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Distributed assignment with limited communication for multi-robot multi-target tracking

  • Yoonchang SungEmail author
  • Ashish Kumar Budhiraja
  • Ryan K. Williams
  • Pratap Tokekar
Article
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Part of the following topical collections:
  1. Special Issue on Robot Communication Challenges: Real-World Problems, Systems, and Methods

Abstract

We study the problem of tracking multiple moving targets using a team of mobile robots. Each robot has a set of motion primitives to choose from in order to collectively maximize the number of targets tracked or the total quality of tracking. Our focus is on scenarios where communication is limited and the robots have limited time to share information with their neighbors. As a result, we seek distributed algorithms that can find solutions in a bounded amount of time. We present two algorithms: (1) a greedy algorithm that is guaranteed to find a 2-approximation to the optimal (centralized) solution but requiring |R| communication rounds in the worst case, where |R| denotes the number of robots, and (2) a local algorithm that finds a \(\mathcal {O}\left( (1+\epsilon )(1+1/h)\right) \)—approximation algorithm in \(\mathcal {O}(h\log 1/\epsilon )\) communication rounds. Here, h and \(\epsilon \) are parameters that allow the user to trade-off the solution quality with communication time. In addition to theoretical results, we present empirical evaluation including comparisons with centralized optimal solutions.

Keywords

Multi-robot system Task assignment Distributed algorithm 

Notes

Acknowledgements

The authors would like to thank Dr. Jukka Suomela from Aalto University for fruitful discussion.

References

  1. Ahmad, A., Lawless, G., & Lima, P. (2017). An online scalable approach to unified multirobot cooperative localization and object tracking. IEEE Transactions on Robotics, 33(5), 1184–1199.Google Scholar
  2. Angluin, D. (1980) Local and global properties in networks of processors. In Proceedings of the twelfth annual ACM symposium on theory of computing. ACM, (pp. 82–93).Google Scholar
  3. Åstrand, M., & Suomela, J. (2010) Fast distributed approximation algorithms for vertex cover and set cover in anonymous networks. In Proceedings of the twenty-second annual ACM symposium on parallelism in algorithms and architectures. ACM, (pp. 294–302).Google Scholar
  4. Åstrand, M., Floréen, P., Polishchuk, V., Rybicki, J., Suomela, J., & Uitto, J. (2009) A local 2-approximation algorithm for the vertex cover problem. In International symposium on distributed computing. Springer (pp. 191–205).Google Scholar
  5. Bandyopadhyay, S., Chung, S.-J., & Hadaegh, F. Y. (2017). Probabilistic and distributed control of a large-scale swarm of autonomous agents. IEEE Transactions on Robotics, 33(5), 1103–1123.Google Scholar
  6. Banfi, J., Guzzi, J., Amigoni, F., Flushing, E. F., Giusti, A., Gambardella, L., & Di Caro, G. A. (2018) An integer linear programming model for fair multitarget tracking in cooperative multirobot systems. Autonomous Robots, pp. 1–16.Google Scholar
  7. Best, G., Forrai, M., Mettu, R. R., & Fitch, R. (2018). Planning-aware communication for decentralised multi-robot coordination. In Proceedings of the international conference on robotics and automation, Brisbane, Australia, (Vol. 21).Google Scholar
  8. Capitan, J., Spaan, M. T., Merino, L., & Ollero, A. (2013). Decentralized multi-robot cooperation with auctioned pomdps. The International Journal of Robotics Research, 32(6), 650–671.Google Scholar
  9. Charrow, B., Kumar, V., & Michael, N. (2014). Approximate representations for multi-robot control policies that maximize mutual information. Autonomous Robots, 37(4), 383–400.Google Scholar
  10. Choi, H.-L., Brunet, L., & How, J. P. (2009). Consensus-based decentralized auctions for robust task allocation. IEEE Transactions on Robotics, 25(4), 912–926.Google Scholar
  11. Chung, S.-J., Paranjape, A., Dames, P., Shen, S., & Kumar, V. (2018). A Survey on Aerial Swarm Robotics. IEEE Transactions on Robotics.Google Scholar
  12. Dimarogonas, D. V., Frazzoli, E., & Johansson, K. H. (2012). Distributed event-triggered control for multi-agent systems. IEEE Transactions on Automatic Control, 57(5), 1291–1297.MathSciNetzbMATHGoogle Scholar
  13. Floréen, P., Hassinen, M., Kaasinen, J., Kaski, P., Musto, T., & Suomela, J. (2011). Local approximability of max-min and min-max linear programs. Theory of Computing Systems, 49(4), 672–697.MathSciNetzbMATHGoogle Scholar
  14. Ge, X., & Han, Q.-L. (2017). Distributed formation control of networked multi-agent systems using a dynamic event-triggered communication mechanism. IEEE Transactions on Industrial Electronics, 64(10), 8118–8127.Google Scholar
  15. Gerkey, B. P., & Matarić, M. J. (2004). A formal analysis and taxonomy of task allocation in multi-robot systems. The International Journal of Robotics Research, 23(9), 939–954.Google Scholar
  16. Ge, X., Yang, F., & Han, Q.-L. (2017). Distributed networked control systems: A brief overview. Information Sciences, 380, 117–131.Google Scholar
  17. Gharesifard, B. & Smith, S. L. (2017). Distributed submodular maximization with limited information. In IEEE transactions on control of network systems.Google Scholar
  18. Guo, M., & Zavlanos, M. M. (2018). Multirobot data gathering under buffer constraints and intermittent communication. IEEE transactions on robotics.Google Scholar
  19. Hanckowiak, M., Karonski, M., & Panconesi, A. (2001). On the distributed complexity of computing maximal matchings. SIAM Journal on Discrete Mathematics, 15(1), 41–57.MathSciNetzbMATHGoogle Scholar
  20. Hönig, W., & Ayanian, N. (2016) Dynamic multi-target coverage with robotic cameras. In IEEE RSJ International conference on intelligent robots and systems (IROS) (pp. 1871–1878).Google Scholar
  21. Howard, T., Pivtoraiko, M., Knepper, R. A., & Kelly, A. (2014). Model-predictive motion planning: Several key developments for autonomous mobile robots. IEEE Robotics and Automation Magazine, 21(1), 64–73.Google Scholar
  22. Kanakia, A., Touri, B., & Correll, N. (2016). Modeling multi-robot task allocation with limited information as global game. Swarm Intelligence, 10(2), 147–160.Google Scholar
  23. Kantaros, Y., Thanou, M., & Tzes, A. (2015). Distributed coverage control for concave areas by a heterogeneous robot-swarm with visibility sensing constraints. Automatica, 53, 195–207.MathSciNetzbMATHGoogle Scholar
  24. Kantaros, Y., & Zavlanos, M. M. (2016). Global planning for multi-robot communication networks in complex environments. IEEE Transactions on Robotics, 32(5), 1045–1061.Google Scholar
  25. Kantaros, Y., & Zavlanos, M. M. (2017). Distributed intermittent connectivity control of mobile robot networks. IEEE Transactions on Automatic Control, 62(7), 3109–3121.MathSciNetzbMATHGoogle Scholar
  26. Kassir, A., Fitch, R., & Sukkarieh, S. (2016) Communication-efficient motion coordination and data fusion in information gathering teams. In 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, (pp. 5258–5265).Google Scholar
  27. Khan, A., Rinner, B., & Cavallaro, A. (2016) Cooperative robots to observe moving targets: Review, IEEE transactions on cybernetics.Google Scholar
  28. Kolling, A., & Carpin, S. (2007). Cooperative observation of multiple moving targets: an algorithm and its formalization. The International Journal of Robotics Research, 26(9), 935–953.Google Scholar
  29. Korsah, G. A., Stentz, A., & Dias, M. B. (2013). A comprehensive taxonomy for multi-robot task allocation. The International Journal of Robotics Research, 32(12), 1495–1512.Google Scholar
  30. Kuhn, F., Moscibroda, T., & Wattenhofer, R. (2006) The price of being near-sighted. In Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm. Society for Industrial and Applied Mathematics, (pp. 980–989).Google Scholar
  31. Le Ny, J., Ribeiro, A., & Pappas, G. J. (2012). Adaptive communication-constrained deployment of unmanned vehicle systems. IEEE Journal on Selected Areas in Communications, 30(5), 923–934.Google Scholar
  32. Lenzen, C., & Wattenhofer, R. (2010) Minimum dominating set approximation in graphs of bounded arboricity. In International symposium on distributed computing. Springer, (pp. 510–524).Google Scholar
  33. Li, H., Chen, G., Huang, T., & Dong, Z. (2017). High-performance consensus control in networked systems with limited bandwidth communication and time-varying directed topologies. IEEE Transactions on Neural Networks and Learning Systems, 28(5), 1043–1054.Google Scholar
  34. Linial, N. (1992). Locality in distributed graph algorithms. SIAM Journal on Computing, 21(1), 193–201.MathSciNetzbMATHGoogle Scholar
  35. Liu, L., & Shell, D. A. (2011). Assessing optimal assignment under uncertainty: An interval-based algorithm. The International Journal of Robotics Research, 30(7), 936–953.Google Scholar
  36. Luo, L., Chakraborty, N., & Sycara, K. (2015). Distributed algorithms for multirobot task assignment with task deadline constraints. IEEE Transactions on Automation Science and Engineering, 12(3), 876–888.Google Scholar
  37. Morgan, D., Subramanian, G. P., Chung, S.-J., & Hadaegh, F. Y. (2016). Swarm assignment and trajectory optimization using variable-swarm, distributed auction assignment and sequential convex programming. The International Journal of Robotics Research, 35(10), 1261–1285.Google Scholar
  38. Naor, M., & Stockmeyer, L. (1995). What can be computed locally? SIAM Journal on Computing, 24(6), 1259–1277.MathSciNetzbMATHGoogle Scholar
  39. Nemhauser, G. L., Wolsey, L. A., & Fisher, M. L. (1978). An analysis of approximations for maximizing submodular set functions–1. Mathematical programming, 14(1), 265–294.MathSciNetzbMATHGoogle Scholar
  40. Niehsen, W. (2002) Information fusion based on fast covariance intersection filtering. In Proceedings of the fifth international conference on information fusion, 2002, vol. 2. IEEE, (pp. 901–904).Google Scholar
  41. Otte, M., & Correll, N. (2013). Any-com multi-robot path-planning: Maximizing collaboration for variable bandwidth. In A. Martinoli, F. Mondada, N. Correll, G. Mermoud, M. Egerstedt, M. A. Hsieh, L. E. Parker, & K. Støy (Eds.), Distributed autonomous robotic systems (pp. 161–173), Springer.Google Scholar
  42. Otte, M., Kuhlman, M., & Sofge, D. (2017) Multi-robot task allocation with auctions in harsh communication environments. In International symposium on multi-robot and multi-agent systems (MRS) 2017. IEEE, (pp. 32–39).Google Scholar
  43. Otte, M., & Correll, N. (2018). Dynamic teams of robots as ad hoc distributed computers: Reducing the complexity of multi-robot motion planning via subspace selection. Autonomous Robots, 42(8), 1691–1713.Google Scholar
  44. Parker, L.E., & Emmons, B. A. (1997) Cooperative multi-robot observation of multiple moving targets. In Proceedings IEEE International conference on robotics and automation, vol. 3 (pp. 2082–2089).Google Scholar
  45. Parker, L. E. (2002). Distributed algorithms for multi-robot observation of multiple moving targets. Autonomous robots, 12(3), 231–255.zbMATHGoogle Scholar
  46. Pimenta, L. C., Schwager, M., Lindsey, Q., Kumar, V., Rus, D., Mesquita, R. C., & Pereira, G. A. (2009). Simultaneous coverage and tracking (scat) of moving targets with robot networks. In G. S. Chirikjian, H. Choset, M. Morales, & T. Murphey (Eds.), Algorithmic foundation of robotics VIII (pp. 85–99). Springer.Google Scholar
  47. Robin, C., & Lacroix, S. (2016). Multi-robot target detection and tracking: Taxonomy and survey. Autonomous Robots, 40(4), 729–760.Google Scholar
  48. Sung, Y., Budhiraja, A. K., Williams, R. K., & Tokekar, P. (2018) Distributed simultaneous action and target assignment for multi-robot multi-target tracking. In 2018 IEEE International conference on robotics and automation (ICRA) (pp. 1–9).Google Scholar
  49. Suomela, J. (2013). Survey of local algorithms. ACM Computing Surveys (CSUR), 45(2), 24.zbMATHGoogle Scholar
  50. Tokekar, P., Isler, V., & Franchi, A. (2014) Multi-target visual tracking with aerial robots. In 2014 IEEE RSJ International conference on intelligent robots and systems (pp. 3067–3072).Google Scholar
  51. Tomlab: Optimization environment large-scale optimization in matlab. http://tomopt.com/docs/quickguide/quickguide006.php, Accessed 3 Jan 2017.
  52. Touzet, C. F. (2000). Robot awareness in cooperative mobile robot learning. Autonomous Robots, 8(1), 87–97.Google Scholar
  53. Turpin, M., Michael, N., & Kumar, V. (2014). Capt: Concurrent assignment and planning of trajectories for multiple robots. The International Journal of Robotics Research, 33(1), 98–112.Google Scholar
  54. Vander Hook, J., Tokekar, P., & Isler, V. (2015). Algorithms for cooperative active localization of static targets with mobile bearing sensors under communication constraints. IEEE Transactions on Robotics, 31(4), 864–876.Google Scholar
  55. Vazirani, V. (2001). Approximation algorithms. Berlin: Springer.zbMATHGoogle Scholar
  56. Williams, R. K., Gasparri, A., Sukhatme, G. S., & Ulivi, G. (2015) Global connectivity control for spatially interacting multi-robot systems with unicycle kinematics. In 2015 IEEE international conference on robotics and automation (ICRA). IEEE, (pp. 1255–1261).Google Scholar
  57. Williams, R. K., & Sukhatme, G. S. (2013). Constrained interaction and coordination in proximity-limited multiagent systems. IEEE Transactions on Robotics, 29(4), 930–944.Google Scholar
  58. Xu, Z., Fitch, R., Underwood, J., & Sukkarieh, S. (2013). Decentralized coordinated tracking with mixed discrete-continuous decisions. Journal of Field Robotics, 30(5), 717–740.Google Scholar
  59. Yan, Z., Jouandeau, N., & Cherif, A. A. (2013). A survey and analysis of multi-robot coordination. International Journal of Advanced Robotic Systems, 10(12), 399.Google Scholar
  60. Young, N. E. (2001) Sequential and parallel algorithms for mixed packing and covering. In Proceedings 42nd IEEE symposium on foundations of computer science (pp. 538–546).Google Scholar
  61. Yu, H., Meier, K., Argyle, M., & Beard, R. W. (2015). Cooperative path planning for target tracking in urban environments using unmanned air and ground vehicles. IEEE/ASME Transactions on Mechatronics, 20(2), 541–552.Google Scholar
  62. Zavlanos, M. M., Egerstedt, M. B., & Pappas, G. J. (2011). Graph-theoretic connectivity control of mobile robot networks. Proceedings of the IEEE, 99(9), 1525–1540.Google Scholar
  63. Zhou, K., Roumeliotis, S. I., et al. (2011). Multirobot active target tracking with combinations of relative observations. IEEE Transactions on Robotics, 27(4), 678–695.Google Scholar
  64. Zhou, L., & Tokekar, P. (2018). Active target tracking with self-triggered communications in multi-robot teams. IEEE Transactions on Automation Science and Engineering, 99, 1–12.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringVirginia TechBlacksburgUSA

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