Skip to main content
SpringerLink
Log in

An improved artificial physics approach to multiple UAVs/UGVs heterogeneous coordination

  • Progress of Projects Supported by NSFC
  • Published:
Science China Technological Sciences Aims and scope Submit manuscript

Abstract

This paper proposed an improved artificial physics (AP) method to solve the autonomous navigation problem for multiple unmanned aerial vehicles (UAVs)/unmanned ground vehicles (UGVs) heterogeneous coordination in the three-dimensional space. The basic AP method has a shortcoming of easily plunging into a local optimal solution, which can result in navigation fails. To avoid the local optimum, we improved the AP method with a random scheme. In the improved AP method, random forces are used to make heterogeneous multi-UAVs/UGVs escape from local optimum and achieve global optimum. Experimental results showed that the improved AP method can achieve smoother trajectories and smaller time consumption than the basic AP method and basic potential field method (PFM).

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

Access this article

Log in via an institution

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

References

  1. Spears W M, Spears D F, Hamann J C, et al. Distributed, physics-based control of swarms of vehicles. Auton Robot, 2004, 17: 137–162

    Article  Google Scholar 

  2. Spears W M, Gordon D F. Using artificial physics to control agents. In: Proceedings of the IEEE Conference on Information, Intelligence, and System. Bethesda MD: IEEE, 1999. 281–288

    Google Scholar 

  3. Spears W M, Spears D F, Heil R, et al. An overview of physicomimetics. In: Swarm R, ed. Lecture Notes in Computer Science. Berlin: Springer-Verlag Press, 2005. 84–97

    Google Scholar 

  4. Duan H B, Liu S Q. Nonlinear dual-mode receding horizon control for multiple UAVs formation flight based on chaotic particle swarm optimization. IET Contr Theor & Appl, 2010, 4: 2565–2578

    Article  Google Scholar 

  5. Wu R B, Jia Q Q, Li H. A novel STAP method for the detection of fast air moving targets from high speed platform. Sci China Inf Sci, 2012, 55: 1259–1269

    Article  MathSciNet  Google Scholar 

  6. Hsieh M A, Chaimowicz L, Cowley A, et al. Adaptive teams of autonomous aerial and ground robots for situational awareness. J Field Robot, 2007, 24: 991–1014

    Article  Google Scholar 

  7. Michael T, Blake B. Semi-autonomous UAV/UGV for dismounted urban operations. In: Proceedings of SPIE. Orlando: SPIE, 2010. 76921C

    Google Scholar 

  8. Phan C, Liu H T. A cooperative UAV/UGV platform for wildfire detection and fighting. In: Proceedings of 2008 Asia Simulation Conference — the 7th International Conference on System Simulation and Scientific Computing. Beijing: IEEE, 2008. 494–498

    Chapter  Google Scholar 

  9. Cui R X, Yan W S, Xu D M. Synchronization of multiple autonomous underwater vehicles without velocity measurements. Sci China Inf Sci, 2012, 55: 1693–1703

    Article  MathSciNet  MATH  Google Scholar 

  10. Rezaee H, Abdollahi F. Adaptive artificial potential field approach for obstacle avoidance of unmanned aircrafts. In: Proceedings of the 2012 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). Kachsiung: IEEE, 2012. 1–6

    Chapter  Google Scholar 

  11. Min G P, Jae H J, Min C L. Obstacle avoidance for mobile robots using artificial potential field approach with simulated annealing. In: Proceedings of the 2001 IEEE International Symposium on Industrial Electronics. Pusan: IEEE, 2001. 1530–1535

    Google Scholar 

  12. Li S P, Fan Y H. An adaptive control with optimal disturbances rejection. Sci China Inf Sci, 2012, 55: 1704–1714

    Article  MathSciNet  MATH  Google Scholar 

  13. Hettiarachchi S, Spears W M. Distributed adaptive swarm for obstacle avoidance. Int J Intel Comp Cybern, 2009, 2: 644–671

    Article  MathSciNet  MATH  Google Scholar 

  14. Olfati-Saber R. Flocking for multi-agent dynamic systems: Algorithms and theory. IEEE Trans Automat Contr, 2006, 51: 401–420

    Article  MathSciNet  Google Scholar 

  15. Ren J, McIsaac K A, Patel R V. Modified newton’s method applied to potential field-based navigation for nonholonomic robots in dynamic environments. Robotica, 2008, 26: 117–127

    Google Scholar 

  16. Ren J, McIsaac K A, Patel R V. Modified newton’s method applied to potential field-based navigation for mobile robots. IEEE Trans Robot, 2006, 22: 384–391

    Article  Google Scholar 

  17. Zhang Y P, Duan H B, Zhang X Y. Stable flocking of multiple agents based on molecular potential field and distributed receding horizon control. Chin Phys Lett, 2011, 28: 040503

    Article  Google Scholar 

  18. Wang Y X, Wang Z H. A fast successive over-relaxation algorithm for force-directed network graph drawing. Sci China Inf Sci, 2012, 55: 677–688

    Article  MathSciNet  MATH  Google Scholar 

  19. Duan H B, Liu S Q, Wu J. Novel intelligent water drops optimization approach to single UCAV smooth trajectory planning. Aerosp Sci Tech, 2009, 13: 442–449

    Article  Google Scholar 

  20. Agirrebeitia J, Aviles R, Bustos I F, et al. A new APF strategy for path planning in environments with obstacles. Mech Mach Theor, 2005, 40: 645–658

    Article  MATH  Google Scholar 

  21. Palejiya D, Tanner H G. Hybrid velocity/force control for robot navigation in compliant unknown environments. Robotica, 2006, 24: 745–758

    Article  Google Scholar 

  22. Duan H B, Luo Q N, Shi Y H, et al. Hybrid particle swarm optimization and genetic algorithm for multi-UAVs formation reconfiguration. IEEE Comput Intel Magaz, 2013, 8: 16–27

    Article  Google Scholar 

  23. Duan H B, Yu Y X, Zhao Z Y. Parameters identification of UCAV flight control system based on predator-prey particle swarm optimization. Sci China Inf Sci, 2013, 56: 012202:1–012202:12

    Article  MathSciNet  Google Scholar 

  24. Zhang X Y, Duan H B, Yu Y X. Receding horizon control for multi-UAVs close formation control based on differential evolution. Sci China Inf Sci, 2010, 53: 223–235

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Science and Technology on Aircraft Control Laboratory, School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China

    QiNan Luo & HaiBin Duan

Authors
  1. QiNan Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. HaiBin Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to QiNan Luo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Luo, Q., Duan, H. An improved artificial physics approach to multiple UAVs/UGVs heterogeneous coordination. Sci. China Technol. Sci. 56, 2473–2479 (2013). https://doi.org/10.1007/s11431-013-5314-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11431-013-5314-2

Keywords

Search

Navigation