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

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Abstract

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.

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References

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  10. Bruyneel, M., Zein, S.: A modified fast marching method for defining fiber placement trajectories over meshes. Comput. Struct. 125, 45–52 (2013)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  15. Alatartsev, S., Stellmacher, S., Ortmeier, F.: Robotic task sequencing problem: A survey. J. Intell. Robot. Syst. 80(2), 279–298 (2015)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  20. Gasparetto, A., Boscariol, P., Lanzutti, A., Vidoni, R.: Trajectory planning in robotics. Math. Comput. Sci. 6(3), 269–279 (2012)

    Article  MathSciNet  Google Scholar 

  21. Latombe, J.-C.: Robot Motion Planning, vol. 124. Springer Science & Business Media (2012)

  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)

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  25. LaValle, S.M.: Planning Algorithms. Cambridge University Press (2006)

  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)

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

  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)

  32. Hassan, M., Liu, D., Paul, G.: Collaboration of multiple autonomous industrial robots through optimal base placements. Journal of Intelligent & Robotic Systems (2017)

  33. Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G.: Robotics: Modelling, Planning and Control. Springer Science & Business Media (2010)

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

  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)

  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)

  39. Peters, S.: Quadtree- and octree-based approach for point data selection in 2D or 3D. Ann. GIS 19(1), 37–44 (2013)

    Article  Google Scholar 

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Acknowledgments

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

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Authors and Affiliations

  1. Centre for Autonomous Systems (CAS) at the University of Technology Sydney (UTS), Ultimo, Australia

    Mahdi Hassan & Dikai Liu

  2. School of Mechanical and Electronic Engineering, Wuhan University of Technology, 430070, Wuhan, China

    Dongliang Xu

Authors
  1. Mahdi Hassan
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  2. Dikai Liu
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  3. Dongliang Xu
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Correspondence to Mahdi Hassan.

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Hassan, M., Liu, D. & Xu, D. A Two-Stage Approach to Collaborative Fiber Placement through Coordination of Multiple Autonomous Industrial Robots. J Intell Robot Syst 95, 915–933 (2019). https://doi.org/10.1007/s10846-018-0919-0

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