Skip to main content

Advertisement

Springer Nature Link
Log in

A Coordinated Navigation Strategy for Multi-Robots to Capture a Target Moving with Unknown Speed

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

The paper proposes an algorithm for multi-robot coordination and navigation in order to intercept a target at a long distance. For this purpose, a limit cycle based algorithm using a neural oscillator with phase differences is proposed. The state of target is unknown, under the assumption that it is stationary or in motion with constant unknown speed along a straight line. Using the proposed algorithm, a group of robots is intended to move towards the target in such a way that the robots surround it. While moving to the target, self-collision between the robots is avoided. Moreover, a collision avoidance with static obstacles as well as dynamic target is realized. The robots reach the target at a desired distance, keeping uniformly distributed angles around the target. The algorithm is further extended so that a static interception point for the target can be estimated in place of pursuing a dynamic target, which is referred to as a virtual target in this paper. In other words, the robots move towards the virtual target instead of the actual target. The robots ultimately encircle the actual target when they arrive at the virtual target. The effectiveness of the proposed method is verified through simulation results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Paley, D.A., et al.: Collective motion, sensor networks, and ocean sampling. In: Proceeding of the IEEE, vol. 95, pp 48–74 (2007)

  2. Triplett, B.I., et al.: Cooperative estimation for coordinated target tracking in a cluttered environment. Mobile Networks and Applications 14(3), 336–349 (2009)

    Article  Google Scholar 

  3. Franchi, A., et al.: Distributed target localization and encirclement with a multi-robot system. In: 7th IFAC Symposium on Intelligent Autonomous Vehicles (2010)

  4. Manzoor, S., et al.: Neural oscillator-based multi-robot coordination algorithm to catch-observe-protect a target. In: IEEE International Conference on Mechatronics and Automation (2015)

  5. Jennings, J.S., et al.: Cooperative search and rescue with a team of mobile robots. IEEE Int. Conf. on Robotics and Automation, 193–200 (1997)

  6. Dong, W., et al.: Cooperative control under-actuated surface vessels. IET Control Theory and Applications 4(9), 1569–1580 (2010)

    Article  MathSciNet  Google Scholar 

  7. Bai, H., Wen, J.T.: Cooperative load transport: a formation-control perspective. IEEE Trans. Robot. 26(4), 742–750 (2010)

    Article  Google Scholar 

  8. Wang, Y., et al.: Cooperative control of multi-missile systems. IET Control Theory and Applications 9, 441–440 (2014)

    Article  MathSciNet  Google Scholar 

  9. Werfel, J., et al.: Designing collective behavior in a termite-inspired robot construction team. Science 343, 754–758 (2014). doi:http://dx.doi.org/10.1126/science.1245842

    Article  Google Scholar 

  10. Das, G.P., et al.: A distributed task allocation algorithm for a multi-robot system in healthcare facilities. J. Intell. Robot. Syst. 80(1), 33–58 (2015)

    Article  Google Scholar 

  11. Schenato, L., Oh, S., Sastry, S., Bose, P.: Swarm coordination for pursuit evasion games using sensor networks. IEEE Int. Conf. on Robotics and Automation, 2493–2498 (2005)

  12. Saber, R.O.: Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Trans. Autom. Control 51(3), 401–420 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  13. O’Grady, R., et al.: Self-assembly strategies in a group of autonomous mobile robots. Springers Autonomous Robots 28(4), 439–455 (2010)

    Article  Google Scholar 

  14. Liu, S., Sun, D., Zhu, C.: Coordinated motion planning for multiple mobile robots along designed paths with formation requirement. IEEE/ASME Transactions on Mechatronics 16(16), 1021–1031 (2011)

    Article  Google Scholar 

  15. Jiang, Q., Kumar, V.: The inverse kinematics of cooperative transport with multiple aerial robots. IEEE Trans. Robot. 29(1), 136–145 (2013)

    Article  Google Scholar 

  16. Dutta, R., et al.: A cooperative formation control strategy maintaining connectivity of a multi-agent system. EEE/RSJ Int. Conf. on Intelligent Robots and Systems, 1189–1194 (2014)

  17. Franchi, A., et al.: Decentralized multi-robot encirclement of a 3D target with guaranteed collision avoidance. Springers Autonomous Robots, 11 (2015). First online

  18. Moors, M., et al.: A probabilistic approach to coordinated multi-robot indoor surveillance. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 3447–3452 (2005)

  19. Balch, T., Arkin, R.C.: Behavior-based formation control for multi-robot teams. IEEE Transitions on Robotics and Automation 14(6), 926–939 (1998)

    Article  Google Scholar 

  20. Erdogmus, P., et al.: Heuristic optimization algorithms in robotics. Serial and Parallel Robot Manipulators - Kinematics, Dynamics, Control and Optimization, InTech Publisher, 311–338 (2012)

  21. Desai, J.P.: A graph theoretic approach for modeling mobile robot team formations. J. Robot. Syst. 19(11), 511–525 (2002)

    Article  MATH  Google Scholar 

  22. Saber, R.O., Murray, R.M.: Graph rigidity and distributed formation stabilization of multi-vehicle systems. IEEE Conf. on Decision and Control 3, 2965–2971 (2002)

    Article  Google Scholar 

  23. Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. Int. Journal of Robotics Research 5(1), 90–98 (1986)

    Article  Google Scholar 

  24. Qu, Z., et al.: Cooperative control of dynamic systems with application to autonomous vehicles. IEEE Transactions of Automatic Control 53(4), 894–911 (2008)

    Article  MathSciNet  Google Scholar 

  25. Clark, M.R., et al.: Coupled oscillator control of autonomous mobile robots. Springer, Autonomous Robots 9(2), 189–198 (2000)

    Article  Google Scholar 

  26. Sepulchre, R., et al.: Collective motion and oscillator synchronization. Cooperative Control: Lecture Notes in Control and Information Science 309, 189–205 (2005)

    Article  MathSciNet  Google Scholar 

  27. Paley, D.A., et al.: Oscillator models and collective motion. In: Proceedings of Block Island Workshop on Cooperative Control (2003)

  28. Sepulchre, R., et al.: Stabilization of planar collective motion: all-to-all communication. IEEE Transaction on Automatic Control 52(4), 811–824 (2005)

    MathSciNet  MATH  Google Scholar 

  29. Klein, D.J., et al.: Collective motion: bi-stability and trajectory tracking. In: IEEE American Control Conference (2002)

  30. Klein, D.J., Morgansen, K.A.: Controlled collective motion for trajectory tracking. In: IEEE American Control Conference (2006)

  31. Kim, D.H., et al.: A real-time limit-cycle navigation method for fast mobile robot and its application to robot soccer. Robot. Auton. Syst. 42(1), 17–30 (2003)

    Article  MATH  Google Scholar 

  32. Quigley, M., et al.: Target acquisition, localization, and surveillance using a fixed-wing mini-UAV and gimbaled camera. Int. Conf. on Robotics and Automation, 2600–2606 (2005)

  33. Wu, M., et al.: A distributed multi-robot cooperative hunting algorithm based on limit cycle. Int. Asia Conf. on Informatics in Control, Automation and Robotics, 156–160 (2009)

  34. Benzerrouk, A., et al.: Navigation of multi-robot formation in unstructured environment using dynamical virtual structures. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 5589–5594 (2010)

  35. Sun, L., et al.: Distributed probabilistic search and tracking of agile mobile ground targets using a network of unmanned aerial vehicles. Springer International Publishing (2014)

  36. Kothari, M., et al.: Cooperative target capturing with incomplete target information. J. Intell. Robot. Syst. 68, 373–384 (2013)

    Article  Google Scholar 

  37. Fu, X., et al.: UAV Mobile ground target pursuit algorithm. J. Intell. Robot. Syst. 68(3), 359–371 (2012)

    Article  Google Scholar 

  38. Shin, H.S., et al.: Earliest intercept geometry guidance to improve mid-course guidance in area air-defence. Int’l J. of Aeronautical and Space Sci. 11(2), 118–125 (2010)

    Article  Google Scholar 

  39. Wu, M., et al.: Integrated heterogeneous multi-robot system for collaborative navigation. Frontiers in the Convergence of Bioscience and Information Technologies, 651–656 (2007)

  40. Kuramoto, Y.: Self-entrainment of a population of coupled non-linear oscillators. International Symposium on Mathematical Problems in Theoretical Physics Lecture Notes in Physics 39, 420–422 (1997)

    Article  Google Scholar 

  41. Saksguchi, H., Kuramoto, Y.: A soluble active rotator model showing phase transitions via mutual entrainment. International Symposium on Mathematical Problems in Theoretical Physics Lecture Notes in Physics 76(3), 576–581 (1986)

    MathSciNet  Google Scholar 

  42. Corke, P.: Robotics, vision and control: fundamental algorithms in MATLAB. Springer-Verlag, Heidelberg (2011)

    Book  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronic Systems Engineering, Hanyang University, Ansan, 15588, South Korea

    Sajjad Manzoor, Sungon Lee & Youngjin Choi

Authors
  1. Sajjad Manzoor
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sungon Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Youngjin Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Youngjin Choi.

Additional information

This work was supported in part by the Convergence Technology Development Program for Bionic Arm through the National Research Foundation (NRF) of Korea funded by the Ministry of Science, ICT & Future Planning (MSIP) (NRF-2015M3C1B2052811), and in part by the Agency for Defense Development (Grant UD160001DD), Republic of Korea.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Manzoor, S., Lee, S. & Choi, Y. A Coordinated Navigation Strategy for Multi-Robots to Capture a Target Moving with Unknown Speed. J Intell Robot Syst 87, 627–641 (2017). https://doi.org/10.1007/s10846-016-0443-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-016-0443-z

Keywords