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

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

ISSN: 3045-4093

Year of publication: 2024

Issue: 45

Type: Article

DOI: 10.17979/JA-CEA.2024.45.10887 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

Abstract

In this research, we introduce a new Predictive Dynamic Window Approach (P-DWA), where the algorithm not only anticipates the optimal trajectory in terms of time but also evaluates the energy consumption of the mobile robot's movement.The P-DWA predicts nine potential destinations, evaluates their time performance, and selects the top three trajectories. By modelling the mobile robot's motors, it becomes possible to estimate the energy consumption and torque required for a 2D map, and from the predicted trajectories, the energy consumption of each one is determined in watt-hours (Wh), opting for those with the lowest consumption. The results show that by considering energy consumption, it is possible to achieve a 9% reduction in energy consumption compared to the conventional Dynamic Window Approach

Bibliographic References

  • 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
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