Journal of Intelligent & Robotic Systems

, Volume 95, Issue 3–4, pp 915–933 | Cite as

A Two-Stage Approach to Collaborative Fiber Placement through Coordination of Multiple Autonomous Industrial Robots

  • Mahdi HassanEmail author
  • Dikai Liu
  • Dongliang Xu


The use of multiple Autonomous Industrial Robots (AIRs) as opposed to a single AIR to perform fiber placement brings about many challenges which have not been addressed by researchers. These challenges include optimal division and allocation of the work and performing path planning in a coordinated manner while considering the requirements and constraints that are unique to the fiber placement task. To solve these challenges, a two-stage approach is proposed in this paper. The first stage considers multiple objectives to optimally allocate each AIR with surface areas, while the second stage aims to generate coordinated paths for the AIRs. Within each stage, mathematical models are developed with several unique objectives and constraints that are specific to the multi-AIR collaborative fiber placement. Several case studies are presented to validate the approach and the proposed mathematical models. Comparison studies with different number of AIRs and variations of the developed mathematical models are also presented.


Multi-robot fiber placement Fiber reinforced composites Multiple autonomous industrial robots Tool-path allocation Complete coverage 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



This work is supported by the Centre for Autonomous Systems (CAS) at the University of Technology Sydney (UTS).

Supplementary material

(MP4 14.8 MB)

(MP4 12.6 MB)


  1. 1.
    Shirinzadeh, B., Cassidy, G., Oetomo, D., Alici, G., Ang, M.H.: Trajectory generation for open-contoured structures in robotic fibre placement. Robot. Comput. Integr. Manuf. 23(4), 380–394 (2007)CrossRefGoogle Scholar
  2. 2.
    Shirinzadeh, B., Alici, G., Foong, C.W., Cassidy, G.: Fabrication process of open surfaces by robotic fibre placement. Robot. Comput. Integr. Manuf. 20(1), 17–28 (2004)CrossRefGoogle Scholar
  3. 3.
    Hassan, M., Liu, D., Paul, G.: Modeling and stochastic optimization of complete coverage under uncertainties in multi-robot base placements. In: International Conference on Intelligent Robots and Systems (IROS), pp. 2978–2984 (2016)Google Scholar
  4. 4.
    Hvilshoj, M., Bogh, S., Skov Nielsen, O., Madsen, O.: Autonomous industrial mobile manipulation (AIMM): Past, present and future. Indust. Robot: Int. J. 39(2), 120–135 (2012)CrossRefGoogle Scholar
  5. 5.
    Debout, P., Chanal, H., Duc, E.: Tool path smoothing of a redundant machine: Application to automated fiber placement. Comput. Aided Des. 43(2), 122–132 (2011)CrossRefGoogle Scholar
  6. 6.
    Li, L., Xu, D., Wang, X., Tan, M.: A survey on path planning algorithms in robotic fibre placement. In: Chinese Control and Decision Conference (CCDC), pp. 4704–4709 (2015)Google Scholar
  7. 7.
    Hu, B., Xu, D.-l.: Fiber placement path planning for open surfaces based on traversal method. Fiber Reinforced Plastics 6, 005 (2014)Google Scholar
  8. 8.
    Yan, L., Chen, Z.C., Shi, Y., Mo, R.: An accurate approach to roller path generation for robotic fibre placement of free-form surface composites. Robot. Comput. Integr. Manuf. 30(3), 277–286 (2014)CrossRefGoogle Scholar
  9. 9.
    Xiaoping, W., Luling, A., Liyan, Z., Laishui, Z.: Uniform coverage of fibres over open-contoured freeform structure based on arc-length parameter. Chin. J. Aeronaut. 21(6), 571–577 (2008)CrossRefGoogle Scholar
  10. 10.
    Bruyneel, M., Zein, S.: A modified fast marching method for defining fiber placement trajectories over meshes. Comput. Struct. 125, 45–52 (2013)CrossRefGoogle Scholar
  11. 11.
    Li, L., Wang, X., Xu, D., Tan, M.: Path planning of airfoil surface for robotic fibre placement. In: 2015 International Conference on Advanced Mechatronic Systems (ICAMechS), pp. 316–321 (2015)Google Scholar
  12. 12.
    Aized, T., Shirinzadeh, B.: Robotic fiber placement process analysis and optimization using response surface method. Int. J. Adv. Manuf. Technol. 55(1), 393–404 (2011)CrossRefGoogle Scholar
  13. 13.
    Jeffries, K.A.: Enhanced robotic automated fiber placement with accurate robot technology and modular fiber placement head. SAE Int. J. Aerosp. 6(2013-01-2290), 774–779 (2013)CrossRefGoogle Scholar
  14. 14.
    Zhang, X., Xie, W.-F., Hoa, S.V.: Modeling and workspace analysis of collaborative advanced fiber placement machine. In: ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers (2014)Google Scholar
  15. 15.
    Alatartsev, S., Stellmacher, S., Ortmeier, F.: Robotic task sequencing problem: A survey. J. Intell. Robot. Syst. 80(2), 279–298 (2015)CrossRefGoogle Scholar
  16. 16.
    Li, J., Meng, X., Zhou, M., Dai, X.: A two-stage approach to path planning and collision avoidance of multibridge machining systems. IEEE Trans. Syst. Man Cybern. Syst. PP(99), 1–11 (2016)CrossRefGoogle Scholar
  17. 17.
    Han, X., Bui, H., Mandal, S., Pattipati, K.R., Kleinman, D.L.: Optimization-based decision support software for a team-in-the-loop experiment: Asset package selection and planning. IEEE Trans. Syst. Man Cybern. Syst. 43(2), 237–251 (2013)CrossRefGoogle Scholar
  18. 18.
    Han, X., Mandal, S., Pattipati, K.R., Kleinman, D.L., Mishra, M.: An optimization-based distributed planning algorithm: A blackboard-based collaborative framework. IEEE Trans. Syst. Man Cybern. Syst. 44(6), 673–686 (2014)CrossRefGoogle Scholar
  19. 19.
    Sariel-Talay, S., Balch, T.R., Erdogan, N.: A generic framework for distributed multirobot cooperation. J. Intell. Robot. Syst. 63(2), 323–358 (2011)CrossRefGoogle Scholar
  20. 20.
    Gasparetto, A., Boscariol, P., Lanzutti, A., Vidoni, R.: Trajectory planning in robotics. Math. Comput. Sci. 6(3), 269–279 (2012)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Latombe, J.-C.: Robot Motion Planning, vol. 124. Springer Science & Business Media (2012)Google Scholar
  22. 22.
    Chotiprayanakul, P., Liu, D., Wang, D., Dissanayake, G.: A 3-dimensional force field method for robot collision avoidance in complex environments. In: International symposium on automation and robotics in construction (ISARC), pp. 19–21 (2007)Google Scholar
  23. 23.
    To, W.K., Paul, G., Kwok, N.M., Liu, D.: An efficient trajectory planning approach for autonomous robots in complex bridge environments. Int. J. Comput. Aided Eng. Technol. 1(2), 185–208 (2009)CrossRefGoogle Scholar
  24. 24.
    Bonilla, I., Mendoza, M., Gonzalez-Galván, E.J., Chavez-Olivares, C., Loredo-Flores, A., Reyes, F.: Path-tracking maneuvers with industrial robot manipulators using uncalibrated vision and impedance control. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 42(6), 1716–1729 (2012)CrossRefGoogle Scholar
  25. 25.
    LaValle, S.M.: Planning Algorithms. Cambridge University Press (2006)Google Scholar
  26. 26.
    Liu, Z., Xu, J., Yang, C., Zhao, Y., Zhang, T.: A TE-E optimal planning technique based on screw theory for robot trajectory in workspace. Journal of Intelligent & Robotic Systems (2017)Google Scholar
  27. 27.
    Li, S., He, J., Li, Y., Rafique, M.U.: Distributed recurrent neural networks for cooperative control of manipulators: A game-theoretic perspective. IEEE Trans. Neural Netw. Learn. Syst. 28(2), 415–426 (2017)MathSciNetCrossRefGoogle Scholar
  28. 28.
    He, W., Ouyang, Y., Hong, J.: Vibration control of a flexible robotic manipulator in the presence of input deadzone. IEEE Trans. Indus. Inf. 13(1), 48–59 (2017)CrossRefGoogle Scholar
  29. 29.
    He, W., He, X., Zou, M., Li, H.: PDE model-based boundary control design for a flexible robotic manipulator with input backlash. IEEE Trans. Control Syst. Technol., 1–8 (2018)Google Scholar
  30. 30.
    Gao, H., He, W., Zhou, C., Sun, C.: Neural network control of a two-link flexible robotic manipulator using assumed mode method. IEEE Trans. Indus. Inf., 1–1 (2018)Google Scholar
  31. 31.
    Hassan, M., Liu, D., Paul, G., Huang, S.: An approach to base placement for effective collaboration of multiple autonomous industrial robots. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3286–3291 (2015)Google Scholar
  32. 32.
    Hassan, M., Liu, D., Paul, G.: Collaboration of multiple autonomous industrial robots through optimal base placements. Journal of Intelligent & Robotic Systems (2017)Google Scholar
  33. 33.
    Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G.: Robotics: Modelling, Planning and Control. Springer Science & Business Media (2010)Google Scholar
  34. 34.
    Yuan, S., Skinner, B., Huang, S., Liu, D.: A new crossover approach for solving the multiple travelling salesmen problem using genetic algorithms. Eur. J. Oper. Res. 228(1), 72–82 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Carter, A.E., Ragsdale, C.T.: A new approach to solving the multiple traveling salesperson problem using genetic algorithms. Eur. J. Oper. Res. 175(1), 246–257 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Deb, K., Goel, T.: Controlled elitist non-dominated sorting genetic algorithms for better convergence. In: Zitzler, E., Thiele, L., Deb, K., Coello Coello, C., Corne, D. (eds.) Evolutionary Multi-Criterion Optimization, vol. 1993 of Lecture Notes in Computer Science, pp 67–81. Springer, Berlin (2001)Google Scholar
  37. 37.
    Riquelme, N., Lücken, C.V., Baran, B.: Performance metrics in multi-objective optimization. In: 2015 Latin American Computing Conference (CLEI), pp. 1–11 (2015)Google Scholar
  38. 38.
    Ishibuchi, H., Masuda, H., Tanigaki, Y., Nojima, Y.: Difficulties in specifying reference points to calculate the inverted generational distance for many-objective optimization problems. In: 2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM), pp. 170–177 (2014)Google Scholar
  39. 39.
    Peters, S.: Quadtree- and octree-based approach for point data selection in 2D or 3D. Ann. GIS 19(1), 37–44 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Centre for Autonomous Systems (CAS) at the University of Technology Sydney (UTS)UltimoAustralia
  2. 2.School of Mechanical and Electronic EngineeringWuhan University of TechnologyWuhanChina

Personalised recommendations