Simulación de comunicación emergente en robótica: apoyo a las cadenas de suministro empleando computación evolutiva
Publicado 2024-07-03
Cómo citar
Resumen
Se presenta un modelo emergente de cooperación y comunicación que involucra un conjunto de robots MarxBot. La tarea consiste en recolección de materia prima almacenada y su depósito en la banda de procesamiento, además de la recolección del elemento fabricado y su depósito en una banda de empaquetado. Los robots son controlados mediante redes neuronales artificiales que son optimizadas mediante un algoritmo genético en el simulador robótico conocido como FARSA (framework for autonomous robotics simulation and analysis). Se prueba en un ambiente simulado de grupos homogéneos de robots con comunicación emergente basada en diodos emisores de luz (LED, por sus siglas en inglés), los cuales tienen un mejor rendimiento en la tarea propuesta que un sistema de comunicación con señales preestablecidas y un grupo que no tiene capacidad de comunicación. Esto se debe al nivel de organización provisto por la emergencia de señales que proviene del grupo y de la interacción con el entorno. Esto demuestra que la perspectiva de la robótica evolutiva es aplicable a las necesidades de la Industria 4.0.
Citas
- Abdolrasol, M. G. M., Hussain, S. M. S., Ustun, T. S., Sarker, M. R., Hannan, M. A., Mohamed, R., Ali, J. A., Mekhilef, S., & Milad, A. (2021). Artificial neural networks based optimization techniques: a review. Electronics, 10(21), 2689. https://doi.org/10.3390/electronics10212689
- Aldana-Franco, F., Montes-González, F., & Nolfi, S. (2024). The improvement of signal communication for a foraging task using evolutionary robotics. Journal of Applied Research and Technology, 22(1), 90-101. https://doi.org/10.22201/icat.24486736e.2024.22.1.1652
- Arunkumar, S., Suganeswaran, K., Nithyavathy, N., & Gobinath, V. K. (2020). Semi-automatic cloth bag making machine. Materials Today: Proceedings, 33, 3454-3457. https://doi.org/10.1016/j.matpr.2020.05.353
- Asad, R., Hayakawa, T., & Yasuda, T. (2023). Evolutionary design of cooperative transport behavior for a heterogeneous robotic swarm. Journal of Robotics and Mechatronics, 35(4), 1007-1015. https://doi.org/10.20965/jrm.2023.p1007
- Bragança, S., Costa, E., Castellucci, I., & Arezes, P. M. (2019). A brief overview of the use of collaborative robots in industry 4.0: human role and safety. Occupational and Environmental Safety and Health, 641-650. https://doi.org/10.1007/978-3-030-14730-3_68
- Cáceres, C. A., Rosário, J. M., & Amaya, D. (2020). Control structure for a car-like robot using artificial neural networks and genetic algorithms. Neural Computing and Applications, 32(20), 15771-15784. https://doi.org/10.1007/s00521-018-3514-1
- Cambier, N., Miletitch, R., Frémont, V., Dorigo, M., Ferrante, E., & Trianni, V. (2020). Language evolution in swarm robotics: a perspective. Frontiers in Robotics and AI, 7, 12. https://doi.org/10.3389/frobt.2020.00012
- Carvalho, J. T., & Nolfi, S. (2024). The role of morphological variation in evolutionary robotics: maximizing performance and robustness. Evolutionary Computation, 1-18. https://doi.org/10.1162/evco_a_00336
- Castelló, E., Jiménez, E., Lopez-Presa, J. L., & Martín-Rueda, J. (2021). Following leaders in byzantine multirobot systems by using blockchain technology. IEEE Transactions on Robotics, 38(2), 1101-1117. https://doi.org/10.1109/TRO.2021.3104243
- Chaabouni, R., Kharitonov, E., Dupoux, E., & Baroni, M. (2021). Communicating artificial neural networks develop efficient color-naming systems. Proceedings of the National Academy of Sciences, 118(12), e2016569118. https://doi.org/10.1073/pnas.2016569118
- Chavan, S., Patil, U., Koshy, S. S., & Srikanth, S. V. (2021). Garbage zero (Garb0): an IoT framework for effective garbage management in smart cities. In 2021 International Conference on Artificial intelligence and Smart Systems (ICAIS), 1336-1342. IEEE. https://doi.org/10.1109/ICAIS50930.2021.9395970
- Çınar, Z. M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability, 12(19), 8211. https://doi.org/10.3390/su12198211
- Devi, K. V. R., Smitha, B. S., Lakhanpal, S., Kalra, R., Sethi, V. A., & Thajil, S. K. (2024). A review: swarm robotics: cooperative control in multi-agent systems. In E3S Web of Conferences, 505, (03013). EDP Sciences. https://doi.org/10.1051/e3sconf/202450503013
- Doncieux, S., Bredeche, N., Mouret, J. B., & Eiben, A. E. G. (2015). Evolutionary robotics: what, why, and where to. Frontiers in Robotics and AI, 2, 4. https://doi.org/10.3389/frobt.2015.00004
- Feng, H., Xue, Y., Li, H., Tang, Z., Wang, W., Wei, Z., Zeng, G., Li, M., & Dai, J. S. (2023). Deformable morphing and multivariable stiffness in the evolutionary robotics. International Journal of Automotive Manufacturing and Materials, 2(4), 1. https://doi.org/10.53941/ijamm.2023.100013
- Ghadge, A., Er Kara, M., Moradlou, H., & Goswami, M. (2020). The impact of Industry 4.0 implementation on supply chains. Journal of Manufacturing Technology Management, 31(4), 669-686. https://doi.org/10.1108/JMTM-10-2019-0368
- Gigliotta, O. (2018). Equal but different: task allocation in homogeneous communicating robots. Neurocomputing, 272, 3-9. https://doi.org/10.1016/j.neucom.2017.05.093
- Goncalves, C. G., Winroth, M. P., & Ribeiro da Silva, E. H. D. (2020). Sustainable manufacturing in Industry 4.0: an emerging research agenda. International Journal of Production Research, 58(5), 1462-1484. https://doi.org/10.1080/00207543.2019.1652777
- Guan, W., Wu, Y., Xie, C., Chen, H., Cai, Y., & Chen, Y. (2017). High-precision approach to localization scheme of visible light communication based on artificial neural networks and modified genetic algorithms. Optical Engineering, 56(10), 106103. https://doi.org/10.1117/1.OE.56.10.106103
- Gupta, N., Khosravy, M., Patel, N., Gupta, S., & Varshney, G. (2020). Evolutionary artificial neural networks: comparative study on state-of-the-art optimizers. Frontier Applications of Nature Inspired Computation, 302-318. https://doi.org/10.1007/978-981-15-2133-1_14
- Hiraga, M., & Ohkura, K. (2022). Topology and weight evolving artificial neural networks in cooperative transport by a robotic swarm. Artificial Life and Robotics, 27(2), 324-332. https://doi.org/10.1007/s10015-021-00716-9
- Howard, D., Collins, J., & Robinson, N. (2022). Taking shape: a perspective on the future of embodied cognition and a new generation of evolutionary robotics. IOP Conference Series: Materials Science and Engineering, 1261(1), 012018. https://doi.org/10.1088/1757-899X/1261/1/012018
- Husbands, P., Shim, Y., Garvie, M., Dewar, A., Domcsek, N., Graham, P., Knight, J., Nowotny, T., & Philippides, A. (2021). Recent advances in evolutionary and bio-inspired adaptive robotics: exploiting embodied dynamics. Applied Intelligence, 51(9), 6467-6496. https://doi.org/10.1007/s10489-021-02275-9
- Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2021). Substantial capabilities of robotics in enhancing industry 4.0 implementation. Cognitive Robotics, 1, 58-75. https://doi.org/10.1016/j.cogr.2021.06.001
- Karten, S., Tucker, M., Li, H., Kailas, S., Lewis, M., & Sycara, K. (2023). Interpretable learned emergent communication for human-agent teams. IEEE Transactions on Cognitive and Developmental Systems, 15(4). https://doi.org/10.1109/TCDS.2023.3236599
- Kohl, P. L., & Rutschmann, B. (2021). Honey bees communicate distance via non-linear waggle duration functions. PeerJ, 9, e11187. https://doi.org/10.7717/peerj.11187
- Lan, Q., Wen, D., Zhang, Z., Zeng, Q., Chen, X., Popovski, P., & Huang, K. (2021). What is semantic communication? A view on conveying meaning in the era of machine intelligence. Journal of Communications and Information Networks, 6(4), 336-371. https://doi.org/10.23919/JCIN.2021.9663101
- Lazaridou, A., & Baroni, M. (2020). Emergent multi-agent communication in the deep learning era. Computation and Language, 1. https://doi.org/10.48550/arXiv.2006.02419
- Lins, R. G., & Givigi, S. N. (2021). Cooperative robotics and machine learning for smart manufacturing: platform design and trends within the context of industrial internet of things. IEEE Access, 9, 95444. https://doi.org/10.1109/ACCESS.2021.3094374
- López, E. J., Leonards, U., & Hermann, G. (2022). Cognitive control decision and human-robot collaboration. In A. Cangelosi & M. Asada (eds.), Cognitive Robotics (pp. 337-360). MIT Press. https://doi.org/10.7551/mitpress/13780.003.0023
- Mastos, T. D., Nizamis, A., Vafeiadis, T., Alexopoulos, N., Ntinas, C., Gkortzis D., Papadopoulos, A., Ioannidis, D., & Tzovaras, D. (2020). Industry 4.0 sustainable supply chains: an application of an IoT enabled scrap metal management solution. Journal of cleaner production, 269, 122377. https://doi.org/10.1016/j.jclepro.2020.122377
- Massera, G., Ferrauto, T., Gigliotta, O., & Nolfi, S. (2013). Farsa: an open software tool for embodied cognitive science. Advances in Artificial Life, (12), 538-545. https://doi.org/10.1162/978-0-262-31709-2-ch078
- Shamout, M., Ben-Abdallah, R., Alshurideh, M., Alzoubi, H., Kurdi, B. A., & Hamadneh, S. (2022). A conceptual model for the adoption of autonomous robots in supply chain and logistics industry. Uncertain Supply Chain Management, 10(2), 577-592. https://doi.org/10.5267/j.uscm.2021.11.006
- Milano, N., Carvalho, J. T., & Nolfi, S. (2019). Moderate environmental variation across generations promotes the evolution of robust solutions. Artificial life, 24(4), 277-295. https://doi.org/10.1162/artl_a_00274
- Miletitch, R., Reina, A., Dorigo, M., & Trianni, V. (2022). Emergent naming conventions in a foraging robot swarm. Swarm Intelligence, 16(3), 211-232. https://doi.org/10.1007/s11721-022-00212-1
- Mourtzis, D., Papakostas, N., & Makris, S. (2019). Complexity in Industry 4.0. Systems and Networks. Complexity, 2019, 1-2. https://doi.org/10.1155/2019/7817046
- Muralidharan, A., & Mostofi, Y. (2021). Communication-aware robotics: Exploiting motion for communication. Annual Review of Control, Robotics, and Autonomous Systems, 4, 115-139. https://doi.org/10.1146/annurev-control-071420-080708
- Pagliuca, P., & Nolfi, S. (2022). The dynamic of body and brain co-evolution. Adaptive Behavior, 30(3), 245-255. https://doi.org/10.1177/1059712321994685
- Pagliuca, P., & Vitanza, A. (2023). N-Mates evaluation: a new method to improve the performance of genetic algorithms in heterogeneous multi-agent systems. https://ceur-ws.org/Vol-3579/paper9.pdf
- Palacios-Leyva, R., Aldana-Franco, F., Lara-Guzmán, B., & Montes-González, F. (2017). The impact of population composition for cooperation emergence in evolutionary robotics. International Journal of Combinatorial Optimization Problems and Informatics, 8(3), 20-32. https://ijcopi.org/ojs/article/view/15
- Patel, S., Wani, S., Jain, U., Schwing, A., Lazebnik, S., Savva, M., & Chang, A. X. (2021). Interpretation of emergent communication in heterogeneous collaborative embodied agents. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 15953-15963). https://doi.org/10.1109/ICCV48922.2021.01565
- Sathiya, V., Chinnadurai, M., Ramabalan, S., & Appolloni, A. (2021). Mobile robots and evolutionary optimization algorithms for green supply chain management in a used-car resale company. Environment, Development and Sustainability, 23(6), 9110-9138. https://doi.org/10.1007/s10668-020-01015-2
- Silva, F., Duarte, M., Correia, L., Oliveira, S. M., & Christensen, A. L. (2016). Open issues in evolutionary robotics. Evolutionary Computation, 24(2), 205-236. https://doi.org/10.1162/EVCO_a_00172
- Simione, L., & Nolfi, S. (2020). Long-term progress and behavoir complexification in competitive coevolution. Artificial Life, 26(4), 409-430. https://doi.org/10.1162/artl_a_00329
- Steyven, A., Hart, E., & Paechter, B. (2015). The cost of communication: environmental pressure and survivability in mEDEA. In Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference, 1239-1240. https://doi.org/10.1145/2739482.2768489
- Thomas, C. K., & Saad, W. (2023). Neuro-symbolic causal reasoning meets signaling game for emergent semantic communications. IEEE Transactions on Wireless Communications, 23(5). https://doi.org/10.1109/TWC.2023.3319981
- Vaidya, S., Ambad, P., & Bhosle, S. (2018). Industry 4.0–a glimpse. Procedia Manufacturing, 20, 233-238. https://doi.org/10.1016/j.promfg.2018.02.034
- Wan, J., Tang, S., Li, D., Imran, M., Zhang, C., Liu, C., & Pang, Z. (2019). Reconfigurable smart factory for drug packing in healthcare industry 4.0. IEEE transactions on industrial informatics, 15(1), 507-516. https://doi.org/10.1109/TII.2018.2843811
- Yang, J. Q., Wang, R., Ren, Y., Mao, J. Y., Wang, Z. P., Zhou, Y., & Han, S. T. (2020). Neuromorphic engineering: from biological to spike‐based hardware nervous systems. Advanced Materials, 32(52), 2003610. https://doi.org/10.1002/adma.202003610
- Zhou, K., Liu, T., & Zhou, L. (2015). Industry 4.0: Towards future industrial opportunities and challenges. In 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 2147-2152. https://doi.org/10.1109/FSKD.2015.7382284