Skip to main content
SpringerLink
Log in

A proposed decentralized formation control algorithm for robot swarm based on an optimized potential field method

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Lately, robot swarm has widely employed in many applications like search and rescue missions, fire forest detection and navigation in hazard environments. Each robot in a swarm is supposed to move without collision and avoid obstacles while performing the assigned job. Therefore, a formation control is required to achieve the robot swarm three tasks. In this article, we introduce a decentralized formation control algorithm based on the potential field method for robot swarm. Our formation control algorithm is proposed to achieve the three tasks: avoid obstacles in the environment, keep a fixed distance among robots to maintain a formation and perform an assigned task. An artificial neural network is engaged in the online optimization of the parameters of the potential force. Then, real-time experiments are conducted to confirm the reliability and applicability of our proposed decentralized formation control algorithm. The real-time experiment results prove that the proposed decentralized formation control algorithm enables the swarm to avoid obstacles and maintain formation while performing a certain task. The swarm manages to reach a certain goal and tracks a given trajectory. Moreover, the proposed decentralized formation control algorithm enables the swarm to escape from local minima, to pass through two narrow placed obstacles without oscillation near them. From a comparison between the proposed decentralized formation control algorithm and the traditional PFM, we obtained that NN-swarm successes to reach its goal with average accuracy 0.14 m compared to 0.22 m for the T-swarm. The NN-swarm also keeps a fixed distance between robots with a higher swarming error reaches 34.83%, while the T-swarm reaches 23.59%. Also, the NN-swarm is more accurate in tracking a trajectory with a higher tracking error reaches 0.0086 m compared to min. error of T-swarm equals to 0.01 m. Besides, the NN-swarm maintains formation much longer than T-swarm while tracking trajectory reaches 94.31% while the T-swarm reaches 81.07% from the execution time, in environments with different numbers of obstacles.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Olfati-Saber R (2006) Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Trans Autom Contr 51(3):1–20

    Article  MathSciNet  Google Scholar 

  2. Murray RM (2007) Recent research in cooperative control of multivehicle systems. J Dyn Syst Meas Control Trans ASME 129(5):571–583

    Article  Google Scholar 

  3. Hu J, Xu J, Xie L (2013) Cooperative search and exploration in robotic networks. Unmanned Syst 01(01):121–142

    Article  Google Scholar 

  4. Martin M, Klupar P, Kilberg S, Winter J (2001) TechSat 21 and revolutionizing space missions using microsatellites. USU/AIAA 1:1–10

    Google Scholar 

  5. Chaimowicz L, Kumar V (2008) Aerial shepherds: coordination among UAVs and swarms of robots. In: Distributed autonomous robotic systems 6. Springer, Tokyo, pp 243–252

  6. Egerstedt M, Hu X (2001) Formation constrained multi-agent control. IEEE Trans Robot Autom 17(6):947–951

    Article  Google Scholar 

  7. Kowalczyk W (2002) Target assignment strategy for scattered robots building formation. In: Proceedings of the 3rd international workshop on robot motion and control, RoMoCo 2002, 2002, pp 181–185

  8. Saber RO, Dunbar WB, Murray RM (2003) Cooperative control of multi-vehicle systems using cost graphs and optimization. Proc Am Control Conf 3:2217–2222

    Google Scholar 

  9. Lot A, Zelinski S, Koo TJ, Sastry S (2003) Optimization-based formation reconfiguration planning for autonomous vehicles. Proc IEEE Int Conf Robot Autom 3:3758–3763

    Google Scholar 

  10. Cao Z, Tan M, Wang S, Fan Y, Zhang B (2002) The optimization research of formation control for multiple mobile robots. Proc World Congr Intell Control Autom (WCICA) 2:1270–1274

    Article  Google Scholar 

  11. Barnes L, Alvis W, Fields M, Valavanis K, Moreno W (2006) Swarm formation control with potential fields formed by bivariate normal functions. In: 14th Mediterranean conference on control and automation, MED’06

  12. Fujibayashi K, Murata S, Sugawara K, Yamamura M (2002) Self-organizing formation algorithm for active elements. In: Proceedings of the IEEE symposium on reliable distributed systems, pp 416–421

  13. Chaimowicz L, Michael N, Kumar V (2005) Controlling swarms of robots using interpolated implicit functions. Proc IEEE Int Conf Robot Autom 2005:2487–2492

    Google Scholar 

  14. Jadbabaie A, Lin J, Morse AS (2003) Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Trans Autom Contr 48(6):988–1001

    Article  MathSciNet  Google Scholar 

  15. Desai JP (2001) Modeling multiple teams of mobile robots: a graph theoretic approach. IEEE Int Conf Intell Robots Syst 1:381–386

    Google Scholar 

  16. Feeney AB, Price DM (2000) A modular architecture for STEP. In: Proceedings of the third international working of robotic motion control. RoMoCo ’02, pp 285–290

  17. Olfati-Saber R, Murray RM (2002) Graph rigidity and distributed formation stabilization of multi-vehicle systems. Proc IEEE Conf Decis Control 3:2965–2971

    Article  Google Scholar 

  18. Balch T, Hybinette M (2000) Social potentials for scalable multi-robot formations. Proc IEEE Int Conf Robot Autom 1:73–80

    Google Scholar 

  19. Dudenhoeffer DD, Jones MP (2000) Formation behavior for large-scale micro-robot force deployment. Winter Simul Conf Proc 1:972–982

    Google Scholar 

  20. Leonard NE, Fiorelli E (2001) Virtual leaders, artificial potentials and coordinated control of groups. Proc IEEE Conf Decis Control 3:2968–2973

    Google Scholar 

  21. Olfati-Saber R, Murray RM (2002) Distributed cooperative control of multiple vehicle formations using structural potential functions. In: IFAC Proceedings volumes (IFAC-PapersOnline), vol 15, no 1, pp 495–500

  22. Schneider FE, Wildermuth D (2003) A potential field based approach to multi robot formation navigation. In: RISSP 2003, vol 2003, pp 680–685

  23. Khatib O (1986) Real-time obstacle avoidance for manipulators and mobile robots. Int J Rob Res 5(1):90–98

    Article  Google Scholar 

  24. Dang AD, Horn J (2015) Formation adaptation control of autonomous robots in a dynamic environment. In: Proceedings of the IEEE international conference on industrial technology, vol 2015, pp 3190–3195

  25. Furferi R, Conti R, Meli E, Ridolfi A (2016) Optimization of potential field method parameters through networks for swarm cooperative manipulation tasks. Int J Adv Robot Syst 13(6):172988141665793

    Article  Google Scholar 

  26. Koren Y, Borenstein J (1991) Potential field methods and their inherent limitations for mobile robot navigation. Proc IEEE Int Conf Robot Autom 2:1398–1404

    Google Scholar 

  27. Wu KH, Chen CH, Lee JD (1996) Genetic-based adaptive fuzzy controller for robot path planning. IEEE Int Conf Fuzzy Syst 3:1687–1691

    Article  Google Scholar 

  28. Jaradat MAK, Garibeh MH, Feilat EA (2012) Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field. Soft Comput 16(1):153–164

    Article  Google Scholar 

  29. Tu KY, Baltes J (2006) Fuzzy potential energy for a map approach to robot navigation. Rob Autom Syst 54(7):574–589

    Article  Google Scholar 

  30. Stoian V, Ivanescu M, Stoian E, Pana C (2006) Using artificial potential field methods and fuzzy logic for mobile robot control. In: 2006 12th international power electronics and motion control conference, 2006, pp 385–389

  31. Jia Q, Li G (2007) Formation control and obstacle avoidance algorithm of multiple autonomous underwater vehicles (AUVs) based on potential function and behavior rules. Proc IEEE Int Conf Autom Logist ICAL 2007:569–573

    Google Scholar 

  32. Park JW, Kwak HJ, Kang YC, Kim DW (2016) Advanced fuzzy potential field method for mobile robot obstacle avoidance. Comput Intell Neurosci 2016

  33. Vadakkepat P, Tan KC, Ming-Liang W (2000) Evolutionary artificial potential fields and their application in real time robot path planning. In: Proceedings of the 2000 congress on evolutionary computation, CEC 2000, vol 1, pp 256–263

  34. Li F, Wang Y, Tan Y, Ge G (2013) Mobile robots path planning based on evolutionary artificial potential fields approach. Iccsee, pp 1314–1317

  35. Bounini F, Gingras D, Pollart H, Gruyer D (2017) Modified artificial potential field method for online path planning applications. In: IEEE intelligent vehicles symposium, proceedings, pp 180–185

  36. Sun J, Tang J, Lao S (2017) Collision avoidance for cooperative UAVs with optimized artificial potential field algorithm. IEEE Access 5:18382–18390

    Article  Google Scholar 

  37. Elkilany BG, Abouelsoud AA, Fathelbab AMR (2017) Adaptive formation control of robot swarms using optimized potential field method. In: Proceedings of the IEEE international conference on industrial technology

  38. Cross SS, Harrison RF, Kennedy RL (1995) Introduction to neural networks, vol 346. MIT Press, Cambridge

    Google Scholar 

  39. PLATFORM-TurtleBot 3-ROBOTIS. Available: http://www.robotis.us/turtlebot-3/. Accessed 31 Dec 2019

  40. XL430-W250-T. http://emanual.robotis.com/docs/en/dxl/x/xl430-w250/. Accessed 31 Dec 2019

  41. TurtleBot3. http://emanual.robotis.com/docs/en/platform/turtlebot3/appendix_opencr1_0/. Accessed 12 Jan 2020

  42. Buy a Raspberry Pi 3 Model B+—Raspberry Pi. : https://www.raspberrypi.org/products/raspberry-pi-3-model-b-plus/. Accessed 12 Jan 2020

  43. TurtleBot3. http://emanual.robotis.com/docs/en/platform/turtlebot3/appendix_lds_01/. Accessed 31 Dec 2019

  44. O’Kane JM (2016) A gentle introduction to ROS, no. 2.1.3. Jason M. O’Kane

Download references

Acknowledgements

The authors would like to thank the Egypt-Japan University of Science and Technology (E-JUST) for continuous help and support.

Author information

Authors and Affiliations

  1. Mechatronics and Robotics Engineering Department, Egypt-Japan University of Science and Technology (E-JUST), New Borg El Arab, Egypt

    Basma Gh. Elkilany

  2. Computer and Automatic Control Engineering Department, Faculty of Engineering, Tanta University, Tanta, Egypt

    Basma Gh. Elkilany

  3. Electronics and Electrical Communication Engineering Department, Faculty of Engineering, Cairo University, Giza, Egypt

    A. A. Abouelsoud

  4. Mechatronics and Robotics Engineering Department, School of Innovative Design Engineering, Egypt-Japan University of Science and Technology (E-JUST), New Borg El Arab, Egypt

    Ahmed M. R. Fathelbab

  5. Mechanical Engineering Department, Faculty of Engineering, Assiut University, Asyut, Egypt

    Ahmed M. R. Fathelbab

  6. Faculty of Science and Engineering, Waseda University, TWIns Room#3C-202, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo, 162-8480, Japan

    Hiroyuki Ishii

Authors
  1. Basma Gh. Elkilany
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. A. Abouelsoud
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ahmed M. R. Fathelbab
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hiroyuki Ishii
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Basma Gh. Elkilany.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Elkilany, B.G., Abouelsoud, A.A., Fathelbab, A.M.R. et al. A proposed decentralized formation control algorithm for robot swarm based on an optimized potential field method. Neural Comput & Applic 33, 487–499 (2021). https://doi.org/10.1007/s00521-020-05032-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05032-0

Keyword

Search

Navigation