Autonomous Robots

, Volume 41, Issue 4, pp 919–944 | Cite as

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

  • Martin SaskaEmail author
  • Tomas Baca
  • Justin Thomas
  • Jan Chudoba
  • Libor Preucil
  • Tomas Krajnik
  • Jan Faigl
  • Giuseppe Loianno
  • Vijay Kumar


A complex system for control of swarms of micro aerial vehicles (MAV), in literature also called as unmanned aerial vehicles (UAV) or unmanned aerial systems (UAS), stabilized via an onboard visual relative localization is described in this paper. The main purpose of this work is to verify the possibility of self-stabilization of multi-MAV groups without an external global positioning system. This approach enables the deployment of MAV swarms outside laboratory conditions, and it may be considered an enabling technique for utilizing fleets of MAVs in real-world scenarios. The proposed visual-based stabilization approach has been designed for numerous different multi-UAV robotic applications (leader-follower UAV formation stabilization, UAV swarm stabilization and deployment in surveillance scenarios, cooperative UAV sensory measurement) in this paper. Deployment of the system in real-world scenarios truthfully verifies its operational constraints, given by limited onboard sensing suites and processing capabilities. The performance of the presented approach (MAV control, motion planning, MAV stabilization, and trajectory planning) in multi-MAV applications has been validated by experimental results in indoor as well as in challenging outdoor environments (e.g., in windy conditions and in a former pit mine).


Micro aerial vehicles (MAVs) Unmanned aerial vehicles (UAVs) Formations Swarms Visual relative localization Stabilization Control Trajectory planning 



This work has been supported by the Ministry of Education of the Czech Republic under project no. LH11053 and the experimental works required for paper revisions by Project No. HS 13167/830/8301616C000 founded by Khalifa University for the MBZIRC competition, both projects supporting the joint research of the Czech Technical University in Prague and the University of Pennsylvania. In addition, Martin Saska has been supported by the Grant Agency of the Czech Republic under postdoc Grant No. P103-12/P756. The work of Jan Faigl has been also partially supported by the Czech Science Foundations (GACR) under the research Project No. 13-18316P. Tomas Krajnik has been supported by the EU project STRANDS (ICT-600623). Tomas Baca has been supported by CTU grant no. SGS15/157/OHK3/2T/13. Final experiments done by Martin Saska for revisions of the paper have been supported by the Czech Science Foundations (GACR) under the research Project No. 16-24206S.


  1. Balch, T., & Hybinette, M. (2000). Social potentials for scalable multi-robot formations. In Proceedings of IEEE conference on robotics and automation.Google Scholar
  2. Ballard, D. H. (1981). Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition, 13(2), 111–122.CrossRefzbMATHGoogle Scholar
  3. Barfoot, T. D., & Clark, C. M. (2004). Motion planning for formations of mobile robots. Robotics and Autonomous Systems, 46, 65–78.CrossRefGoogle Scholar
  4. Barnes, L., Garcia, R., Fields, M., & Valavanis, K. (2008). Swarm formation control utilizing ground and aerial unmanned systems. In IEEE/RSJ international conference on intelligent robots and systems.Google Scholar
  5. Bennet, D. J., & McInnes, C. R. (2009). Verifiable control of a swarm of unmanned aerial vehicles. Journal of Aerospace Engineering, 223(7), 939–953.Google Scholar
  6. Berman, S., Halasz, A., Hsieh, M., & Kumar, V. (2009). Optimized stochastic policies for task allocation in swarms of robots. IEEE Transactions on Robotics, 25(4), 927–937.CrossRefGoogle Scholar
  7. Bošnak, M., Matko, D., & Blažič, S. (2012). Quadrocopter control using an on-board video system with off-board processing. Robotics and Autonomous Systems, 60(4), 657–667.CrossRefGoogle Scholar
  8. Buerkle, A., & Leuchter, S. (2009). Development of micro UAV swarms. Autonome mobile systeme 2009 (pp. 217–224)., Informatik aktuell series Berlin: Springer.CrossRefGoogle Scholar
  9. Cai, W., Yu, Q., & Wang, H. (2004). A fast contour-based approach to circle and ellipse detection. In 5th world congress on intelligent control and automation (WCICA).Google Scholar
  10. Cai, N., Xi, J.-X., & Zhong, Y.-S. (2011). Swarm stability of high-order linear time-invariant swarm systems. Control Theory Applications IET, 5(2), 402–408.MathSciNetCrossRefGoogle Scholar
  11. Carreras, M., Ridao, P., García, R., & Nicosevici, T. (2003). Vision-based localization of an underwater robot in a structured environment. In ICRA.Google Scholar
  12. Chao, Z., Zhou, S.-L., Ming, L., & Zhang, W.-G. (2012). UAV formation flight based on nonlinear model predictive control. Mathematical Problems in Engineering, 2012(1), 1–16.CrossRefzbMATHGoogle Scholar
  13. Cheah, C. C., Hou, S. P., & Slotine, J. J. E. (2009). Region-based shape control for a swarm of robots. Automatica, 45(10), 2406–2411.MathSciNetCrossRefzbMATHGoogle Scholar
  14. Christensen, A., O’Grady, R., & Dorigo, M. (2009). From fireflies to fault-tolerant swarms of robots. IEEE Transactions on Evolutionary Computation, 13(4), 754–766.CrossRefGoogle Scholar
  15. Doitsidis, L., Weiss, S., Renzaglia, A., Kosmatopoulos, E., Siegwart, R., Scaramuzza, D., et al. (2012). Optimal surveillance coverage for teams of micro aerial vehicles in GPS-denied environments using onboard vision. Autonomous Robots, 33(1–2), 173–188.CrossRefGoogle Scholar
  16. Faigl, J., Krajník, T., Chudoba, J., Preucil, L., Saska, M. (2013). Low-cost embedded system for relative localization in robotic swarms. In Proceedings of IEEE international conference on robotics and automation.Google Scholar
  17. Faigl, J., Krajník, T., Vonásek, V., & Přeučil, L. (2012). On Localization Uncertainty in an Autonomous Inspection. In IEEE international conference on robotics and automation (ICRA).Google Scholar
  18. Fazli, P., Davoodi, A., & Mackworth, A. (2013). Multi-robot repeated area coverage. Autonomous Robots, 34(4), 251–276.CrossRefGoogle Scholar
  19. Filho, C., Lima Neto, F., Lins, A., Nascimento, A., & Lima, M. (2009). fish school search. Nature-inspired algorithms for optimisation, studies in computational intelligence (pp. 261–277). Berlin: Springer.CrossRefGoogle Scholar
  20. Garca Carrillo, L., Sanchez, A., Dzul, A., & Lozano, R. (2011). Stabilization and trajectory tracking of a quad-rotor using vision. Journal of Intelligent & Robotic Systems, 61, 103–118.CrossRefGoogle Scholar
  21. Hamann, H., & Worn, H. (2008). A framework of spacetime continuous models for algorithm design in swarm robotics. Swarm Intelligence, 2, 209–239.CrossRefGoogle Scholar
  22. Holland, O., Woods, J., Nardi, R., & Clark, A. (2005). Beyond swarm intelligence: The UltraSwarm. In IEEE swarm intelligence symposium.Google Scholar
  23. Jia, L.-Q., Liu, H.-M., Wang, Z.-H., & Chen, H. (2011). An effective non-HT circle detection for centers and radii. In ICMLC.Google Scholar
  24. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks (Vol. 4).Google Scholar
  25. Kloetzer, M., & Belta, C. (2007). Temporal logic planning and control of robotic swarms by hierarchical abstractions. IEEE Transactions on Robotics, 23(2), 320–330.CrossRefGoogle Scholar
  26. Krajník, T., Nitsche, M., Faigl, J., Vanek, P., Saska, M., Přeučil, L., Duckett, T., & Mejail, M. (2014). A practical multirobot localization system. Journal of Intelligent & Robotic Systems, Online first, 2014.
  27. Kumar, M., Garg, D., & Kumar, V. (2010). Segregation of heterogeneous units in a swarm of robotic agents. IEEE Transactions on Automatic Control, 55(3), 743–748.MathSciNetCrossRefGoogle Scholar
  28. Lange, S., Sunderhauf, N., & Protzel, P. (2009). A vision based onboard approach for landing and position control of an autonomous multirotor uav in GPS-denied environments. In International conference on advanced robotics (ICAR).Google Scholar
  29. Lee, T., Leoky, M., & McClamroch, N. (2010). Geometric tracking control of a quadrotor UAV on se(3). In 49th IEEE conference on decision and control (CDC).Google Scholar
  30. Leonard, N., & Fiorelli, E. (2001). Virtual leaders, artificial potentials and coordinated control of groups. In Proceedings of the 40th IEEE Conference on Decision and Control.Google Scholar
  31. Liu, C., Chen, W.-H., & Andrews, J. (2011). Piecewise constant model predictive control for autonomous helicopters. Robotics and Autonomous Systems, 59(78), 571–579.CrossRefGoogle Scholar
  32. Liu, W., Winfield, A., Sa, J., Chen, J., & Dou, L. (2007). Strategies for energy optimisation in a swarm of foraging robots. Swarm Robotics, 4433, 14–26.CrossRefGoogle Scholar
  33. Marjovi, A., & Marques, L. (2013). Optimal spatial formation of swarm robotic gas sensors in odor plume finding. Autonomous Robots, 35(2–3), 93–109.CrossRefGoogle Scholar
  34. Masselli, A., & Zell, A. (2012). A novel marker based tracking method for position and attitude control of MAVs. In Proceedings of international micro air vehicle conference and flight competition.Google Scholar
  35. Michael, N., Mellinger, D., Lindsey, Q., & Kumar, V. (2010). The grasp multiple micro-UAV testbed. IEEE Robotics Automation Magazine, 17(3), 56–65.CrossRefGoogle Scholar
  36. Multimedia. (2015). Various experiments with multi-MAV system verifying the proposed approach. Retrieved from 8 August 2015.
  37. No, T. S., Kim, Y., Tahk, M.-J., & Jeon, G.-E. (2011). Cascade-type guidance law design for multiple-UAV formation keeping. Aerospace Science and Technology, 15(6), 431–439.CrossRefGoogle Scholar
  38. Rad, A. A., Faez, K., & Qaragozlou, N. (2003). Fast circle detection using gradient pair vectors. In DICTA.Google Scholar
  39. Saska, M., Chudoba, J., Preucil, L., Thomas, J., Loianno, G., Tresnak, A., Vonasek, V., & Kumar, V. (2014a). Autonomous deployment of swarms of micro-aerial vehicles in cooperative surveillance. In Proceedings of 2014 international conference on unmanned aircraft systems (ICUAS).Google Scholar
  40. Saska, M., Hess, M., & Schilling, K. (2007). Hierarchical spline path planning method for complex environments. In Proceedings of the 4th international conference on informatics in control, automation and robotics. Angers, France.Google Scholar
  41. Saska, M., Kasl, Z., Preucil, L. (2014b). Motion planning and control of formations of micro aerial vehicles. In Proceedings of the 19th world congress of the international federation of automatic control.Google Scholar
  42. Saska, M., Mejia, J. S., Stipanovic, D. M., Schilling, K. (2009). Control and navigation of formations of car-like robots on a receding horizon. In Proceedings of 3rd IEEE multi-conference on systems and control.Google Scholar
  43. Saska, M., Vonasek, V., & Preucil, L. (2010). Control of ad-hoc formations for autonomous airport snow shoveling. In IEEE/RSJ international conference on intelligent robots and systems (Vol. 1, pp. 4995–5000). Taipei: IEEE Industrial Electronics Society.Google Scholar
  44. Saska, M., Krajnik, T., Vonasek, V., Kasl, Z., Spurny, V., & Preucil, L. (2014c). Fault-tolerant formation driving mechanism designed for heterogeneous MAVs-UGVs groups. Journal of Intelligent and Robotic Systems, 73(1–4), 603–622.Google Scholar
  45. Saska, M., Mejia, J., Stipanovic, D., Vonasek, V., Schilling, K., & Preucil, L. (2013a). Control and navigation in manoeuvres of formations of unmanned mobile vehicles. European Journal of Control, 19(2), 157–171.MathSciNetCrossRefzbMATHGoogle Scholar
  46. Saska, M., Spurny, V., & Vonasek, V. (2016). Predictive control and stabilization of nonholonomic formations with integrated spline-path planning. Robotics and Autonomous Systems, 75, 379–397.CrossRefGoogle Scholar
  47. Saska, M., Vonasek, V., Krajnik, T., & Preucil, L. (2014d). Coordination and navigation of heterogeneous MAV&UGV formations localized by a hawk-eye-like approach under a model predictive control scheme. International Journal of Robotics Research, 33(10), 1393–1412.Google Scholar
  48. Saska, M., Vonasek, V., & Preucil, L. (2013b). Trajectory planning and control for airport snow sweeping by autonomous formations of ploughs. Journal of Intelligent and Robotic Systems, 72(2), 239–261.CrossRefGoogle Scholar
  49. Schmickl, T., & Crailsheim, K. (2008). Trophallaxis within a robotic swarm: bio-inspired communication among robots in a swarm. Autonomous Robots, 25, 171–188.CrossRefGoogle Scholar
  50. Sharma, R. K., & Ghose, D. (2009). Collision avoidance between UAV clusters using swarm intelligence techniques. International Journal of Systems Science, 40, 521–538.MathSciNetCrossRefzbMATHGoogle Scholar
  51. Teacy, W., Nie, J., McClean, S., & Parr, G. (2010). Maintaining connectivity in UAV swarm sensing. In IEEE GLOBECOM Workshops.Google Scholar
  52. Trianni, V. (2008). Evolutionary swarm robotics. New York: Springer.CrossRefGoogle Scholar
  53. Turpin, M., Michael, N., & Kumar, V. (2012). Trajectory design and control for aggressive formation flight with quadrotors. Autonomous Robots, 33(1–2), 143–156.CrossRefGoogle Scholar
  54. Winfield, A., Liu, W., Nembrini, J., & Martinoli, A. (2008). Modelling a wireless connected swarm of mobile robots. Swarm Intelligence, 2, 241–266.CrossRefGoogle Scholar
  55. Yang, S., Scherer, S. A., & Zell, A. (2012). An onboard monocular vision system for autonomous takeoff, hovering and landing of a micro aerial vehicle. In International conference on unmanned aircraft systems (ICUAS’12).Google Scholar
  56. Yang, S., Scherer, S., & Zell, A. (2012). An Onboard monocular vision system for autonomous takeoff, hovering and landing of a micro aerial vehicle. Journal of Intelligent & Robotic Systems, 69(1–4), 499–515.Google Scholar
  57. Yu, H., & Beard, R. (2013). A vision-based collision avoidance technique for micro air vehicles using local-level frame mapping and path planning. Autonomous Robots, 34(1–2), 93–109.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Martin Saska
    • 1
    Email author
  • Tomas Baca
    • 1
  • Justin Thomas
    • 2
  • Jan Chudoba
    • 1
  • Libor Preucil
    • 1
  • Tomas Krajnik
    • 3
  • Jan Faigl
    • 4
  • Giuseppe Loianno
    • 2
  • Vijay Kumar
    • 2
  1. 1.Dept. of Cybernetics, Faculty of Electrical EngineeringCzech Technical University in PraguePragueCzech Republic
  2. 2.GRASP LabUniversity of PennsylvaniaPhiladelphiaUSA
  3. 3.Lincoln Centre for Autonomous SystemsUniversity of LincolnLincolnUK
  4. 4.Dept. of Computer ScienceFaculty of Electrical Engineering Czech Technical University in PraguePragueCzech Republic

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