Abstract
Multi-Agent Systems (MAS) are an Artificial Intelligence (AI) branch where agents handle distributed nature tasks in a cooperative system. MAS is widely used in robotic systems in scenarios where multiple robots must cooperate. In this direction, the robot soccer domain has been used as a test bed to stimulate research in this area, as it reproduces some important features of these systems, such as coordination. Each soccer team member is an agent whose behavior must be coordinated with the other team members cooperating to win the game. Simulation tools are frequently used in this context to create rehearsed plays, called setplays, during team training. However, these tools generally have a limited set of behaviors, e.g., kicking, available to use in setplays, and new behaviors must be manually implemented. This implementation requires knowledge of specific source codes and a significant programming effort, in addition to leaving the behavior coupled and dependent on the tool. This work proposes the Robot Soccer Behavior Generator (RoboSocBG), a solution to develop new behaviors in the context of simulated soccer robots. It uses Model-Driven Development (MDD), an approach that enables the specification of behavior platform-independent models and code generation in specific tools. The solution was tested in our laboratory and validated in a case study. The results evidenced its feasibility to generate code in different platforms.
Article PDF
Similar content being viewed by others
Availability of data and materials
If requested, the authors will share the data collected and used in the case study.
Code Availability
References
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 4th edn. Pearson, Berkeley (2020)
Robocup: Objective. www.robocup.org/objective. Accessed 23 Oct 2022
Simões, M., Nobre, J., Sousa, G., Souza, C., Silva, R., Campos, J., Souza, J., Nogueira, T.: Generating a dataset for learning setplays from demonstration. In: SN Applied Sciences, vol. 3, article no. 608 (2021). https://doi.org/10.1007/s42452-021-04571-y
Antonio, G., Maria-Dolores, C.: Multi-agent deep reinforcement learning to manage connected autonomous vehicles at tomorrow’s intersections. IEEE Trans. Veh. Technol. 71(7), 7033–7043 (2022). https://doi.org/10.1109/TVT.2022.3169907
Chakour, I., El Mourabit, Y., Daoui, C., Baslam, M.: Multi-agent system based on machine learning for early diagnosis of diabetes. In: 2020 IEEE 6th International Conference on Optimization and Applications, pp. 1–6 (2020). https://doi.org/10.1109/ICOA49421.2020.9094511
Foehn, P., Brescianini, D., Kaufmann, E., Cieslewski, T., Gehrig, M., Muglikar, M., Scaramuzza, D.: AlphaPilot: autonomous Drone Racing. Springer (2021). https://doi.org/10.1007/s10514-021-10011-y
Stefanova-Stoyanova, V., Stankov, I.: Multi-agent systems (mas) in the area of iot and using a model with distributed shared memory system (dsm). In: 2020 XXIX International Scientific Conference Electronics (ET), pp. 1–4. (2020). https://doi.org/10.1109/ET50336.2020.9238153
Busy, M., Caniot, M.: Qibullet, a bullet-based simulator for the pepper and NAO robots. Comput. Res. Repos. (2019). https://doi.org/10.48550/ARXIV.1909.00779
Hao, C., Chengju, L., Qijun, C.: Self-localization in highly dynamic environments based on dual-channel unscented particle filter. Robotica 39, 1216–1229 (2021). https://doi.org/10.1017/S0263574720001046
Simōes, M.A.C., Mascarenhas, G., Fonseca, R., dos Santos, V.M.P., Mascarenhas, F., Nogueira, T.: Bahiart setplays collecting toolkit and bahiart gym. Softw. Impacts 14, 100401 (2022). https://doi.org/10.1016/j.simpa.2022.100401
Brambilla, M., Cabot, J., Wimmer, M.: Model-driven software engineering in practice. (2012)
Zhu, M., Wang, A.I.: Model-driven game development: a literature review. In: ACM Comput. Surv., vol. 52(6), article no. 123, pp. 1–32. Association for Computing Machinery, New York, USA (2019). https://doi.org/10.1145/3365000
Heineck, T., Gonçalves, E., Sousa, A., Oliveira, M., Castro, J.: Model-driven development in robotics domain: a systematic literature review. In: 2016 X Brazilian Symposium on Software Components, Architectures and Reuse (SBCARS), pp. 151–160 (2016). https://doi.org/10.1109/SBCARS.2016.12
Mota, L., et al.: Collaborative behavior in soccer: the setplay free software framework. Lect. Notes Comput. Sci. 8992, 709–716 (2015). https://doi.org/10.1007/978-3-319-18615-3_58
Cravo, J., Almeida, F., Abreu, P.H., Reis, L.P., Lau, N., Mota, L.: Strategy planner: graphical definition of soccer set-plays. Data Knowl. Eng. 94, 110–131 (2014). https://doi.org/10.1016/j.datak.2014.10.001
Reis, L.P., et al.: Playmaker: graphical definition of formations and setplays. In: 5th Iberian Conference on Information Systems and Technologies. pp. 1–6 (2010). https://ieeexplore.ieee.org/document/5556598
Tdps robocup 3d soccer simulation. Available at http://archive.robocup.info/Soccer/Simulation/3D/TDPs/RoboCup/. Accessed 24 Oct 2022
Marques, F.T.: Generic coordination methodologies applied to the robocup simulation leagues. Master Degree Dessertation in Informatics and Computing Engineering (2010). https://repositorio-aberto.up.pt/bitstream/10216/63353/1/000147444.pdf. Accessed 15 Nov 2022
Lee, D.T., Schachter, B.J.: Two algorithms for constructing a delaunay triangulation. Int. J. Comput. Inf. Sci. 9, 219–242 (1980). https://doi.org/10.1007/BF00977785
OMG: Uml profile for niem. Object Management Group (2014). https://www.omg.org/spec/NIEM-UML/1.0/PDF. Accessed 24 Oct 2022
Kitchenham, B.: Procedures for performing systematic reviews. Keele University (2004). https://libguides.library.arizona.edu/ld.php?content_id=49906992. Accessed 24 Oct 2022
Cai, L., Cen, M., Luo, Z., Li, H.: Modeling risk behaviors in virtual environment based on multi-agent. In: 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), vol. 4, pp. 445–448. (2010). https://doi.org/10.1109/ICCAE.2010.5451636
Magalhães Mascarenhas, A.P., Maciel, R.S.P., Andrade, A.: Towards a metamodel design methodology: experiences from a model transformation metamodel design. Int. Conf. Softw. Eng. Knowl. Eng. (2015). https://doi.org/10.18293/SEKE2015-54
Cravo, J.G.B.: Splanner - uma aplicação gráfica de definição flexível de jogadas estudadas no robocup. Master degree dessertation in Informatics and Computing Engineering (2011). https://repositorio-aberto.up.pt/bitstream/10216/62120/1/000149781.pdf Accessed 15 Nov 2022
Almeida, F., Mota, L., Lau, N., Reis, L.P.: Fc portugal 2d simulation: team description paper. (2013). http://archive.robocup.info/Soccer/Simulation/2D/TDPs/RoboCup/2013/FCPortugal_SS2D_RC2013_TDP.pdf, Accessed 15 Nov 2022
Sommerville, I.: Software engineering (2011)
Wohlin, C., Runeson, P., et al.: Experimentation in Software Engineering. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29044-2
Solingen, R., Basili, V., Caldiera, H.D. G.and Rombach: Goal Question Metric (GQM) Approach. John Wiley & Sons, Hoboken (2002). https://doi.org/10.1002/0471028959.SOF142
Sales, R., Magalhães Mascarenhas, A.P., Simões, M., Souza, J.: Tutorial for the case study. (2022) https://forms.gle/WU94edvD2rKFbeWC9. Accessed 24 Oct 2022
Sales, R., Magalhães Mascarenhas, A.P., Simões, M., Souza, J.: Robosocbg tool evaluation questionnaire. (2022). https://forms.gle/M3cNwN64woGYszSF8. Accessed 24 Oct 2022
Sales, R., Magalhães Mascarenhas, A.P., Simões, M., Souza, J.: Participant profile. (2022). https://forms.gle/GuuAHKVGHrgvpRVa7. Accessed 24 Oct 2022
Sales, R., Magalhães Mascarenhas, A.P., Simões, M., Souza, J.: Research on robot soccer setplay creation software. (2022). https://forms.gle/XZ4JLDK6ebcLoD5LA. Accessed 24 Oct 2022
Funding
Not applicable.
Author information
Authors and Affiliations
Contributions
All authors contributed to the RoboSocBG conception and design. Raoni Sales developed the Project. The first manuscript version was written by Raoni Sales and Ana Patricia Mascarenhas. Then, all authors commented and worked on this release until approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest/Competing interests
The Authors Declare no Conflicts of Interest.
Ethics approval
Not applicable.
Consent to participate
The authors declare that appropriate consent has been taken from the case study participants.
Consent for publication
The authors have agreed to publish this manuscript.
Supplementary information
Not applicable
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Sales, R., Fontes Magalhães Mascarenhas, A.P., Simões, M.A.C. et al. Towards Automatic Code Generation for Robotic Soccer Behavior Simulation. J Intell Robot Syst 110, 18 (2024). https://doi.org/10.1007/s10846-023-02036-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10846-023-02036-5