Optimización energética en robots agrícolas con sistemas predictivos y Ventana Dinámica
DOI:
https://doi.org/10.17979/ja-cea.2024.45.10887Palabras clave:
Control predictivo de modelos y basado en la optimización, Mobile robots, Robot móviles, Seguimiento de trayectorias y seguimiento de caminos, Guiado y control, Control predictivoResumen
En esta investigación, introducimos un nuevo Enfoque de Ventana Dinámica Predictiva (P-DWA), donde el algoritmo no solo anticipa la trayectoria óptima en términos de tiempo, sino que también evalúa el consumo energético del movimiento del robot móvil. El P-DWA predice nueve posibles destinos, evalúa su rendimiento temporal y elige las tres mejores trayectorias. Mediante el modelado de los motores del robot móvil, se logra estimar el consumo energético y el par requerido para un mapa 2D y de las trayectorias predichas se determina el consumo de cada una de ellas en vatios-hora (W/h), para optar por aquellas que menor consumo requieran. Los resultados muestran que, mediante la consideración energética, es posible llegar a reducir el 9% del consumo energético comparación con el enfoque de Ventana Dinámica convencional.
Citas
Botta, A., Cavallone, P., Baglieri, L., Colucci, G., Tagliavini, L., Quaglia, G., 2022. A Review of Robots, Perception, and Tasks in Precision Agriculture. Applied Mechanics 3, 830–854. https://doi.org/10.3390/applmech3030049 DOI: https://doi.org/10.3390/applmech3030049
Chen, C., Pei, L., Xu, C., Zou, D., Qi, Y., Zhu, Y., Li, T., 2019. Trajectory Optimization of LiDAR SLAM Based on Local Pose Graph, in: Sun, J., Yang, C., Yang, Y. (Eds.), China Satellite Navigation Conference (CSNC) 2019 Proceedings, Lecture Notes in Electrical Engineering. Springer Singapore, Singapore, pp. 360–370. https://doi.org/10.1007/978-981-13-7751-8_36 DOI: https://doi.org/10.1007/978-981-13-7751-8_36
Cheng, C., Fu, J., Su, H., Ren, L., 2023. Recent Advancements in Agriculture Robots: Benefits and Challenges. Machines 11, 48. https://doi.org/10.3390/machines11010048 DOI: https://doi.org/10.3390/machines11010048
Cornejo-Lupa, M.A., Ticona-Herrera, R.P., Cardinale, Y., Barrios-Aranibar, D., 2021. A Survey of Ontologies for Simultaneous Localization and Mapping in Mobile Robots. ACM Comput. Surv. 53, 1–26. https://doi.org/10.1145/3408316 DOI: https://doi.org/10.1145/3408316
Emmi, L., Fernández, R., Gonzalez-de-Santos, P., 2023. An Efficient Guiding Manager for Ground Mobile Robots in Agriculture. Robotics 13, 6. https://doi.org/10.3390/robotics13010006 DOI: https://doi.org/10.3390/robotics13010006
Fox, D., Burgard, W., Thrun, S., 1997. The dynamic window approach to collision avoidance. IEEE Robot. Automat. Mag. 4, 23–33. https://doi.org/10.1109/100.580977 DOI: https://doi.org/10.1109/100.580977
García, C.E., Prett, D.M., Morari, M., 1989. Model predictive control: Theory and practice—A survey. Automatica 25, 335–348. https://doi.org/10.1016/0005-1098(89)90002-2 DOI: https://doi.org/10.1016/0005-1098(89)90002-2
Karaman, S., Frazzoli, E., 2011. Sampling-based algorithms for optimal motion planning. The International Journal of Robotics Research 30, 846–894. https://doi.org/10.1177/0278364911406761 DOI: https://doi.org/10.1177/0278364911406761
Kim, J., Yang, G.-H., 2022. Improvement of Dynamic Window Approach Using Reinforcement Learning in Dynamic Environments. Int. J. Control Autom. Syst. 20, 2983–2992. https://doi.org/10.1007/s12555-021-0462-9 DOI: https://doi.org/10.1007/s12555-021-0462-9
Kumar, A., Maneesha, Pandey, P.K., 2024. Advances in Simultaneous Localization and Mapping (SLAM) for Autonomous Mobile Robot Navigation, in: Uddin, M.S., Bansal, J.C. (Eds.), Proceedings of International Joint Conference on Advances in Computational Intelligence, Algorithms for Intelligent Systems. Springer Nature Singapore, Singapore, pp. 481–493. https://doi.org/10.1007/978-981-97-0180-3_38 DOI: https://doi.org/10.1007/978-981-97-0180-3_38
Kunwar, F., Benhabib, B., 2008. Advanced Predictive Guidance Navigation for Mobile Robots: A Novel Strategy for Rendezvous in Dynamic Settings. International Journal on Smart Sensing and Intelligent Systems 1, 858–890. https://doi.org/10.21307/ijssis-2017-325 DOI: https://doi.org/10.21307/ijssis-2017-325
Liu, C., Lee, S., Varnhagen, S., Tseng, H.E., 2017. Path planning for autonomous vehicles using model predictive control, in: 2017 IEEE Intelligent Vehicles Symposium (IV). Presented at the 2017 IEEE Intelligent Vehicles Symposium (IV), IEEE, Los Angeles, CA, USA, pp. 174–179. https://doi.org/10.1109/IVS.2017.7995716 DOI: https://doi.org/10.1109/IVS.2017.7995716
Loganathan, A., Ahmad, N.S., 2023. A systematic review on recent advances in autonomous mobile robot navigation. Engineering Science and Technology, an International Journal 40, 101343. https://doi.org/10.1016/j.jestch.2023.101343 DOI: https://doi.org/10.1016/j.jestch.2023.101343
Missura, M., Bennewitz, M., 2019. Predictive Collision Avoidance for the Dynamic Window Approach, in: 2019 International Conference on Robotics and Automation (ICRA). Presented at the 2019 International Conference on Robotics and Automation (ICRA), IEEE, Montreal, QC, Canada, pp. 8620–8626. https://doi.org/10.1109/ICRA.2019.8794386 DOI: https://doi.org/10.1109/ICRA.2019.8794386
Rosolia, U., Zhang, X., Borrelli, F., 2018. Data-Driven Predictive Control for Autonomous Systems. Annu. Rev. Control Robot. Auton. Syst. 1, 259–286. https://doi.org/10.1146/annurev-control-060117-105215 DOI: https://doi.org/10.1146/annurev-control-060117-105215
Song, K.-T., Chiu, Y.-H., Kang, L.-R., Song, S.-H., Yang, C.-A., Lu, P.-C., Ou, S.-Q., 2018. Navigation Control Design of a Mobile Robot by Integrating Obstacle Avoidance and LiDAR SLAM, in: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Presented at the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, Miyazaki, Japan, pp. 1833–1838. https://doi.org/10.1109/SMC.2018.00317 DOI: https://doi.org/10.1109/SMC.2018.00317
Teso-Fz-Betoño, D., Zulueta, E., Fernandez-Gamiz, U., Saenz-Aguirre, A., Martinez, R., 2019. Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvement. Electronics 8, 935. https://doi.org/10.3390/electronics8090935 DOI: https://doi.org/10.3390/electronics8090935
Wang, X., Taghia, J., Katupitiya, J., 2016. Robust Model Predictive Control for Path Tracking of a Tracked Vehicle with a Steerable Trailer in the Presence of Slip. IFAC-PapersOnLine 49, 469–474. https://doi.org/10.1016/j.ifacol.2016.10.085 DOI: https://doi.org/10.1016/j.ifacol.2016.10.085
Yao, M., Deng, H., Feng, X., Li, P., Li, Y., Liu, H., 2024. Improved dynamic windows approach based on energy consumption management and fuzzy logic control for local path planning of mobile robots. Computers & Industrial Engineering 187, 109767. https://doi.org/10.1016/j.cie.2023.109767 DOI: https://doi.org/10.1016/j.cie.2023.109767
Yépez-Ponce, D.F., Salcedo, J.V., Rosero-Montalvo, P.D., Sanchis, J., 2023. Mobile robotics in smart farming: current trends and applications. Front. Artif. Intell. 6, 1213330. https://doi.org/10.3389/frai.2023.1213330 DOI: https://doi.org/10.3389/frai.2023.1213330
Descargas
Publicado
Número
Sección
Licencia
Derechos de autor 2024 Daniel Teso Fz. de Betoño, Iñigo Aramendia, José Antonio Ramos-Hernanz, Idoia Manero, Daniel Caballero-Martin, José Manuel Lopez-Guede
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.