Muestreo y comunicaciónimpacto en el control de formaciones en sistemas multi-robot heterogéneos

  1. Mañas-Álvarez, Francisco-José 1
  2. Guinaldo, María 1
  3. Dormido, Raquel 1
  4. Dormido, Sebastián 1
  1. 1 Universidad Nacional de Educación a Distancia
    info

    Universidad Nacional de Educación a Distancia

    Madrid, España

    ROR https://ror.org/02msb5n36

Revue:
Revista iberoamericana de automática e informática industrial ( RIAI )

ISSN: 1697-7920

Année de publication: 2024

Volumen: 21

Número: 2

Pages: 125-136

Type: Article

DOI: 10.4995/RIAI.2023.20155 DIALNET GOOGLE SCHOLAR lock_openAccès ouvert editor

D'autres publications dans: Revista iberoamericana de automática e informática industrial ( RIAI )

Projets liés

Control de Sistemas Ciberfísicos Cooperativos Multiagente: aspectos teóricos y prácticos

9912

Control resiliente y seguro de sistemas ciberfísicos cooperativos

9032

Résumé

This work presents the analysis of the sampling an communication frequencies in a multi-robot system (MRS) and its effect over the temporal performance and computational load. The experimental system is composed of mobile robots (the Khepera IV), and a type of aerial robots, the Crazyflie 2.1. The analysis is performed for the formation control of the MRS from initial conditions to a desired formation, which is defined in terms of relative distances between agents. Three scenarios regarding the control architecture are evaluated: centralized, distributed in ROS 2, and distributed onboard the robot. The minimum operating frequency for periodic sampling is determined, and an event-based sampling protocol is presented to reduce the number of transmitted messages. In this case, the optimal constant threshold that provides an equivalent temporal performance is determined, but with a reduction in the number of samples of around 80%.

€ Voir financement

Information sur le financement

Financeurs

Références bibliographiques

  • Amsters, R., Slaets, P., 2020. Turtlebot 3 as a robotics education platform. In: Robotics in Education. Springer, Cham, Switzerland, pp. 170-181. https://doi.org/10.1007/978-3-030-26945-6
  • Anderson, B. D., Yu, C., Fidan, B., Hendrickx, J. M., 2008. Rigid graph control architectures for autonomous formations. IEEE Control Systems Magazine 28 (6), 48-63. https://doi.org/10.1109/MCS.2008.929280
  • Aranda, M., López-Nicolás, G., Sagüés, C., Mezouar, Y., 2015. Formation control of mobile robots using multiple aerial cameras. IEEE Transactions on Robotics 31 (4), 1064-1071. https://doi.org/10.1109/TRO.2015.2452777
  • Aranda-Escolastico, E., Guinaldo, M., Heradio, R., Chacon, J., Vargas, H., Sánchez, J., Dormido, S., 2020. Event-based control: A bibliometric analysis of twenty years of research. IEEE Access 8, 47188-47208. https://doi.org/10.1109/ACCESS.2020.2978174
  • Bansal, S., Akametalu, A. K., Jiang, F. J., Laine, F., Tomlin, C. J., 2016. Learning quadrotor dynamics using neural network for flight control. In: 2016 IEEE 55th Conference on Decision and Control (CDC). IEEE, Las Vegas, NV, USA, pp. 4653-4660. https://doi.org/10.1109/CDC.2016.7798978
  • Barra, J., Scorletti, G., Lesecq, S., Zarudniev, M., Blanco, E., 2020. Attraction domain estimation of linear controllers for the attitude control of vtol vehicles: P/pi control of a quadrotor. In: 2020 European Control Conference (ECC). IEEE, St. Petersburg, Russia, pp. 1644-1649. https://doi.org/10.23919/ECC51009.2020.9143612
  • Bogdan, P., Marculescu, R., 2011. Towards a science of cyber-physical systems design. In: 2011 IEEE/ACM Second international conference on cyberphysical systems, Chicago, IL, USA. IEEE, Chicago, IL, USA, pp. 99-108. https://doi.org/10.1109/ICCPS.2011.14
  • Budaciu, C., Botezatu, N., Kloetzer, M., Burlacu, A., 2019. On the evaluation of the crazyflie modular quadcopter system. In: 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE, Zaragoza, Spain, pp. 1189-1195. https://doi.org/10.1109/ETFA.2019.8869202
  • Chee, K. Y., Jiahao, T. Z., Hsieh, M. A., 2022. Knode-mpc: A knowledge-based data-driven predictive control framework for aerial robots. IEEE Robotics and Automation Letters 7 (2), 2819-2826. https://doi.org/10.1109/LRA.2022.3144787
  • Cornejo, J., Magallanes, J., Denegri, E., Canahuire, R., 2018. Trajectory tracking control of a differential wheeled mobile robot: a polar coordinates control and lqr comparison. In: 2018 IEEE XXV International Conference on Electronics, Electrical Engineering and Computing (INTERCON). IEEE, Lima, Peru, pp. 1-4. https://doi.org/10.1109/INTERCON.2018.8526366
  • Cortés, J., Egerstedt, M., 2017. Coordinated control of multi-robot systems: A survey. SICE Journal of Control, Measurement, and System Integration 10 (6), 495-503. https://doi.org/10.9746/jcmsi.10.495
  • Didier, A., Parsi, A., Coulson, J., Smith, R. S., 2021. Robust adaptive model predictive control of quadrotors. In: 2021 European Control Conference (ECC). IEEE, Delft, Netherlands, pp. 657-662. https://doi.org/10.23919/ECC54610.2021.9654893
  • Fidan, B., Yu, C., Anderson, B. D., 2007. Acquiring and maintaining persistence of autonomous multi-vehicle formations. IET Control Theory & Applications 1 (2), 452-460. https://doi.org/10.1049/iet-cta:20050409
  • Garcia, G. A., Kim, A. R., Jackson, E., Keshmiri, S. S., Shukla, D., 2017. Modeling and flight control of a commercial nano quadrotor. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, Miami, FL, USA, pp. 524-532. https://doi.org/10.1109/ICUAS.2017.7991439
  • Giernacki, W., Rao, J., Sladic, S., Bondyra, A., Retinger, M., Espinoza-Fraire, T., 2022. Dji tello quadrotor as a platform for research and education in mobile robotics and control engineering. In: 2022 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, Dubrovnik, Croatia, pp. 735-744. https://doi.org/10.1109/ICUAS54217.2022.9836168
  • Giernacki, W., Skwierczynsky, M., Witwicki, W., Wronski, P., Kozierski, P., 2017. Crazyflie 2.0 quadrotor as a platform for research and education in robotics and control engineering. In: 2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR). IEEE, Miedzyzdroje, Poland, pp. 37-42. https://doi.org/10.1109/MMAR.2017.8046794
  • Grasso, P., Innocente, M. S., Tai, J. J., Haas, O., Dizqah, A. M., 2022. Analysis and accuracy improvement of uwb-tdoa-based indoor positioning system. Sensors 22 (23), 9136. https://doi.org/10.3390/s22239136
  • Guinaldo, M., Dimarogonas, D. V., Johansson, K. H., Sánchez, J., Dormido, S., 2013. Distributed event-based control strategies for interconnected linear systems. IET Control Theory & Applications 7 (6), 877-886. https://doi.org/10.1049/iet-cta.2012.0525
  • Guinaldo, M., Sánchez, J., Dormido, S., 2017. Control en red basado en eventos: de lo centralizado a lo distribuido. Revista Iberoamericana de Automática e Inform'áica Industrial 14 (1), 16-30. https://doi.org/10.1016/j.riai.2016.09.007
  • Hasseni, S.-E.-I., Abdou, L., Glida, H.-E., 2021. Parameters tuning of a quadrotor pid controllers by using nature-inspired algorithms. Evolutionary Intelligence 14, 61-73. https://doi.org/10.1007/s12065-019-00312-8
  • Heemels, W. P., Johansson, K. H., Tabuada, P., 2012. An introduction to eventtriggered and self-triggered control. In: 2012 IEEE 51st IEEE Conference on Decision and Control (CDC). IEEE, Maui, HI, USA, pp. 3270-3285. https://doi.org/10.1109/CDC.2012.6425820
  • Heikkinen, J., Minav, T., Stotckaia, A. D., 2017. Self-tuning parameter fuzzy pid controller for autonomous differential drive mobile robot. In: 2017 XXIEEE international conference on soft computing and measurements (SCM). IEEE, St. Petersburg, Russia, pp. 382-385. https://doi.org/10.1109/SCM.2017.7970592
  • Krick, L., Broucke, M. E., Francis, B. A., 2009. Stabilisation of infinitesimally rigid formations of multi-robot networks. International Journal of Control 82 (3), 423-439. https://doi.org/10.1080/00207170802108441
  • Lamraoui, H. C., Qidan, Z., Benrabah, A., 2017. Dynamic velocity tracking control of differential-drive mobile robot based on ladrc. In: 2017 IEEE International Conference on Real-time Computing and Robotics (RCAR). IEEE, Okinawa, Japan, pp. 633-638. https://doi.org/10.1109/RCAR.2017.8311934
  • Leonard, N. E., Paley, D. A., Lekien, F., Sepulchre, R., Fratantoni, D. M., Davis, R. E., 2007. Collective motion, sensor networks, and ocean sampling. Proceedings of the IEEE 95 (1), 48-74. https://doi.org/10.1109/JPROC.2006.887295
  • Mañas-Álvarez, F. J., Guinaldo, M., Dormido, R., Dormido-Canto, S., 2023. Scalability of cyber-physical systems with real and virtual robots in ros 2. Sensors 23 (13), 6073. https://doi.org/10.3390/s23136073
  • Mañas-Álvarez, F.-J., Guinaldo, M., Dormido, R., Socas, R., Dormido, S., 2022. Formation by consensus in heterogeneous robotic swarms with twinsin- the-loop. In: ROBOT2022: Fifth Iberian Robotics Conference. Springer, Cham, Switzerland, pp. 435-447. https://doi.org/10.1007/978-3-031-21065-5
  • Mañas-Álvarez, F.-J., Guinaldo, M., Dormido, R., Dormido, S., 2023. Robotic park: Multi-agent platform for teaching control and robotics. IEEE Access 11, 34899-34911. https://doi.org/10.1109/ACCESS.2023.3264508
  • Nguyen, N. P., Lee, B. H., Xuan-Mung, N., Ha, L. N. N. T., Jeong, H. S., Lee, S. T., Hong, S. K., 2022. Persistent charging system for crazyflie platform. Drones 6 (8), 212. https://doi.org/10.3390/drones6080212
  • Oh, K.-K., Park, M.-C., Ahn, H.-S., 2015. A survey of multi-agent formation control. Automatica 53, 424-440. https://doi.org/10.1016/j.automatica.2014.10.022
  • Pichierri, L., Testa, A., Notarstefano, G., 2023. Crazychoir: Flying swarms of crazyflie quadrotors in ros 2. IEEE Robotics and Automation Letters 8 (8), 4713-4720. https://doi.org/10.1109/LRA.2023.3286814
  • Ren, W., Sorensen, N., 2008. Distributed coordination architecture for multirobot formation control. Robotics and Autonomous Systems 56 (4), 324- 333. https://doi.org/10.1016/j.robot.2007.08.005
  • Silano, G., Aucone, E., Iannelli, L., 2018. Crazys: a software-in-the-loop platform for the crazyflie 2.0 nano-quadcopter. In: 2018 26th Mediterranean Conference on Control and Automation (MED). IEEE, Zadar, Croatia, pp. 1-6. https://doi.org/10.1109/MED.2018.8442759
  • Soares, J. M., Navarro, I., Martinoli, A., 2016. The khepera iv mobile robot: performance evaluation, sensory data and software toolbox. In: Robot 2015: Second Iberian Robotics Conference: Advances in Robotics, Volume 1. Springer, Cham, Switzerland, pp. 767-781. https://doi.org/10.1007/978-3-319-27146-0
  • Stefek, A., Pham, V. T., Krivanek, V., Pham, K. L., 2021. Optimization of fuzzy logic controller used for a differential drive wheeled mobile robot. Applied Sciences 11 (13), 6023. https://doi.org/10.3390/app11136023
  • Taffanel, A., Rousselot, B., Danielsson, J., McGuire, K., Richardsson, K., Eliasson, M., Antonsson, T., H¨onig, W., 2021. Lighthouse positioning system: Dataset, accuracy, and precision for uav research. In: ICRA Workshop on Robot Swarms in the Real World. ArXiv. DOI: 10.48550/arXiv.2104.11523
  • Vázquez, U., González-Sierra, J., Fernández-Anaya, G., Hernández-Martínez, E. G., 2021. Análisis del desempeño de un control pid de orden fraccional en un robot móvil diferencial. Revista Iberoamericana de Automática e Informática Industrial 19 (1), 74-83. https://doi.org/10.4995/riai.2021.15036
  • Vicon Motion Systems, 2022. Accuracy in motion. [Consulta el 01-07-2023]. URL: https://www.vicon.com/wp-content/uploads/2022/07/Vicon-Metrology-Solutions.pdf
  • Vágner, M., Palkovics, D., Kovács, L., 2022. 3d localization and data quality estimation with marvelmind. In: 2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS). IEEE, Debrecen, Hungary, pp. 302- 307. https://doi.org/10.1109/CITDS54976.2022.9914386
  • Zekry, O. H., Attia, T., Hafez, A. T., Ashry, M., 2023. Pid trajectory tracking control of crazyflie nanoquadcopter based on genetic algorithm. In: 2023 IEEE Aerospace Conference. IEEE, Big Sky, MT, USA, pp. 1-8. https://doi.org/10.1109/AERO55745.2023.10115538
La source de données: Dialnet