This article describes various approaches that utilize computer vision and Lidar technology. These approaches include, but not limited to, vision-based algorithms such as the Faster RCNN model and AprilTag; and single shot detectors (SSD). In carrying out docking and recharging operations, the aforementioned approaches have shown varying degrees of success and accuracy. In order to make it easier for mobile robot systems to perform autonomous docking and recharging (ADaR) in industrial settings, this study presents a new method that employs vision and Lidar technology. In this study, we propose the YOLOv7 deep learning model to find charging stations. To further simplify docking with the specified wireless charging station, a Lidar-based approach is used to precisely modify the robot's position. An account of the assessment standards and training procedure used for the adjusted YOLOv7 model is provided in the results and discussion section. In this research, it was found that the model's 86.5% mean Average Precision (mAP) within the IoU range of 0.5 to 0.9 is evidence of its efficacy. In addition, the detection and identification of charging stations had an average accuracy rate of 95% in the studies conducted in real-world settings.
Keywords
Autonomous Docking and Recharging, Single Shot Detectors, Automated Driving Systems, Mobile Robots, Computer Vision, Mean Average Precision.
A. Bakdi, A. Hentout, H. Boutami, A. Maoudj, O. Hachour, and B. Bouzouia, “Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy-logic control,” Robotics and Autonomous Systems, vol. 89, pp. 95–109, Mar. 2017, doi: 10.1016/j.robot.2016.12.008.
L. Matthies and S. A. Shafer, “Error modeling in stereo navigation,” IEEE Journal on Robotics and Automation, vol. 3, no. 3, pp. 239–248, Jun. 1987, doi: 10.1109/jra.1987.1087097.
M. Müller, V. Casser, J. Lahoud, N. Smith, and B. Ghanem, “SIM4CV: a Photo-Realistic Simulator for Computer vision applications,” International Journal of Computer Vision, vol. 126, no. 9, pp. 902–919, Mar. 2018, doi: 10.1007/s11263-018-1073-7.
P. M. Vaz, R. Ferreira, V. Grossmann, and M. Ribeiro, “Docking of a mobile platform based on infrared sensors,” ISIE ’97 Proceeding of the IEEE International Symposium on Industrial Electronics, Nov. 2002, doi: 10.1109/isie.1997.649089.
Y. K. Kwak, “Development of docking system for mobile robots using cheap infrared sensors,” 1st International Conference on Sensing Technology, p. 0, Nov. 2005, [Online]. Available: https://koasas.kaist.ac.kr/handle/10203/6285
F. Fan and S. Ishibashi, “Underwater applications of light emitting diodes,” 2015 IEEE Underwater Technology (UT), Feb. 2015, doi: 10.1109/ut.2015.7108259.
M. L. Doumbia, C. Xu, and V. Havyarimana, “An Auto-Recharging System Design and Implementation Based on Infrared Signal for Autonomous Robots,” 2019 5th International Conference on Control, Automation and Robotics (ICCAR), Apr. 2019, doi: 10.1109/iccar.2019.8813317.
V. Dimitrov, M. Wills, and T. Padır, “Realization of vision-based navigation and object recognition algorithms for the sample return challenge,” 2015 IEEE Aerospace Conference, Mar. 2015, doi: 10.1109/aero.2015.7119271.
M. Dunbabin, B. Lang, and B. Wood, “Vision-based docking using an autonomous surface vehicle,” 2008 IEEE International Conference on Robotics and Automation, May 2008, doi: 10.1109/robot.2008.4543182.
Haldorai, B. L. R, S. Murugan, and M. Balakrishnan, “Harnessing Intelligent AI to Elevate Business Modeling: A Perspective,” EAI/Springer Innovations in Communication and Computing, pp. 429–440, 2024, doi: 10.1007/978-3-031-53972-5_22.
Haldorai, B. L. R, S. Murugan, and M. Balakrishnan, “Nature Inspired Optimizers and Their Importance for AI: An Inclusive Analysis,” EAI/Springer Innovations in Communication and Computing, pp. 407–427, 2024, doi: 10.1007/978-3-031-53972-5_21.
N. Melenbrink, J. Werfel, and A. Menges, “On-site autonomous construction robots: Towards unsupervised building,” Automation in Construction, vol. 119, p. 103312, Nov. 2020, doi: 10.1016/j.autcon.2020.103312.
P. M. Vaz, R. Ferreira, V. Grossmann, and M. Ribeiro, “Docking of a mobile platform based on infrared sensors,” ISIE ’97 Proceeding of the IEEE International Symposium on Industrial Electronics, Nov. 2002, doi: 10.1109/isie.1997.649089.
S. Roh et al., “Flexible docking mechanism using combination of magnetic force with error-compensation capability,” 2008 IEEE International Conference on Automation Science and Engineering, Aug. 2008, doi: 10.1109/coase.2008.4626554.
L. Olatomiwa, S. Mekhilef, M. I. Ismail, and M. Moghavvemi, “Energy management strategies in hybrid renewable energy systems: A review,” Renewable & Sustainable Energy Reviews, vol. 62, pp. 821–835, Sep. 2016, doi: 10.1016/j.rser.2016.05.040.
U. Kartoun et al., “Vision-Based Autonomous Robot Self-Docking and Recharging,” 2006 World Automation Congress, Jul. 2006, doi: 10.1109/wac.2006.375987.
S.-J. Wang et al., “Effects and mechanisms of Xiaochaihu Tang against liver fibrosis: An integration of network pharmacology, molecular docking and experimental validation,” Journal of Ethnopharmacology, vol. 303, p. 116053, Mar. 2023, doi: 10.1016/j.jep.2022.116053.
F. Guangrui and W. Geng, “Vision-based autonomous docking and re-charging system for mobile robot in warehouse environment,” 2017 2nd International Conference on Robotics and Automation Engineering (ICRAE), Dec. 2017, doi: 10.1109/icrae.2017.8291357.
S. Liu, M. Özay, T. Okatani, H. Xu, K. Sun, and L. Yang, “Detection and pose estimation for Short-Range Vision-Based underwater docking,” IEEE Access, vol. 7, pp. 2720–2749, Jan. 2019, doi: 10.1109/access.2018.2885537.
W. Liu et al., “SSD: Single Shot MultiBox Detector,” in Lecture Notes in Computer Science, 2016, pp. 21–37. doi: 10.1007/978-3-319-46448-0_2.
X. Long, W. Cui, and Z. Zheng, “Safety Helmet Wearing Detection Based On Deep Learning,” 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Mar. 2019, doi: 10.1109/itnec.2019.8729039.
C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” arXiv (Cornell University), Jul. 2022, doi: 10.48550/arxiv.2207.02696.
I. Gallo, A. U. Rehman, R. H. Dehkordi, N. Landro, R. La Grassa, and M. Boschetti, “Deep Object Detection of Crop Weeds: Performance of YOLOv7 on a Real Case Dataset from UAV Images,” Remote Sensing, vol. 15, no. 2, p. 539, Jan. 2023, doi: 10.3390/rs15020539.
M. S. Seyfioğlu, B. Erol, S. Z. Gürbüz, and M. G. Amin, “Diversified rADaR micro-Doppler simulations as training data for deep residual neural networks,” 2018 IEEE RADaR Conference (RADaRConf18), Apr. 2018, doi: 10.1109/rADaR.2018.8378629.
Acknowledgements
Authors thank Reviewers for taking the time and effort necessary to review the manuscript.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
No data available for above study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Amado Paul
Amado Paul
School of Data science, PSL Research University, Rue Mazarine, Paris, France.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Michel Alain and Amado Paul, “Robot Docking and Charging Techniques in Real Time Deep Learning Model”, Journal of Robotics Spectrum, vol.2, pp. 013-022, 2024. doi: 10.53759/9852/JRS202402002.
