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Pose Consensus of Multiple Robots with Time-Delays Using Neural Networks

Published online by Cambridge University Press:  15 January 2019

Carlos I. Aldana ,
Rodrigo Munguía ,
Emmanuel Cruz-Zavala  and
Emmanuel Nuño Open the ORCID record for Emmanuel Nuño [Opens in a new window]
Carlos I. Aldana
Affiliation:
Department of Computer Sciences, CUCEI, University of Guadalajara, 44430 Guadalajara, Mexico
Rodrigo Munguía
Affiliation:
Department of Computer Sciences, CUCEI, University of Guadalajara, 44430 Guadalajara, Mexico
Emmanuel Cruz-Zavala
Affiliation:
Department of Computer Sciences, CUCEI, University of Guadalajara, 44430 Guadalajara, Mexico
Emmanuel Nuño*
Affiliation:
Department of Computer Sciences, CUCEI, University of Guadalajara, 44430 Guadalajara, Mexico
*
*Corresponding author. E-mail: [email protected]
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Summary

This paper proposes a novel control scheme based on Radial Basis Artificial Neural Network to solve the leader–follower and leaderless pose (position and orientation) consensus problems in the Special Euclidean space of dimension three (SE(3)). The controller is designed for robot networks composed of heterogeneous (kinematically and dynamically different) and uncertain robots with variable time-delays in the interconnection. The paper derives a sufficient condition on the controller gains and the robot interconnection, and using Barbalat’s Lemma, both consensus problems are solved. The proposed approach employs the singularity-free, unit-quaternions to represent the orientation of the end-effectors in the SE(3). The significance and advantages of the proposed control scheme are that it solves the two pose consensus problems for heterogeneous robot networks considering variable time-delays in the interconnection without orientation representation singularities, and the controller does not require to know the dynamic model of the robots. The performance of the proposed controller is illustrated via simulations with a heterogeneous robot network composed of robots with 6-DoF and 7-DoF.

Keywords

Artificial neural networkMulti-agent systemsConsensus controlVariable time-delays
Type
Articles
Information
Robotica , Volume 37 , Issue 5 , May 2019 , pp. 883 - 905
Copyright
Copyright © Cambridge University Press 2019 

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References

Camazine, S., Deneubourg, J.-L., Franks, N. R., Sneyd, J., Theraulaz, G. and Bonabeau, E.. Self-Organization in Biological Systems (Princeton University Press, Princeton, New Jersey, 2003).Google Scholar
Olfati-Saber, R. and Murray, R. M., “Consensus problems in networks of agents with switching topology and time-delays,” IEEE Trans. Autom. Control 49(9), 1520–1533 (2004).CrossRefGoogle Scholar
Murata, S. and Kurokawa, H., Self-Organizing Robots. Springer Tracts in Advanced Robotics, vol. 77 (Springer, Tokyo, 2012).CrossRefGoogle Scholar
Lee, D. and Spong, M. W., “Stable flocking of multiple inertial agents on balanced graphs,” IEEE Trans. Autom. Control 52(8), 1469–1475 (2007).CrossRefGoogle Scholar
Gu, D. B. and Wang, Z., “Leader-follower flocking: Algorithms and experiments,” IEEE Trans. Control Syst. Technol. 17(5), 1211–1219 (2009).Google Scholar
Rodriguez-Angeles, A. and Nijmeijer, H., “Mutual synchronization of robots via estimated state feedback: a cooperative approach,” IEEE Trans. Control Syst. Technol. 12(4), 542–554 (2004).CrossRefGoogle Scholar
Abdessameud, A., Tayebi, A. and Polushin, I. G., “Attitude synchronization of multiple rigid bodies with communication delays,” IEEE Trans. Autom. Control 57(9), 2405–2411 (2012).CrossRefGoogle Scholar
Wang, X., Yu, C. and Lin, Z., “A dual quaternion solution to attitude and position control for rigid-body coordination,” IEEE Trans. Rob. 28(5), 1162–1170 (2012).CrossRefGoogle Scholar
Qin, J., Zheng, W. and Gao, H., “Coordination of multiple agents with double-integrator dynamics under generalized interaction topologies,” IEEE Trans. Syst. Man Cybern. Part B: Cybern. 42(1), pp. 44–57 (2012).Google ScholarPubMed
Furno, L., Nielsen, M. C. and Blanke, M., “Centralised versus decentralised control reconfiguration for collaborating underwater robots,” IFAC-PapersOnLine 48(21), 732–739 (2015). Special Issue on 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes.CrossRefGoogle Scholar
Zhang, K. and Demetriou, M. A., “Adaptation and optimization of the synchronization gains in the adaptive spacecraft attitude synchronization,” Aerosp. Sci. Technol. 46, 116–123 (2015).CrossRefGoogle Scholar
Gunn, T. and Anderson, J., “Dynamic heterogeneous team formation for robotic urban search and rescue,” J. Comput. Syst. Sci. 81(3), 553–567 (2015). Special Issue on selected papers from the 4th International Conference on Ambient Systems, Networks and Technologies (ANT 2013).CrossRefGoogle Scholar
Shukla, A. and Karki, H., “Application of robotics in onshore oil and gas industry–A review part i,” Rob. Auton. Syst. Part B 75, 490–507 (2016).CrossRefGoogle Scholar
Knepper, R. A., Layton, T., Romanishin, J. and Rus, D., “IkeaBot: An Autonomous Multi-robot Coordinated Furniture Assembly System,” 2013 IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany (2013) pp. 855–862.CrossRefGoogle Scholar
Roa, M. A., Berenson, D. and Huang, W., “Mobile manipulation: Toward smart manufacturing [TC spotlight],” IEEE Rob. Autom. Mag. 22, 14–15 (2015).CrossRefGoogle Scholar
Bechar, A. and Vigneault, C., “Agricultural robots for field operations: Concepts and components,” Biosyst. Eng. 149, 94–111 (2016).CrossRefGoogle Scholar
Ren, W. and Cao, Y., Distributed Coordination of Multi-Agent Networks: Emergent Problems, Models, and Issues (Springer-Verlag London Limited, London, 2011).CrossRefGoogle Scholar
Nuño, E., Sarras, I. and Basañez, L., “Consensus in networks of nonidentical Euler-Lagrange systems using P+d controllers,” IEEE Transactions on Robotics, vol. 6, no. 29, pp. 1503–1508, 2013.CrossRefGoogle Scholar
Liu, Y., Min, H., Wang, S., Ma, L. and Liu, Z., “Consensus for multiple heterogeneous Euler-Lagrange systems with time-delay and jointly connected topologies,” J. Franklin Inst. 351(6), 3351–3363 (2014).CrossRefGoogle Scholar
Aldana, C. I., Romero, E., Nuño, E. and Basañez, L., “Pose consensus in networks of heterogeneous robots with variable time delays,” Int. J. Robust Nonlinear Control 25(14), 2279–2298 (2015).CrossRefGoogle Scholar
Aldana, C. I., Nuño, E., Basañez, L. and Romero, E., “Operational space consensus of multiple heterogeneous robots without velocity measurements,” J. Franklin Inst. 351, 1517–1539 (2014).CrossRefGoogle Scholar
Klotz, J., Kan, Z., Shea, J. M., Pasiliao, E. L. and Dixon, W. E., “Asymptotic synchronization of leader-follower networks of uncertain euler-lagrange systems,” IEEE Trans. Control Netw. Syst. 2(2), 174–182 (2015).CrossRefGoogle Scholar
Ren, W., Beard, R. W. and Atkins, E. M., “A Survey of Consensus Problems in Multi-agent Coordination,” Proceedings of the 2005, American Control Conference, 2005, Portland, OR (2005) pp. 1859–1864.CrossRefGoogle Scholar
Nuño, E., Ortega, R., Basañez, L. and Hill, D., “Synchronization of networks of nonidentical Euler-Lagrange systems with uncertain parameters and communication delays,” IEEE Trans. Autom. Control 56(4), 935–941 (2011).CrossRefGoogle Scholar
Tellez, F. O., Loukianov, A. G., Sanchez, E. N. and Corrochano, E. J. B., “Decentralized neural identification and control for uncertain nonlinear systems: Application to planar robot,” J. Franklin Inst. 347(6), 1015–1034 (2010).CrossRefGoogle Scholar
Chen, C., Liu, Y. J. and Wen, G. X., “Fuzzy neural network-based adaptive control for a class of uncertain nonlinear stochastic systems,” IEEE Trans. Cybern. 44, 583–593 (2014).CrossRefGoogle ScholarPubMed
Chen, W., Hua, S. and Zhang, H., “Consensus-based distributed cooperative learning from closed-loop neural control systems,” IEEE Trans. Neural Netw. Learn. Syst. 26(2), 331–345 (2015).CrossRefGoogle ScholarPubMed
Chen, W., Hua, S. and Sam Ge, S., “Consensus-based distributed cooperative learning control for a group of discrete-time nonlinear multi-agent systems using neural networks,” Automatica 50(9), 2254–2268 (2014).CrossRefGoogle Scholar
Hua, C. C., Yang, Y. and Guan, X., “Neural network-based adaptive position tracking control for bilateral teleoperation under constant time delay,” Neurocomputing 113(3), 204–212 (2013).CrossRefGoogle Scholar
Wang, H., “Passivity based synchronization for networked robotic systems with uncertain kinematics and dynamics,” Automatica 49(3), 755–761 (2013).CrossRefGoogle Scholar
Yang, Y., Ge, C., Wang, H., Li, X. and Hua, C., “Adaptive neural network based prescribed performance control for teleoperation system under input saturation,” J. Franklin Inst. 352(5), 1850–1866 (2015).CrossRefGoogle Scholar
Miranda, C., De-la Mora, A. and Nuño, E., “Control of Uncertain Teleoperators with Time-Delays using Artificial Neural Networks,” IFAC World Congress, Cape Town, South Africa (2014) pp. 1766–1771.Google Scholar
El-Ferik, S., Qureshi, A. and Lewis, F. L., “Neuro-adaptive cooperative tracking control of unknown higher-order affine nonlinear systems,” Automatica 50(3), 798–808 (2014).CrossRefGoogle Scholar
Zhao, D., Ni, W. and Zhu, Q., “A framework of neural networks based consensus control for multiple robotic manipulators,” Neurocomputing 140, 8–18 (2014).CrossRefGoogle Scholar
Paola, D. D., Naso, D. and Turchiano, B., “Consensus-Based Robust Decentralized Task Assignment for Heterogeneous Robot Networks,” Proceedings of the 2011 American Control Conference, San Francisco, CA, USA (2011) pp. 4711–4716.CrossRefGoogle Scholar
Yanguas-Rojas, D. and Mojica-Nava, E., “Exploration with heterogeneous robots networks for search and rescue,” IFAC-PapersOnLine 50(1), 7935–7940 (2017). Special Issue on 20th IFAC World Congress.CrossRefGoogle Scholar
Thunberg, J., Goncalves, J. and Hu, X., “Consensus and formation control on SE(3) for switching topologies,” Automatica 66, 109–121 (2016).CrossRefGoogle Scholar
Liu, Y.-C., “Distributed synchronization for heterogeneous robots with uncertain kinematics and dynamics under switching topologies,” J. Franklin Inst. 352(9), 3808–3826 (2015).CrossRefGoogle Scholar
Funda, J., Taylor, R. H. and Paul, R. P., “On homogeneous transforms, quaternions, and computational efficiency,” IEEE Trans. Rob. Autom. 6(3), 382–388 (1990).CrossRefGoogle Scholar
Kelly, R., Santibáñez, V. and Loría, A., Control of Robot Manipulators in Joint Space (Springer-Verlag London Limited, London, 2005).Google Scholar
Spong, M. W., Hutchinson, S. and Vidyasagar, M., Robot Modeling and Control, 1st ed. (John Wiley & Sons, Inc., Hoboken, NJ, USA, 2005).Google Scholar
Caccavale, F., Siciliano, B. and Villani, L., “The role of Euler parameters in robot control,” Asian J. Control 1(1), 25–34 (1999).CrossRefGoogle Scholar
Kuipers, J. B., Quaternions and Rotation Sequences: A Primer with Applications to Orbits, Aerospace and Virtual Reality. (Princeton University Press, Princeton, NJ, 1999).Google Scholar
Chen, C. and Jackson, D., “Parameterization and evaluation of robotic orientation workspace: A geometric treatment,” IEEE Trans. Rob. 27(4), 656–663 (2011).CrossRefGoogle Scholar
Wen, J. Y. and Kreutz-Delgado, K., “The attitude control problem,” IEEE Trans. Autom. Control 36(10), 1148–1162 (1991).CrossRefGoogle Scholar
Fjellstad, O. E., Control of Unmanned Underwater Vehicles in Six Degrees of Freedom A Quaternion Feedback Approach Ph.D. Thesis (The Norwegian Institute of Technology, 1994).Google Scholar
Siciliano, B., Sciavicco, L., Villani, L. and Oriolo, G., Robotics, Modelling, Planning and Control (Springer-Verlag London Limited, London, 2009).Google Scholar
Patiño, H. D., Carelli, R. and Kuchen, B. R., “Neural networks for advanced control of robot manipulators,” IEEE Trans. Neural Netw. 13(2), 343–354 (2002).CrossRefGoogle ScholarPubMed
Lee, M. J. and Choi, Y. K., “An adaptive neurocontroller using RBFN for robot manipulators,” IEEE Trans. Ind. Electron. 51(3), 711–717 (2004).CrossRefGoogle Scholar
Liu, J., Radial Basis Function (RBF) Neural Network Control for Mechanical Systems (Springer-Verlag, Berlin Heidelberg, 2013).CrossRefGoogle Scholar
Nuño, E., Aldana, C. I. and Basañez, L., “Task space consensus in networks of heterogeneous and uncertain robotic systems with variable time-delays,” Int. J. Adapt. Control Signal Process. 31(6), 917–937 (2017).CrossRefGoogle Scholar
Liu, Y.-C. and Chopra, N., “Controlled synchronization of heterogeneous robotic manipulators in the task space,” IEEE Trans. Rob. 28(1), 268–275 (2012).CrossRefGoogle Scholar
Corke, P. I., Robotics, Vision & Control: Fundamental Algorithms in MATLAB (Springer, Berlin Heidelberg, 2011).CrossRefGoogle Scholar
Ejarque, R. and Basañez, L., “Control adaptatiu per al robot manipulador KUKA Light Weight Robot 4+,” Technical report, Institute of Industrial and Control Engineering, Technical University of Catalonia, 2014. http://hdl.handle.net/2099.1/24233.Google Scholar
Spurrier, R. A., “Comment on: Singularity-Free Extraction of a Quaternion from a Direction-Cosine Matrix,” J. Spacecraft Rockets 15(4), 255–255 (1978).CrossRefGoogle Scholar