to continual variation in individual robot performance[J]. Autonomous Robots, 2000, 8(3): 239–267.CrossRefGoogle Scholar
  7. [7]
    Gerkey B, Mataric M. A formal analysis and taxonomy of task allocation in multi-robot systems [J]. Intl J of Robotics Research, 2004, 23(9): 939–954.CrossRefGoogle Scholar
  8. [8]
    Stone P, Veloso M. Task decomposition, dynamic role assignment, and low-bandwidth communication for real-time strategic teamwork [J]. Artificial Intelligence, 1999, 110(2): 241–273.CrossRefGoogle Scholar
  9. [9]
    Mataric M, Sukhatme G. Task-allocation and coordination of multiple robots for planetary exploration[C] // Proceedings of the International Conference on Advanced Robotics. Buda, 2001: 61–70.Google Scholar
  10. [10]
    Zlot R, Stentz A, Dias M, et al. Multi-robot exploration controlled by a market economy[C]// Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Washington, 2002: 3016–3023.Google Scholar
  11. [11]
    Burgard W, Moors M, Stachniss C, et al. Coordinated multi-robot exploration[J]. IEEE Transaction on Robotics, 2005, 21(3):376–386.CrossRefGoogle Scholar
  12. [12]
    ZHANG Fei, CHEN Wei-dong, XI Yu-geng. Improved market-based approach to collaborative multirobot exploration[J]. Control and Decision, 2005, 20(5): 516–524. (in Chinese)Google Scholar
  13. [13]
    Tovey C, Lagoudakis M, Jain S, et al. The generation of bidding rules for auction-based robot coordination[C]// Parker L, Scheider F, Schultz A. Multirobot Systems: From Swarms to Intelligent Automata. Berlin: Springer, 2005: 3–14.CrossRefGoogle Scholar
  14. [14]
    Sitters R. The minimum latency problem is Np-hard for weighted trees [C]// Proceedings of the Ninth Conference on Integer Programming and Combinatorial Optimization. Cambridge, MA: Springer, 2002: 230–239.CrossRefGoogle Scholar
  15. [15]
    Lagoudakis M, Berhault M, Koenig S, et al. Simple auction with performance guarantees for multi-robot task allocation[C]// Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Sendai, 2004: 698–705.Google Scholar
  16. [16]
    Dias M B, Zlot N, Kalra R, et al. Market-based multirobot coordination: a survey and analysis[R]. CMU - RI - TR - 05 - 13, Robotics Institute, Carnegie Mellon University, 2005.Google Scholar

Copyright information

© Science Press 2001

Authors and Affiliations

  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaChina
  2. 2.Department of Computer ScienceXiangtan UniversityXiangtanChina

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