Heuristics on the Data-Collecting Robot Problem with Immediate Rewards

  • Zhi XingEmail author
  • Jae C. Oh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9862)


We propose the Data-collecting Robot Problem, where robots collect data as they visit nodes in a graph, and algorithms to solve it. There are two variations of the problem: the delayed-reward problem, in which robots must travel back to the base station to deliver the data collected and to receive rewards; and the immediate-reward problem, in which the reward is immediately given to the robots as they visit each node. The delayed-reward problem is discussed in one of the authors’ work. This paper focuses on the immediate-reward problem. The solution structure has a clustering step and a tour-building step. We propose Progressive Gain-aware Clustering that finds good quality solutions with efficient time complexity. Among the six proposed tour-building heuristics, Greedy Insertion and Total-Loss algorithms perform best when data rewards are different.


Adversary route planning Multi-robot systems Autonomous systems 



We thank Mahmuda Rahman and Jeff Hudack for reviewing drafts of this paper and providing valuable suggestions.


  1. 1.
    Archetti, C., Feillet, D., Hertz, A., Grazia Speranza, M.: The capacitated team orienteering and profitable tour problems. J. Oper. Res. Soc. 60, 831–842 (2009)CrossRefzbMATHGoogle Scholar
  2. 2.
    Blum, A., Chalasani, P., Coppersmith, D., Pulleyblank, B., Raghavan, P., Sudan, M.: The minimum latency problem. In: Proceedings of the 26th Symposium on Theory of Computing, STOC, p. 9 (1994)Google Scholar
  3. 3.
    Blum, A., Chawla, S., Karger, D.R., Lane, T., Meyerson, A., Minkoff, M.: Approximation algorithms for orienteering and discounted-reward TSP. SIAM J. Comput. 37(2), 653–670 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Chaudhuri, K., Godfrey, B., Rao, S., Talwar, K.: Paths, trees, and minimum latency tours. In: Proceedings - 44th Annual IEEE Symposium on Foundations of Computer Science, FOCS, pp. 36–45. IEEE (2003)Google Scholar
  5. 5.
    Choi, H.L., Brunet, L., How, J.P.: Consensus-based decentralized auctions for robust task allocation. IEEE Trans. Robot. 25(4), 912–926 (2009)CrossRefGoogle Scholar
  6. 6.
    Ekici, A., Retharekar, A.: Multiple agents maximum collection problem with time dependent rewards. Comput. Ind. Eng. 64(4), 1009–1018 (2013)CrossRefGoogle Scholar
  7. 7.
    Feillet, D., Dejax, P., Gendreau, M.: Traveling salesman problems with profits: an overview. Transp. Sci. 39, 188–205 (2001)CrossRefGoogle Scholar
  8. 8.
    Hudack, J., Oh, J.: Multi-agent sensor data collection with attrition risk. In: Proceedings - The 26th International Conference on Automated Planning and Scheduling, ICAPS (2016)Google Scholar
  9. 9.
    Moshref-Javadi, M., Lee, S.: A taxonomy to the class of minimum latency problems. In: Proceedings - IIE Annual Conference, pp. 3896. Institute of Industrial Engineers-Publisher (2013)Google Scholar
  10. 10.
    Rosenkrantz, D.J., Stearns, R.E., Lewis, P.M.: An analysis of several heuristics for the traveling salesman problem. In: Ravi, S.S., Shukla, S.K. (eds.) Fundamental Problems in Computing: Essays in Honor of Professor Daniel J. Rosenkrantz, pp. 45–69. Springer Science & Business Media, Dordrecht (2009)CrossRefGoogle Scholar
  11. 11.
    Talarico, L., Sörensen, K., Springael, J.: Metaheuristics for the risk-constrained cash-in-transit vehicle routing problem. Eur. J. Oper. Res. 244(2), 457–470 (2015)CrossRefzbMATHGoogle Scholar
  12. 12.
    Toth, P., Vigo, D.: Vehicle Routing: Problems, Methods, and Applications, vol. 18. Society for Industrial and Applied Mathematics, Philadelphia (2014)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Electrical Engineering and Computer ScienceSyracuse UniversitySyracuseUSA

Personalised recommendations