BDI-based Multi-Robot System Architecture: A Disinfecting Robot Routine Illustrative Case
DOI:
https://doi.org/10.5753/reic.2026.7176Keywords:
Automated Planning, BDI Agents, Multi-Agent Systems, MAS Architecture, Multi-Robot SystemsAbstract
This work's primary contribution is the evaluation of a Multi-Robot Systems (MRS) solution that tightly integrates Automated Planning (AP) into a Belief-Desire-Intention (BDI)-based Multi-Agent System (MAS), enhancing performance in simulated scenarios. Coordinating multiple robots to achieve collective objectives across varied situations remains a major challenge in MRS. The literature proposes approaches where robots must cooperate, exchange information, and implement planning recovery mechanisms to ensure mission continuity. These challenges are directly linked to MAS combined with AP. In many MAS architectures, particularly those based on the BDI model, agents require accurate beliefs about the environment, defined objectives, and robust action plans, often assessed in restricted, controlled scenarios. However, to substantiate claims of generalizability, extensive testing across diverse scenarios is essential. This study applies a disinfecting robot routine in a hospital as an illustrative case with two heterogeneous robots. The results demonstrate that the architecture is effective and adaptable in the simulated hospital environment.
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Askarpour, M., Tsigkanos, C., Menghi, C., Calinescu, R., Pelliccione, P., García, S., Caldas, R., von Oertzen, T. J., Wimmer, M., Berardinelli, L., Rossi, M., Bersani, M. M., and Rodrigues, G. S. (2021). RoboMAX: Robotic mission adaptation exemplars. In Proc. of Int. Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), pages 245–251. DOI: 10.1109/SEAMS51251.2021.00040.
Austin, J. L. (1962). How to do things with words. Harvard University Press.
Aziz, H., Chan, H., Cseh, A., Li, B., Ramezani, F., and Wang, C. (2021). Multi-robot task allocation-complexity and approximation. In Proc. of 20th Int. Conf. on Autonomous Agents and MultiAgent Systems (AAMAS), page 133–141. DOI: 10.48550/arXiv.2103.12370.
Bansod, Y., Patra, S., Nau, D., and Roberts, M. (2022). Htn replanning from the middle. In The International FLAIRS Conference Proceedings, volume 35. DOI: 10.32473/flairs.v35i.130732.
Bordini, R. H., Hübner, J. F., and Wooldridge, M. (2007). Programming Multi-Agent Systems in AgentSpeak using Jason. John Wiley & Sons, Inc., Hoboken, NJ, USA.
Bratman, M. (1987). Intention, Plans, and Practical Reason. Cambridge, MA: Harvard University Press, Cambridge.
Cashmore, M., Fox, M., Long, D., Magazzeni, D., Ridder, B., Carreraa, A., Palomeras, N., Hurtós, N., and Carrerasa, M. (2015). ROSPlan: Planning in the robot operating system. In Proc. of 35th Int. Conf. on Automated Planning and Scheduling (ICAPS), page 333–341. DOI: 10.1609/icaps.v25i1.13699.
Corrêa, A. B., Pereira, A. G., and Ritt, M. (2018). Domain-dependent heuristics and tie-breakers: Topics in automated planning. Electronic Journal of Undergraduate Research on Computing (REIC), 16(3). DOI: 10.5753/reic.2018.1057.
da Silva, C. J. T. (2024). A Multi-robot System Architecture with Multi-agent Planning. Master’s thesis, Computer Science Department, University of Brasília, Brazil. Available at: [link].
da Silva, C. J. T. and Ralha, C. G. (2023). Multi-robot system architecture focusing on plan recovery for dynamic environments. In 2023 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1668–1673. DOI: 10.1109/SSCI52147.2023.10371972.
da Silva, C. J. T. and Ralha, C. G. (2024). Multi-agent system architectural aspects for continuous replanning. In Anais do XVIII Workshop-Escola de Sistemas de Agentes, seus Ambientes e Aplicações (WESAAC), pages 39–50, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/wesaac.2024.33454.
Erol, K., Hendler, J., and Nau, D. (1996). Complexity results for HTN planning. Annals of Mathematics and Artificial Intelligence, 18:69–93. DOI: 10.1007/BF02136175.
Erol, K., Hendler, J., and Nau, D. S. (1994). HTN planning: complexity and expressivity. In Proc. of the 12th AAAI National Conference on Artificial Intelligence, AAAI’94, page 1123–1128. AAAI Press.
González, J. C., García-Olaya, A., and Fernández, F. (2020). Multi-layered multi-robot control architecture for the robocup logistics league. In Proc. of IEEE Int. Conf. on Autonomous Robot Systems and Competitions, pages 120–125. DOI: 10.1109/ICARSC49921.2020.9096151.
Klavins, E. (2004). Communication Complexity of Multi-robot Systems, pages 275–291. Springer Berlin Heidelberg, Berlin, Heidelberg. DOI: 10.1007/978-3-540-45058-0_17.
Komenda, A., Stolba, M., and Kovacs, D. L. (2016). The international competition of distributed and multiagent planners (CoDMAP). AI Magazine, 37(3):109–115. DOI: 10.1609/aimag.v37i3.2658.
Lesire, C., Bailon-Ruiz, R., Barbier, M., and Grand, C. (2022). A hierarchical deliberative architecture framework based on goal decomposition. In Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pages 9865–9870. DOI: 10.1109/IROS47612.2022.9981488.
Martín, F., Clavero, J. G., Matellán, V., and Rodríguez, F. J. (2021). PlanSys2: A planning system framework for ROS2. In Proc. of IEEE Int. Conf. on Intelligent Robots and Systems (IROS), page 9742–9749. DOI: 10.1109/IROS51168.2021.9636544.
Moreira, L. H. and Ralha, C. G. (2021). Evaluation of decision-making strategies for robots in intralogistics problems using multi-agent planning. In Proc. of IEEE Congress on Evolutionary Computation, pages 1272–1279. DOI: 10.1109/CEC45853.2021.9504887.
Moreira, L. H. and Ralha, C. G. (2022). An efficient lightweight coordination model to multi-agent planning. Knowledge and Information Systems, 64:415–439. DOI: 10.1007/s10115-021-01638-5.
OMG (2014). Business process model and notation specification version 2.0.2 (BPMNTM ). [https://www.omg.org/spec/BPMN](https://www.omg.org/spec/BPMN). Accessed: 2025-08-03.
Rizk, Y., Awad, M., and Tunstel, E. W. (2019). Cooperative heterogeneous multi-robot systems: A survey. ACM Comput. Surv., 52(2). DOI: 10.1145/3303848.
Salzman, O. and Stern, R. (2020). Research challenges and opportunities in multi-agent path finding and multi-agent pickup and delivery problems. In Proc. of 19th Int. Conf. on Autonomous Agents and MultiAgent Systems (AAMAS), page 1711–1715. DOI: 10.5555/3398761.3398959.
Santos, R. M., Tavares, C. J., and Ralha, C. G. (2025). Multi-robot system architecture validation using disinfecting robot routine. In Anais do XIX Workshop-Escola de Sistemas de Agentes, seus Ambientes e Aplicações (WESAAC), pages 101–112, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/wesaac.2025.37529.
Torreño, A., Onaindia, E., Komenda, A., and Štolba, M. (2017). Cooperative multi-agent planning: A survey. ACM Comput. Surv., 50(6). DOI: 10.1145/3128584.
Verma, J. K. and Ranga, V. (2021). Multi-robot coordination analysis, taxonomy, challenges and future scope. Journal of Intelligent & Robotic Systems, 102(1). DOI: 10.1007/s10846-021-01378-2.
Weiss, G. (2016). Multiagent Systems. The MIT Press, 2nd edition.
Wooldridge, M. (2009). An Introduction to MultiAgent Systems. Wiley Publishing, 2nd edition.
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