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Journal of Intelligent & Robotic Systems

, Volume 84, Issue 1–4, pp 469–492 | Cite as

Swarm Distribution and Deployment for Cooperative Surveillance by Micro-Aerial Vehicles

  • Martin Saska
  • Vojtěch Vonásek
  • Jan Chudoba
  • Justin Thomas
  • Giuseppe Loianno
  • Vijay Kumar
Article

Abstract

The task of cooperative surveillance of pre-selected Areas of Interest (AoI) in outdoor environments by groups of closely cooperating Micro Aerial Vehicles (MAVs) is tackled in this paper. In the cooperative surveillance mission, finding distributions of the MAVs in the environment to properly cover the AoIs and finding feasible trajectories to reach the obtained surveillance locations from the initial depot are crucial tasks that have to be fulfilled. In addition, motion constraints of the employed MAVs, environment constraints (e.g. non-fly zones), and constraints imposed by localization of members of the groups need to be satisfied in the planning process. We formulate the task of cooperative surveillance as a single high-dimensional optimization problem to be able to integrate all these requirements. Due to numerous constraints that have to be satisfied, we propose to solve the problem using an evolutionary-based optimization technique. An important aspect of the proposed method is that the cooperating MAVs are localized relatively to each other, rather than using a global localization system. This increases robustness of the system and its deploy-ability in scenarios, in which compact shapes of the MAV group with short relative distances are required.

Keywords

Micro-aerial vehicles Cooperative surveillance Swarms PSO Visual localization Motion planning Swarm coverage 

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References

  1. 1.
    Krajnik, T., Nitsche, M., Faigl, J., Vanek, P., Saska, M., Preucil, L., Duckett, T., Mejail, M.: A practical multirobot localization system. In: Accepted by Journal of Intelligent & Robotic Systems (2014)Google Scholar
  2. 2.
    Faigl, J., Krajník, T., Chudoba, J., Preucil, L., Saska, M.: Low-cost embedded system for relative localization in robotic swarms. In: Proc. of IEEE International Conference on Robotics and Automation (2013)Google Scholar
  3. 3.
    Saska, M., Chudoba, J., Precil, L., Thomas, J., Loianno, G., Tresnak, A., Vonasek, V., Kumar, V.: Autonomous deployment of swarms of micro-aerial vehicles in cooperative surveillance. In: 2014 International Conference on Unmanned Aircraft Systems (ICUAS) (2014)Google Scholar
  4. 4.
    Schmickl, T., Crailsheim, K.: Trophallaxis within a robotic swarm: bio-inspired communication among robots in a swarm. Autonom. Robots 25, 171–188 (2008)CrossRefGoogle Scholar
  5. 5.
    Teacy, W., Nie, J., McClean, S., Parr, G.: Maintaining connectivity in uav swarm sensing. In: IEEE GLOBECOM Workshops (2010)Google Scholar
  6. 6.
    Berman, S., Halasz, A., Hsieh, M., Kumar, V.: Optimized stochastic policies for task allocation in swarms of robots. IEEE Trans. Robot. 25(4), 927–937 (2009)CrossRefGoogle Scholar
  7. 7.
    Liu, W., Winfield, A., Sa, J., Chen, J., Dou, L.: Strategies for energy optimisation in a swarm of foraging robots. In: Swarm Robotics, vol. 4433, pp. 14–26 (2007)Google Scholar
  8. 8.
    Hamann, H., Worn, H.: A framework of space-time continuous models for algorithm design in swarm robotics. Swarm Intell. 2, 209–239 (2008)CrossRefGoogle Scholar
  9. 9.
    Winfield, A., Liu, W., Nembrini, J., Martinoli, A.: Modelling a wireless connected swarm of mobile robots. Swarm Intell. 2, 241–266 (2008)CrossRefGoogle Scholar
  10. 10.
    Saska, M., Vakula, J., Preucil, L: Swarms of micro aerial vehicles stabilized under a visual relative localization. In: ICRA2014: Proceedings of 2014 IEEE International Conference on Robotics and Automation (2014)Google Scholar
  11. 11.
    Kumar, M., Garg, D., Kumar, V.: Segregation of heterogeneous units in a swarm of robotic agents. IEEE Trans. Autom. Control 55(3), 743–748 (2010)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Saska, M: MAV-swarms: Unmanned aerial vehicles stabilized along a given path using onboard relative localization. In: Proceedings of 2015 International Conference on Unmanned Aircraft Systems (ICUAS) (2015)Google Scholar
  13. 13.
    Turpin, M., Michael, N., Kumar, V.: Trajectory design and control for aggressive formation flight with quadrotors. Autonom. Robots 33(1–2), 143–156 (2012). [Online]. Available. doi: 10.1007/s10514-012-9279-y CrossRefGoogle Scholar
  14. 14.
    Bennet, D. J., McInnes, C. R.: Verifiable control of a swarm of unmanned aerial vehicles. J. Aerospace Eng. 223(7), 939–953 (2009)Google Scholar
  15. 15.
    Barnes, L., Garcia, R., Fields, M., Valavanis, K.: Swarm formation control utilizing ground and aerial unmanned systems. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2008)Google Scholar
  16. 16.
    Holland, O., Woods, J., Nardi, R., Clark, A.: Beyond swarm intelligence: The UltraSwarm. In: IEEE Swarm Intelligence Symposium, pp. 217–224 (2005)Google Scholar
  17. 17.
    Brkle, A., Leuchter, S.: Development of micro uav swarms. In: Autonome Mobile Systeme 2009, ser. Informatik aktuell, pp. 217–224 (2009)Google Scholar
  18. 18.
    Cai, N., Xi, J.-X., Zhong, Y.-S.: Brief paper swarm stability of high-order linear time-invariant swarm systems. Control Theory Appl. IET 5(2), 402–408, 20 (2011)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Cheah, C.C., Hou, S.P., Slotine, J.J.E.: Region-based shape control for a swarm of robots. Automatica 45(10), 2406–2411 (2009)MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Cortes, J., Martinez, S., Karatas, T., Bullo, F.: Coverage control for mobile sensing networks. IEEE Trans. Robot. Autom 20(2), 243–255 (2004)CrossRefGoogle Scholar
  21. 21.
    Renzaglia, A., Doitsidis, L., Martinelli, A., Kosmatopoulos, E.: Adaptive-based distributed cooperative multi-robot coverage. In: American Control Conference (ACC) (2011)Google Scholar
  22. 22.
    Renzaglia, A., Doitsidis, L., Martinelli, A., Kosmatopoulos, E.: Adaptive-based, scalable design for autonomous multi-robot surveillance. In: IEEE CDC (2010)Google Scholar
  23. 23.
    Mathews, E., Graf, T., Kulathunga, K.S.S.B.: Biologically inspired swarm robotic network ensuring coverage and connectivity. In: 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 84–90 (2012)Google Scholar
  24. 24.
    Liu, J.-z., Wang, B.-l., Ao, J.-y., Wang, S., Wu, Q.: An immune-swarm intelligence based algorithm for deterministic coverage problems of wireless sensor networks. J. Central South Univ. 19(11), 3154–3161 (2012)CrossRefGoogle Scholar
  25. 25.
    Maza, I., Ollero, A.: Multiple uav cooperative searching operation using polygon area decomposition and efficient coverage algorithms. In: Alami, R., Chatila, R., Asama, H. (eds.) Distributed Autonomous Robotic Systems 6, pp 221–230. Springer, Japan (2007)CrossRefGoogle Scholar
  26. 26.
    Ha, T: The UAV Continuous Coverage Problem. Air Force Institute of Technology (2010)Google Scholar
  27. 27.
    Schwager, M., Julian, B. J., Rus, D.: Optimal coverage for multiple hovering robots with downward facing cameras. In: IEEE ICRA (2009)Google Scholar
  28. 28.
    Doitsidis, L., Renzaglia, A., Weiss, S., Kosmatopoulos, E., Scaramuzza, D., Siegwart, R: 3d surveillance coverage using maps extracted by a monocular slam algorithm. In: IEEE/RSJ IROS (2011)Google Scholar
  29. 29.
    Saska, M., Hess, M., Schilling, K.: Hierarchical spline path planning method for complex environments. In: Proc. of the 4th International Conference on Informatics in Control, Automation and Robotics (2007)Google Scholar
  30. 30.
    Saska, M., Hess, M., Schilling, K.: Voronoi strains - a spline path planning algorithm for complex environments. In: Proc. of the IASTED conference on Artificial Intelligence and Applications (2007)Google Scholar
  31. 31.
    Mouret, J.-B., Doncieux, S.: Encouraging behavioral diversity in evolutionary robotics: An empirical study. Evol. Comput. 20(1), 91–133 (2012)CrossRefGoogle Scholar
  32. 32.
    Vonasek, V., Saska, M., Kosnar, K., Preucil, L.: Global motion planning for modular robots with local motion primitives. In: IEEE ICRA (2013)Google Scholar
  33. 33.
    Saska, M., Vonasek, V., Krajnik, T., Preucil, L.: Coordination and navigation of heterogeneous MAV–UGV formations localized by a ‘hawk-eye’-like Approach Under A Model Predictive Control Scheme. Int. J. Robot. Res. 33(10), 1393–1412 (2014)CrossRefGoogle Scholar
  34. 34.
    Saska, M., Krajnik, T., Vonasek, V., Kasl, Z., Spurny, V., Preucil, L: Fault-tolerant formation driving mechanism designed for heterogeneous MAVs-UGVs groups. J. Intell. Robot. Syst. 73 (1–4), 603–622 (2014)CrossRefGoogle Scholar
  35. 35.
    Saska, M., Krajnik, T., Vonasek, V., Vanek, P., Preucil, L.: Navigation, localization and stabilization of formations of unmanned aerial and ground vehicles. In: Proceedings of 2013 International Conference on Unmanned Aircraft Systems (2013)Google Scholar
  36. 36.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. Proc. Int. Conf. Neural Netw. IEEE 4, 1942–1948 (1995)CrossRefGoogle Scholar
  37. 37.
    O’Rourke, J: Art Gallery Theorems and Algorithms. Oxford University Press (1987)Google Scholar
  38. 38.
    LaValle, S.M.: Rapidly-exploring random trees: A new tool for path planning. In: TR 98-11, Computer Science Dept. Iowa State University (1998)Google Scholar
  39. 39.
    Turpin, M., Michael, N., Kumar, V.: Concurrent assignment and planning of trajectories for large teams of interchangeable robots. In: International Conference on Robotics and Automation. Karlsruhe, Germany (2013)CrossRefGoogle Scholar
  40. 40.
    Lee, T., Leoky, M., McClamroch, N.: Geometric tracking control of a quadrotor uav on se(3). In: 49th IEEE Conference on Decision and Control (CDC) (2010)Google Scholar
  41. 41.
    Saska, M., Baca, T., Thomas, J., Chudoba, J., Preucil, L., Krajnik, T., Faigl, J., Loianno, G., Kumar, V.: System for deployment of groups of micro aerial vehicles in gps-denied environments using onboard visual relative localization. Accepted for Autonomous Robots (2016)Google Scholar
  42. 42.
    Movies, Movies of experiments of the mav cooperative surveillance. http://imr.felk.cvut.cz/mavsurveillance/ [online] [cit. 2014-12-12]

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Martin Saska
    • 1
  • Vojtěch Vonásek
    • 1
  • Jan Chudoba
    • 1
  • Justin Thomas
    • 2
  • Giuseppe Loianno
    • 2
  • Vijay Kumar
    • 2
  1. 1.Department of Cybernetics, Faculty of Electrical EngineeringCzech Technical University in PraguePragueCzech Republic
  2. 2.GRASP LabUniversity of PennsylvaniaPhiladelphiaUSA

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