Abstract
This work presents an algorithm that enables one or more humanoid robots to position themselves within a specific area by identifying the goal and determining the distance between the robot and the objective. The main objective is to facilitate the operation of the system in soccer-playing robots. To achieve this, two premises are established. Firstly, in the hypothetical scenario where two robots from the same team compete for the ball, the algorithm determines which robot is closer and makes the decision of who will go for the objective, thereby avoiding collisions among the robots. Subsequently, the robot will move towards the target area, i.e., the goal. Secondly, the hypothetical case is considered in which each robot belongs to a different team, and both will attempt to reach the ball before the other.
In both situations, the robot performs the identification of the ball and the other robot, whether friend or foe, in order to determine the distances between the robots and the ball by applying Deep Learning algorithms. For object identification, YOLOv3, a classic model for object identification that uses convolutional neural networks, was retrained. The computer vision is implemented by a RealSense camera which uses stereoscopic vision to calculate the depth of the objects. The robots utilize a self-localization and strategy algorithm, which is executed by each robot’s command system. There is no direct communication between the robots in either scenario. However, a wireless communication system between the robot and the computer is required, with Wi-Fi as the initial choice.
The algorithm’s performance will be evaluated using measures such as accuracy, precision, and sensitivity, employing tools like the confusion matrix and the Receiver Operating Characteristic (ROC) curve.
The authors thanks to Consejo Nacional de Ciencia y Tecnología de México for Master Scholarships CVU No. M. O. Leon-Pineda—1144833 and I. G. Valdespin-Garcia—1147565; and to Y. E. Gonzalez-Navarro—Secretaría de Investigación y Posgrado del Instituto Politécnico Nacional for the support provided for the preparation of this work through the projects SIP 20201539 and SIP 20231572.
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
References
Siciliano, B., Khatib, O.: Springer Handbook of Robotics. Springer (2016). https://play.google.com/store/books/details/Springer_Handbook_of_Robotics?id=HphMDAAAQBAJ, https://doi.org/10.1007/978-3-540-30301-5
Buckley, D.: History Makers - 1893 George Moore’s Steam Man. Publisher, 1896. Designed by the Canadian inventor George Moore in 1893, this life-size (6-foot tall) steam-powered android was able to walk at a rate of up to 5 mph. The Walking Steam Man was constructed of tin. The body contained a steam boiler powered by means of a gasoline engine
Darwin-op (dynamic anthropomorphic robot with intelligence-Open platform). https://casualrobots.com/robot_Darwin.html. Accessed 07 2023
YOLO V3. https://viso.ai/deep-learning/yolov3-overview/. Accessed 07 2023
Escobar Hernández, J.C.: La inteligencia artificial y la enseñanza de lenguas: una aproximación al tema. Decires 21(25), 29–44 (2021). https://doi.org/10.22201/cepe.14059134e.2021.21.25.3
RoboCup Humanoid League. https://humanoid.robocup.org, Último acceso: 13 enero 2021
Hernández Ramírez, J., et al.: Training bioloid robots for playing football. In: Lecture Notes in Computer Science, vol. 8112 (2013)
Sanchez Alvarado, E., Rivas Borbon, O.M.: Táctica y estrategia en futbol. Heredoa (2012)
Ibarra Bonilla, M.N.: Navegación autónoma de un robot con técnicas de localización y ruteo. PhD thesis, Tonantzintla, Puebla (2009)
Pratomo, A.H., Kwon, D.K., Kim, H.R.: Cooperative strategy for an interactive robot soccer system by reinforcement, pp. 236–242. Control, Automation and Systems (2003)
RoboCup - Official Website. https://www.robocup.org/. Accessed Sept 2023
TalentLand - Official Website. https://www.talent-land.mx/. Accessed Sept 2023
ROBOTIS: Robotis minimal mistakes (2020). https://emanual.robotis.com/docs/en/platform/op3/introduction/
Intel. Intel RealSense. Intel Corporation. https://www.intelrealsense.com/depth-camera-d415/
Intel. Intel neural compute stick 2, En línea. https://ark.intel.com/content/www/us/en/ark/products/140109/intel-neural-compute-stick-2.html
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016)
Szeliski, R.: Computer Vision: Algorithms and Applications. Springer (2010). https://doi.org/10.1007/978-1-84882-935-0
Raspberry Pi, En línea. https://www.raspberrypi.org/products/raspberry-pi-4-model-b/
NVIDIA. Nvidia desarrollador, En línea. https://developer.nvidia.com/embedded/jetson-nano-developer-kit
Radxa (2018). https://wiki.radxa.com/RockpiN10
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendices
Appendix
A Additional Tables
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Leon-Pineda, M.O., Valdespin-Garcia, I.G., Gonzalez-Navarro, Y.E. (2024). Self-location Algorithm for the Strategic Movement of Humanoid Robots. In: Calvo, H., Martínez-Villaseñor, L., Ponce, H. (eds) Advances in Soft Computing. MICAI 2023. Lecture Notes in Computer Science(), vol 14392. Springer, Cham. https://doi.org/10.1007/978-3-031-47640-2_22
Download citation
DOI: https://doi.org/10.1007/978-3-031-47640-2_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-47639-6
Online ISBN: 978-3-031-47640-2
eBook Packages: Computer ScienceComputer Science (R0)