Skip to main content
SpringerLink
Log in

Formation building and collision avoidance for a fleet of NAOs based on optical sensor with local positions and minimum communication

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

Multi-robot system has become a research hotspot because of low demand on the sensors’ accuracy, high reliability, and high efficiency. To put all the robots together, formation control is a crucial problem. In this paper, we propose a local position-based method to plan trajectories and build a pyramid pattern for a fleet of NAOs in the obstacle-free environment by refering to the position-based method and giving an O(n log n) collision avoidance strategy inspired from one graph theory, where the local positions are estimated from optical sensors. To get the local positions, an integrated image processing method is developped. Firstly a mask-base is generated to store the features of NAOs, and a cross-correlation method is introduced to recognize the NAO. Subsequently, the distance and angle models are proposed to get the local information from a single image. Then, a visual compass is introduced to obtain the orientation of one NAO. After the local information exchange by the WiFi communication, a neighbor-check method is put foward to distinguish the homogeneous NAOs (all the NAOs look like the same). Further, a common frame is constructed as an artificial global frame, and straight non-intercrossing trajectories are planned according to the O(n log n) collision avoidance strategy. At last, the performance of our proposed local position-based method is verified by the simulations with up to 15 robots and the indoor experiments with 3 NAOs in a real environment. The convergence of the method has been demonstrated in both obstacle-free and static obstacle environments.

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. Gu D B. A game theory approach to target tracking in sensor networks. IEEE Trans Syst Man Cybern B, 2011, 41: 2–13

    Article  Google Scholar 

  2. Jin Y C, Guo H L, Meng Y. A hierarchical gene regulatory network for adaptive multirobot pattern formation. IEEE Trans Syst Man Cybern B, 2012, 42: 805–816

    Article  Google Scholar 

  3. Ahmad M D, Kasim M A A, Mohammed M A, et al. Multi-robot system for real-time sensing and monitoring. In: Proceedings of the 15th International Workshop on Research and Education in Mechatronics (REM), El Gouna, 2014

    Google Scholar 

  4. Arai T, Pagello E, Parker L E. Guest editorial advances in multirobot systems. IEEE Trans Robot Autom, 2002, 18: 655–661

    Article  Google Scholar 

  5. Li X, Zhu D Q, Qian Y. A survey on formation control algorithms for multi-AUV system. Unmanned Syst, 2014, 2: 351–359

    Article  Google Scholar 

  6. Benzerrouk A, Adouane L, Martinet P. Stable navigation in formation for a multi-robot system based on a constrained virtual structure. Robot Auton Syst, 2014, 62: 1806–1815

    Article  Google Scholar 

  7. Balch T, Arkin R C. Behavior-based formation control for multirobot teams. IEEE Trans Robot Autom, 1998, 14: 926–939

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Loria A, Dasdemir J, Jarquin N A. Leader-follower formation and tracking control of mobile robots along straight paths. IEEE Trans Control Syst Technol, 2016, 24: 727–732

    Article  Google Scholar 

  10. Oh K K, Park M C, Ahn H S. A survey of multi-agent formation control. Automatica, 2015, 53: 424–440

    Article  MathSciNet  MATH  Google Scholar 

  11. Alonso-Mora J, Breitenmoser A, Rufli M, et al. Optimal reciprocal collision avoidance for multiple non-holonomic robots. In: Proceedings of the 10th International Symposium on Distributed Autonomous Robotic Systems, Lausanne, 2010. 203–216

    Google Scholar 

  12. Paulos J, Eckenstein N, Tosun T, et al. Automated self-assembly of large maritime structures by a team of robotic boats. IEEE Trans Autom Sci Eng, 2015, 12: 958–968

    Article  Google Scholar 

  13. Xia Y Q, Na X T, Sun Z Q, et al. Formation control and collision avoidance for multi-agent systems based on position estimation. ISA Trans, 2016, 61: 287–296

    Article  Google Scholar 

  14. Nikou A, Verginis C K, Dimarogonas D V. Robust distance-based formation control of multiple rigid bodies with orientation alignment. 2017. ArXiv:1611.01824

    Google Scholar 

  15. Oh K K, Ahn H S. Distance-based control of cycle-free persistent formations. In: Proceedings of IEEE International Symposium on Intelligent Control, Denver, 2011. 816–821

    Google Scholar 

  16. Bartlett S L, Hampapur A, Huber M J, et al. Vision for mobile robots. In: Image Technology. Berlin: Springer, 1996

    Google Scholar 

  17. Fredslund J, Mataric M J. A general algorithm for robot formations using local sensing and minimal communication. IEEE Trans Robot Autom, 2002, 18: 837–846

    Article  Google Scholar 

  18. Gouaillier D, Hugel V, Blazevic P, et al. Mechatronic design of NAO humanoid. In: Proceedings of IEEE International Conference on Robotics and Automation, Kobe, 2009. 769–774

    Google Scholar 

  19. Wang X M, Zerr B, Thomas H, et al. Pattern formation for a fleet of auvs based on optical sensor. In: Proceedings of OCEANS-17, Aberdeen, 2017

    Google Scholar 

  20. Kim Y, Mesbahi M. On maximizing the second smallest eigenvalue of a state-dependent graph Laplacian. IEEE Trans Autom Control, 2006, 51: 116–120

    Article  MathSciNet  MATH  Google Scholar 

  21. Alonso-Mora J, Breitenmoser A, Rufli M, et al. Multi-robot system for artistic pattern formation. In: Proceedings of IEEE International Conference on Robotics and Automation, Shanghai, 2011. 4512–4517

    Google Scholar 

  22. Yu S, Barca J C. Autonomous formation selection for ground moving multi-robot systems. In: Proceedings of IEEE International Conference on Advanced Intelligent Mechatronics, Busan, 2015. 54–59

    Google Scholar 

  23. Benozzi L. A detection strategy for line formation of a team of humanoid robots. Dissertation for Master Degree. Florence: University of Florence, 2017

    Google Scholar 

  24. Aldebaran: Nao software 1.14.5 documentation/hardware/nao technical overview/joints. 2013. https://doi.org/doc.aldebaran.com/1-14/family/robots/jointsrobot.html

  25. Aldebaran: Nao software 1.14.5 documentation/reference/naoqi api/naoqi vision/alvisualcompass. 2013. https://doi.org/doc.aldebaran.com/1-14/naoqi/vision/alvisualcompass.html?highlight=compass

  26. Fujinaga N, Yamauchi Y, Ono H, et al. Pattern formation by oblivious asynchronous mobile robots. SIAM J Comput, 2015, 44: 740–785

    Article  MathSciNet  MATH  Google Scholar 

  27. Aldebaran: Nao software 1.14.5 documentation/hardware/nao technical overview/contact and tactile sensors. 2013. https://doi.org/doc.aldebaran.com/1-14/family/robots/contact-sensorsrobot.html

  28. Aldebaran: Nao software 1.14.5 documentation/hardware/nao technical overview/sonars. 2013. https://doi.org/doc.aldebaran.com/1-14/family/robots/sonarrobot.html

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61571478, 61601428, 51709245, 51509229).

Author information

Authors and Affiliations

  1. Engineering college, Ocean University of China, Qingdao, 266100, China

    Xiaomin Wang & Zexiao Xie

  2. Lab-STICC, UMR CNRS 6285, Brest, 29806, France

    Xiaomin Wang, Lorenzo Benozzi, Benoit Zerr & Benoit Clement

  3. Ecole National Supérieure de Techniques Avancées (ENSTA) Bretagne, Brest, 29806, France

    Xiaomin Wang, Lorenzo Benozzi, Benoit Zerr, Hélène Thomas & Benoit Clement

  4. Department of Industrial Engineering, University of Florence, Florence, 50121, Italy

    Lorenzo Benozzi

Authors
  1. Xiaomin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lorenzo Benozzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Benoit Zerr
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zexiao Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hélène Thomas
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Benoit Clement
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaomin Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, X., Benozzi, L., Zerr, B. et al. Formation building and collision avoidance for a fleet of NAOs based on optical sensor with local positions and minimum communication. Sci. China Inf. Sci. 62, 52205 (2019). https://doi.org/10.1007/s11432-018-9681-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-018-9681-3

Keywords

Search

Navigation