Absrtact
The function realization of biped robot involves many problems, such as hardware development and software algorithm. The application of machine vision technology can better improve the stability and rapidity of the robot to complete the kicking soccer action. This paper focuses on the problem of how to effectively use machine vision to recognize small balls and coordinate the robot's legs to complete the soccer kicking action. Firstly, the target of the small ball is recognized based on the YOLOv4 algorithm, and the coordinate position of the small ball is given, and then converted into the motion control parameters of the robot's legs, so as to guide the biped robot to complete the football kicking. Finally, this paper tests and verifies the proposed scheme, proves the feasibility of this scheme, and provides some help for the follow-up research in the field of visual biped robot soccer.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Guenter, S.M.: The american robot: a cultural history. J. Am. History 108(2), 355–356 (2021)
Li, X., Yi, D.: The technological innovation of American industrial robot in the start-up period. Sci. Technol. Indus. 17(10), 138–143 (2017)
Chestnutt J., Lau M., Cheung G., et al.: Footstep planning for the Honda ASIMO humanoid. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005. ICRA 2005. IEEE (2005)
Sakagami, Y., Watanabe, R., Aoyama, C., et al.: The intelligent ASIMO: system overview and integration. In: IEEE/RSJ International Conference on Intelligent Robots & Systems. IEEE (2002)
Favot, V., Buschmann, T., Schwienbacher, M., et al.: The sensor-controller network of the humanoid robot LOLA. In: IEEE-RAS International Conference on Humanoid Robots. IEEE (2012)
Polishchuk, M., Opashnianskyi, M., Suyazov, N.: Walking mobile robot of arbitrary orientation. Int. J. Eng. Manuf. 8(3), 1–11 (2018). https://doi.org/10.5815/ijem.2018.03.01
Shi, L., Wang, Q., Wu, L.: Research on humanoid robot soccer system based on fuzzy logic. Int. J. Comput. Netw. Inf. Secur. 2(1), 38–44 (2010). https://doi.org/10.5815/ijcnis.2010.01.06
Marie, M.J., Mahdi, S.S., Tarkan, E.Y.: Intelligent control for a swarm of two-wheel mobile robot with presence of external disturbance. Int. J. Mod. Educ. Comput. Sci. (IJMECS) 11(11), 7–12 (2019)
Tiwari, A.K., Nadimpalli, S.V.: New fusion algorithm provides an alternative approach to robotic path planning. Int. J. Inf. Eng. Electron. Bus. 12(3), 1–7 (2020). https://doi.org/10.5815/ijieeb.2020.03.01
Sun, Y., Luo, A.: Development research on China’s industrial robot. Sci. Technol. Eng. 12, 2912–2918 (2012)
Radhika Mani, M., Varma, G.P.S., Potukuchi, D.M., Satyanarayana, Ch.: Design of a novel shape signature by farthest point angle for object recognition. Int. J. Image Graph. Signal Process. 7(1), 35–46 (2014). https://doi.org/10.5815/ijigsp.2015.01.05
Singh, S., Saurav, S., Shekhar, C., Vohra, A.: Moving object detection scheme for automated video surveillance systems. Int. J. Image Graph. Signal Process. 8(7), 49–58 (2016). https://doi.org/10.5815/ijigsp.2016.07.06
Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: unified, real-time object detection. In: Computer Vision & Pattern Recognition. IEEE (2016)
Su, S., Delbracio, M., Wang, J., et al.: Deep video deblurring for hand-held cameras. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2017)
Tiwari, A.K., Nadimpalli, S.V.: Learning semantic image attributes using image recognition and knowledge graph embeddings. Int. J. Image Graph. Signal Process. 12(5), 44–52 (2020). https://doi.org/10.5815/ijigsp.2020.05.05
Diwakar, D.R.: Recent object detection techniques: a survey. Int. J. Image Graph. Signal Process. (IJIGSP) 14(2), 47–60 (2022)
Zhang, G., Zeng, Z., Zhang, S., et al.: SIFT matching with CNN evidences for particular object retrieval. Neuro Comput. 238(238), 399–409 (2017)
He, K., Zhang, X., Ren, S., et al.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)
Cai, F., Chen, H., Ma, J.: Man-made object detection based on texture clustering and geometric structure feature extracting. Int. J. Inf. Technol. Comput. Sci. 3(2), 9–16 (2011). https://doi.org/10.5815/ijitcs.2011.02.02
Acknowledgment
This project is supported by Scientific Research Basic Ability Improvement Project for Young and Middle-Aged Teachers of Guangxi Universities(2022KY1781).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, X., Lv, J., Lu, H. (2023). Study and Implementation of Biped Robot Soccer Based on Machine Vision. In: Hu, Z., Wang, Y., He, M. (eds) Advances in Intelligent Systems, Computer Science and Digital Economics IV. CSDEIS 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 158. Springer, Cham. https://doi.org/10.1007/978-3-031-24475-9_48
Download citation
DOI: https://doi.org/10.1007/978-3-031-24475-9_48
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-24474-2
Online ISBN: 978-3-031-24475-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)