Optimización energética en robots agrícolas con sistemas predictivos y Ventana Dinámica

  1. Teso Fz. de Betoño, Daniel 1
  2. Aramendia, Iñigo 1
  3. Ramos-Hernanz, José Antonio 1
  4. Manero, Idoia 1
  5. Caballero-Martin, Daniel 1
  6. Lopez-Guede, José Manuel 1
  1. 1 Universidad del País Vasco/Euskal Herriko Unibertsitatea
    info

    Universidad del País Vasco/Euskal Herriko Unibertsitatea

    Lejona, España

    ROR https://ror.org/000xsnr85

Revista:
Jornadas de Automática
  1. Cruz Martín, Ana María (coord.)
  2. Arévalo Espejo, V. (coord.)
  3. Fernández Lozano, Juan Jesús (coord.)

ISSN: 3045-4093

Año de publicación: 2024

Número: 45

Tipo: Artículo

DOI: 10.17979/JA-CEA.2024.45.10887 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

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.

Referencias bibliográficas

  • 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