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Dynamic Leader Allocation in Multi-robot Systems Based on Nonlinear Model Predictive Control

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Abstract

This paper presents an approach to the dynamic leader selection problem in autonomous non-holonomic mobile robot formations when the current leader enters a failure state. Our method is based on a tree structure coupled with a modified version of the Nonlinear Model Predictive Control (NMPC) that allows for behavior change at the controller level. An explanation of the control algorithm, behavior selection, and leader selection structure is given, after which the results of both simulations and experiments using a three robot formation are shown and discussed.

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References

  1. Balch, T., Arkin, R.C.: Behavior-based formation control for multirobot teams. IEEE Trans. Robot. Autom. 14(6), 926–939 (1998)

    Article  Google Scholar 

  2. Chen, J., Sun, D., Yang, J., Chen, H.: Leader-follower formation control of multiple non-holonomic mobile robots incorporating a receding-horizon scheme. Int. J. Robot. Res. 29(6), 727–747 (2010). https://doi.org/10.1177/0278364909104290

    Article  Google Scholar 

  3. Cruz, C.D.L., Carelli, R.: Dynamic modeling and centralized formation control of mobile robots. In: IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics. https://doi.org/10.1109/IECON.2006.347299, pp 3880–3885 (2006)

  4. Dang, A.D., La, H.M., Horn, J.: Distributed formation control for autonomous robots following desired shapes in noisy environment. In: 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). https://doi.org/10.1109/MFI.2016.7849502, pp 285–290 (2016)

  5. Deng, L., Ma, X., Gu, J., Li, Y., Xu, Z., Wang, Y.: Artificial immune network-based multi-robot formation path planning with obstacle avoidance. Int. J. Robot. Autom. 31(3), 233–242 (2016)

    Google Scholar 

  6. Desai, J.P., Ostrowski, J.P., Kumar, V.: Modeling and control of formations of nonholonomic mobile robots. IEEE Trans. Robot. Autom. 17(6), 905–908 (2001)

    Article  Google Scholar 

  7. Du, X., Htet, K.K.K., Tan, K.K.: Development of a genetic-algorithm-based nonlinear model predictive control scheme on velocity and steering of autonomous vehicles. IEEE Trans. Ind. Electron. 63(11), 6970–6977 (2016)

    Article  Google Scholar 

  8. Feng, K.: Fuzzy sliding-mode consensus formation control of networked heterogeneous mecanum-wheeled multi-robots with dynamic effects. In: 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp 1–7 (2018). https://doi.org/10.1109/FUZZ-IEEE.2018.8491531

  9. Feng, K., Lu, J., Chen, J.: Nonlinear model predictive control based on support vector machine and genetic algorithm. Chin. J. Chem. Eng. 23(12), 2048–2052 (2015)

    Article  Google Scholar 

  10. Hou, Z., Wang, W., Zhang, G., Han, C.: A survey on the formation control of multiple quadrotors. In: 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). https://doi.org/10.1109/URAI.2017.7992717, pp 219–225 (2017)

  11. Hsia, K., Su, K., Guo, H., Chung, C.: Infrared communication of leader-follower robots in home security system. In: 2016 IEEE International Conference on Industrial Technology (ICIT), pp 1654–1659 (2016). https://doi.org/10.1109/ICIT.2016.7475010

  12. Hu, J., Lanzon, A.: An innovative tri-rotor drone and associated distributed aerial drone swarm control. Robot. Auton. Syst. 103, 162–174 (2018). https://doi.org/10.1016/j.robot.2018.02.019

    Article  Google Scholar 

  13. Jin, Y., Guo, H., Meng, Y.: Robustness analysis and failure recovery of a bio-inspired self-organizing multi-robot system. In: 2009 Third IEEE International Conference on Self-Adaptive and Self-Organizing Systems. https://doi.org/10.1109/SASO.2009.19, pp 154–164 (2009)

  14. Karpov, V., Karpova, I.: Leader election algorithms for static swarms. Biologically Inspired Cognitive Architectures 12, 54–64 (2015). https://doi.org/10.1016/j.bica.2015.04.001

    Article  Google Scholar 

  15. Kasić, A., Velagić, J., Osmanović, A.: Design of nmpc-based framework for mobile robot motion in unstructured environments. In: 2018 International Symposium ELMAR. https://doi.org/10.23919/ELMAR.2018.8534610, pp 183–186 (2018)

  16. Lawryńczuk, M.: Modelling and predictive control of a neutralisation reactor using sparse support vector machine wiener models. Neurocomputing 205, 311–328 (2016)

    Article  Google Scholar 

  17. Lei, C., Chaofang, H., Na, W.: Obstacle avoidance control of unmanned ground vehicle based on nmpc. In: 2017 Chinese Automation Congress (CAC), pp 6402–6406 (2017). https://doi.org/10.1109/CAC.2017.8243931

  18. Li, F., Ding, Y., Hao, K.: A dynamic leader-follower strategy for multi-robot systems. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp 298–303 (2015), https://doi.org/10.1109/SMC.2015.64

  19. Li, F., Ding, Y., Hao, K.: A neuroendocrine inspired dynamic leader selection model in formation control for multi-robot system. In: 2017 29th Chinese Control and Decision Conference (CCDC), pp 5454–5459 (2017). https://doi.org/10.1109/CCDC.2017.7979466

  20. Li, F., Ding, Y., Zhou, M., Hao, K., Chen, L.: An affection-based dynamic leader selection model for formation control in multirobot systems. IEEE Trans. Syst. Man Cybern. Syst. Hum. 47(7), 1217–1228 (2017). https://doi.org/10.1109/TSMC.2016.2564931

    Article  Google Scholar 

  21. Li, Z., Yuan, W., Chen, Y., Ke, F., Chu, X., Chen, C.L.P.: Neural-dynamic optimization-based model predictive control for tracking and formation of nonholonomic multirobot systems. IEEE Trans. Neural Netw. Learn. Syst. 29(12), 6113–6122 (2018). https://doi.org/10.1109/TNNLS.2018.2818127

    Article  Google Scholar 

  22. Lima, P.U., Ahmad, A., Dias, A., Conceição, A.G., Moreira, A.P., Silva, E., Almeida, L., Oliveira, L., Nascimento, T.P.: Formation control driven by cooperative object tracking. Robot. Auton. Syst. 63, 68–79 (2015). https://doi.org/10.1016/j.robot.2014.08.018. http://www.sciencedirect.com/science/article/pii/S0921889014001870

    Article  Google Scholar 

  23. Mehrez, M.W., Mann, G.K.I., Gosine, R.G.: An optimization based approach for relative localization and relative tracking control in multi-robot systems. J. Intell. Robot. Syst. 85(2), 385–408 (2017). https://doi.org/10.1007/s10846-016-0408-2

    Article  Google Scholar 

  24. Mehrez, M.W., Mann, G.K.I., Gosine, R.G.: Stabilizing nmpc of wheeled mobile robots using open-source real-time software. In: 2013 16th International Conference on Advanced Robotics (ICAR), pp. 1–6. https://doi.org/10.1109/ICAR.2013.6766536 (2013)

  25. Mehrez, M.W., Mann, G.K.I., Gosine, R.G.: Comparison of stabilizing nmpc designs for wheeled mobile robots: an experimental study. In: 2015 Moratuwa Engineering Research Conference (MERCon), pp. 130–135. https://doi.org/10.1109/MERCon.2015.7112333 (2015)

  26. Nascimento, T.P., Conceição, A.S., Moreira, A.P.: Multi-robot nonlinear model predictive formation control: Moving target and target absence. Robot. Auton. Syst. 61(12), 1502–1515 (2013)

    Article  Google Scholar 

  27. Nascimento, T.P., Costa, L.F.S., Conceição, A.G.S., Moreira, A.P.: Nonlinear model predictive formation control: an iterative weighted tuning approach. J. Intell. Robot. Syst. 80(3), 441–454 (2015). https://doi.org/10.1007/s10846-015-0183-5

    Article  Google Scholar 

  28. Nascimento, T.P., Dórea, C.E.T., Gonçalves, L.M.G.: Nonholonomic mobile robots’ trajectory tracking model predictive control: a survey. Robotica, pp. 1–21. https://doi.org/10.1017/S0263574717000637 (2018)

  29. Nascimento, T.P., Dórea, C.E.T., Gonçalves, L.M.G.: Nonlinear model predictive control for trajectory tracking of nonholonomic mobile robots: a modified approach. Int. J. Adv. Robot. Syst. 15(1), 1–14 (2018). https://doi.org/10.1177/1729881418760461

    Article  Google Scholar 

  30. Nascimento, T.P., Moreira, A.P., Conceição, A.G.S., Bonarini, A.: Intelligent state changing applied to multi-robot systems. Robot. Auton. Syst. 61(2), 115–124 (2013). https://doi.org/10.1016/j.robot.2012.10.011

    Article  Google Scholar 

  31. Ostafew, C.J., Schoellig, A.P., Barfoot, T.D.: Learning-based nonlinear model predictive control to improve vision-based mobile robot path-tracking in challenging outdoor environments. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 4029–4036. https://doi.org/10.1109/ICRA.2014.6907444 (2014)

  32. Ostafew, C.J., Schoellig, A.P., Barfoot, T.D.: Robust constrained learning-based nmpc enabling reliable mobile robot path tracking. Int. J. Robot. Res. 35(13), 1547–1563 (2016)

    Article  Google Scholar 

  33. Qiang, L., Heng, W., Huican, L., Shuqi, Q., Nanxun, D., Bing, L.: Formation control of multi robot based on uwb distance measurement. In: 2018 Chinese Control And Decision Conference (CCDC), pp. 2404–2408. https://doi.org/10.1109/CCDC.2018.8407528 (2018)

  34. Ribeiro, T.T., Fernandez, R.O.: Conceição, A.G.S.: Nmpc-based visual leader-follower formation control for wheeled mobile robots. In: 2018 IEEE 16th International Conference on Industrial Informatics (INDIN), pp. 406–411. https://doi.org/10.1109/INDIN.2018.8472107 (2018)

  35. Saeidi, H., Mikulski, D.G., Wang, Y.: Trust-based leader selection for bilateral haptic teleoperation of multi-robot systems. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6575–6581. https://doi.org/10.1109/IROS.2017.8206569 (2017)

  36. Sakurama, K.: Leader selection via lasso for formation control of time-delayed multi-agent systems. Neurocomputing 270, 18–26 (2017). https://doi.org/10.1016/j.neucom.2016.12.108

    Article  Google Scholar 

  37. Schultze, M., Horn, J.: Modeling, state estimation and nonlinear model predictive control of cathode exhaust gas mass flow for {PEM} fuel cells. Control. Eng. Pract. 49, 76–86 (2016)

    Article  Google Scholar 

  38. Shi, Z., Tu, J., Zhang, Q., Liu, L., Wei, J.: A survey of swarm robotics system. In: Tan, Y., Shi, Y., Ji, Z. (eds.) Advances in Swarm Intelligence, pp 564–572. Springer, Berlin (2012)

  39. Simba, K.R., Uchiyama, N., Sano, S.: Real-time smooth trajectory generation for nonholonomic mobile robots using bézier curves. Robot. Comput. Integr. Manuf. 41, 31–42 (2016). https://doi.org/10.1016/j.rcim.2016.02.002. http://www.sciencedirect.com/science/article/pii/S0736584516300552

    Article  Google Scholar 

  40. Subramanian, S., Nazari, S., Alvi, M.A., Engell, S.: Robust nmpc schemes for the control of mobile robots in the presence of dynamic obstacles. In: 2018 23rd International Conference on Methods Models in Automation Robotics (MMAR), pp. 768–773. https://doi.org/10.1109/MMAR.2018.8485841 (2018)

  41. Tutuko, B., Nurmaini, S., Fitriana, G.F.: Tracking control enhancement on non-holonomic leader-follower robot. In: 2017 International Conference on Electrical Engineering and Computer Science (ICECOS), pp. 83–86. https://doi.org/10.1109/ICECOS.2017.8167172 (2017)

  42. Wesselowski, K., Fierro, R.: A dual-mode model predictive controller for robot formations. In: 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475), vol. 4, pp 3615–3620 (2003). https://doi.org/10.1109/CDC.2003.1271709

  43. Wilson, J., Charest, M., Dubay, R.: Non-linear model predictive control schemes with application on a 2 link vertical robot manipulator. Robot. Comput. Integr. Manuf. 41, 23–30 (2016)

    Article  Google Scholar 

  44. Xiao, H., Chen, C.P., Li, T., Han, M.: General projection neural network based nonlinear model predictive control for multi-robot formation and tracking. IFAC-PapersOnLine 50(1), 838–843 (2017). https://doi.org/10.1016/j.ifacol.2017.08.149. http://www.sciencedirect.com/science/article/pii/S240589631730191X. 20th IFAC World congress

    Article  Google Scholar 

  45. Xiao, S., Feng, L., Lian, H., Du, B.: Dynamic formation and obstacle avoidance control for multi robot system. In: 2016 12th World Congress on Intelligent Control and Automation (WCICA), pp. 59–63. https://doi.org/10.1109/WCICA.2016.7578642 (2016)

  46. Yu, S., Barca, J.C.: Autonomous formation selection for ground moving multi-robot systems. In: 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), pp. 54–59. https://doi.org/10.1109/AIM.2015.7222508 (2015)

  47. Zhao, Y., Park, D., Moon, J., Lee, J.: Leader-follower formation control for multiple mobile robots by a designed sliding mode controller based on kinematic control method. In: 2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), pp. 186–189. https://doi.org/10.23919/SICE.2017.8105709 (2017)

  48. Zhengcai, C., Yingtao, Z., Qidi, W.: Adaptive trajectory tracking control for a nonholonomic mobile robot. Chinese Journal of Mechanical Engineering 23(3), 546–552 (2011)

    Google Scholar 

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Authors and Affiliations

  1. Laboratory of Systems Engineering and Robotics, Department of Computer Systems, Federal University of Paraiba, João Pessoa, Paraiba, Brazil

    Augusto de Holanda B. M. Tavares, Sarah Pontes Madruga, Alisson V. Brito & Tiago P. Nascimento

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  1. Augusto de Holanda B. M. Tavares
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  2. Sarah Pontes Madruga
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Correspondence to Tiago P. Nascimento.

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The authors would like to thank CNPq for the financial support through the call EDITAL UNIVERSAL MCTI/CNPq N 14/2014, and CAPES for the financial support

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Tavares, A.d.H.B.M., Madruga, S.P., Brito, A.V. et al. Dynamic Leader Allocation in Multi-robot Systems Based on Nonlinear Model Predictive Control. J Intell Robot Syst 98, 359–376 (2020). https://doi.org/10.1007/s10846-019-01064-4

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