Multi-agent System Architectural Aspects for Continuous Replanning
Resumo
Robots’ coordination to achieve the system’s goal is one of the challenges that complex Multi-Robot Systems (MRS) encounter. One could use automated planning (AP) to better face this challenge by diminishing problems and continually correcting the execution when failures occur. Some works in the literature try to fix this problem, but there are still few, and there’s not much analysis between them. This work implements a Multi-Agent System (MAS) to simulate an MRS mission using a MAS architecture integrated with AP illustrated with space resource gathering robots. The results show the importance of the ability to plan recovery and research in complex space missions field.Referências
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Basmadji, F., Seweryn, K., and Sasiadek, J. (2020). Space robot motion planning in the presence of nonconserved linear & angular momenta. Multibody System Dynamics, 50.
Bischoff, E., Teufel, J., Inga, J., and Hohmann, S. (2021). Towards interactive coordination of heterogeneous robotic teams – introduction of a reoptimization framework. In Proc. of IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC), pages 1380–1386.
Carreno, Y., Ng, J. H. A., Petillot, Y., and Petrick, R. (2022). Planning, execution, and adaptation for multi-robot systems using probabilistic and temporal planning. In Proc. of 21st Int. Conf. on Autonomous Agents and MultiAgent Systems (AAMAS), page 217–225.
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.
da Silva, C. J. T. (2024). A multi-robot system architecture with multi-agent planning. Computer Science Department, University of Brasilia, Campus Darcy Ribeiro - Asa Norte, Brasília - DF, 70910-900, Brazil.
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.
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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.
Kiener, J. and von Stryk, O. (2010). Towards cooperation of heterogeneous, autonomous robots: A case study of humanoid and wheeled robots. Robotics and Autonomous Systems, 58(7):921–929. Advances in Autonomous Robots for Service and Entertainment.
Klavins, E. (2004). Communication Complexity of Multi-robot Systems, pages 275–291. Springer, Berlin, Heidelberg.
Komenda, A., Stolba, M., and Kovacs, D. L. (2016). The international competition of distributed and multiagent planners (CoDMAP). AI Magazine, 37(3):109–115.
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.
Magnaguagno, M. C., Meneguzzi, F., and De Silva, L. (2022). HyperTensioN: A three-stage compiler for planning. In Proc. of 30th Int. Conf. on Automated Planning and Scheduling (ICAPS), pages 1–4.
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.
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Moreira, L. H. and Ralha, C. G. (2021a). 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.
Moreira, L. H. and Ralha, C. G. (2021b). Plan recovery process in multi-agent dynamic environments. In Gusikhin, O., Nijmeijer, H., and Madani, K., editors, Proc. of 18th Int. Conf. on Informatics in Control, Automation and Robotics (ICINCO), pages 187–194.
Moreira, L. H. and Ralha, C. G. (2022a). An efficient lightweight coordination model to multi-agent planning. Knowledge and Information Systems, 64:415–439.
Moreira, L. H. and Ralha, C. G. (2022b). Method for evaluating plan recovery strategies in dynamic multi-agent environments. Journal of Experimental & Theoretical Artificial Intelligence, pages 1–25.
Object Management Group (OMG) (2015). Meta-Object Facility (MOF) Specification, version 2.5. OMG Document Number formal/2015-03-01.
Rizk, Y., Awad, M., and Tunstel, E. W. (2019). Cooperative heterogeneous multi-robot systems: A survey. ACM Comput. Surv., 52(2).
Rodrigues, G., Caldas, R., Araujo, G., de Moraes, V., Rodrigues, G., and Pelliccione, P. (2022). An architecture for mission coordination of heterogeneous robots. Journal of Systems and Software, 191(111363).
Rumbaugh, J., Jacobson, I., and Booch, G. (2004). Unified Modeling Language Reference Manual, The (2nd Edition). Pearson Higher Education.
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.
Schmitt, P. S., Wirnshofer, F., Wurm, K. M., Wichert, G. v., and Burgard, W. (2019). Modeling and planning manipulation in dynamic environments. In Proc. of Int. Conf. on Robotics and Automation (ICRA), pages 176–182.
Silva, L. d., Meneguzzi, F., and Logan, B. (2020). BDI Agent Architectures: A Survey. In Proc. of 29th Int. Joint Conf. on Artificial Intelligence, (IJCAI), pages 4914–4921.
Sun, Y., Wu, J., and Liu, T. (2023). Joint task allocation and path planning for space robot. IEEE Access, 11:42314–42323.
Verma, J. K. and Ranga, V. (2021). Multi-robot coordination analysis, taxonomy, challenges and future scope. Journal of Intelligent & Robotic Systems, 102(1).
Weiss, G. (2016). Multiagent Systems. The MIT Press, 2nd edition.
Wooldridge, M. (2009). An introduction to multiagent systems. John Wiley & Sons.
Basmadji, F., Seweryn, K., and Sasiadek, J. (2020). Space robot motion planning in the presence of nonconserved linear & angular momenta. Multibody System Dynamics, 50.
Bischoff, E., Teufel, J., Inga, J., and Hohmann, S. (2021). Towards interactive coordination of heterogeneous robotic teams – introduction of a reoptimization framework. In Proc. of IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC), pages 1380–1386.
Carreno, Y., Ng, J. H. A., Petillot, Y., and Petrick, R. (2022). Planning, execution, and adaptation for multi-robot systems using probabilistic and temporal planning. In Proc. of 21st Int. Conf. on Autonomous Agents and MultiAgent Systems (AAMAS), page 217–225.
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.
da Silva, C. J. T. (2024). A multi-robot system architecture with multi-agent planning. Computer Science Department, University of Brasilia, Campus Darcy Ribeiro - Asa Norte, Brasília - DF, 70910-900, Brazil.
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.
et al., M. (2021). RoboMAX: Robotic mission adaptation exemplars. In Proc. of Int. Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), pages 245–251.
Georgievski, I. and Aiello, M. (2015). Htn planning: Overview, comparison, and beyond. Artificial Intelligence, 222:124–156.
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.
Kiener, J. and von Stryk, O. (2010). Towards cooperation of heterogeneous, autonomous robots: A case study of humanoid and wheeled robots. Robotics and Autonomous Systems, 58(7):921–929. Advances in Autonomous Robots for Service and Entertainment.
Klavins, E. (2004). Communication Complexity of Multi-robot Systems, pages 275–291. Springer, Berlin, Heidelberg.
Komenda, A., Stolba, M., and Kovacs, D. L. (2016). The international competition of distributed and multiagent planners (CoDMAP). AI Magazine, 37(3):109–115.
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.
Magnaguagno, M. C., Meneguzzi, F., and De Silva, L. (2022). HyperTensioN: A three-stage compiler for planning. In Proc. of 30th Int. Conf. on Automated Planning and Scheduling (ICAPS), pages 1–4.
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.
Matsumoto, Y. 2022). Ruby. [link]. Accessed: 2024-06-03.
Moreira, L. H. and Ralha, C. G. (2021a). 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.
Moreira, L. H. and Ralha, C. G. (2021b). Plan recovery process in multi-agent dynamic environments. In Gusikhin, O., Nijmeijer, H., and Madani, K., editors, Proc. of 18th Int. Conf. on Informatics in Control, Automation and Robotics (ICINCO), pages 187–194.
Moreira, L. H. and Ralha, C. G. (2022a). An efficient lightweight coordination model to multi-agent planning. Knowledge and Information Systems, 64:415–439.
Moreira, L. H. and Ralha, C. G. (2022b). Method for evaluating plan recovery strategies in dynamic multi-agent environments. Journal of Experimental & Theoretical Artificial Intelligence, pages 1–25.
Object Management Group (OMG) (2015). Meta-Object Facility (MOF) Specification, version 2.5. OMG Document Number formal/2015-03-01.
Rizk, Y., Awad, M., and Tunstel, E. W. (2019). Cooperative heterogeneous multi-robot systems: A survey. ACM Comput. Surv., 52(2).
Rodrigues, G., Caldas, R., Araujo, G., de Moraes, V., Rodrigues, G., and Pelliccione, P. (2022). An architecture for mission coordination of heterogeneous robots. Journal of Systems and Software, 191(111363).
Rumbaugh, J., Jacobson, I., and Booch, G. (2004). Unified Modeling Language Reference Manual, The (2nd Edition). Pearson Higher Education.
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.
Schmitt, P. S., Wirnshofer, F., Wurm, K. M., Wichert, G. v., and Burgard, W. (2019). Modeling and planning manipulation in dynamic environments. In Proc. of Int. Conf. on Robotics and Automation (ICRA), pages 176–182.
Silva, L. d., Meneguzzi, F., and Logan, B. (2020). BDI Agent Architectures: A Survey. In Proc. of 29th Int. Joint Conf. on Artificial Intelligence, (IJCAI), pages 4914–4921.
Sun, Y., Wu, J., and Liu, T. (2023). Joint task allocation and path planning for space robot. IEEE Access, 11:42314–42323.
Verma, J. K. and Ranga, V. (2021). Multi-robot coordination analysis, taxonomy, challenges and future scope. Journal of Intelligent & Robotic Systems, 102(1).
Weiss, G. (2016). Multiagent Systems. The MIT Press, 2nd edition.
Wooldridge, M. (2009). An introduction to multiagent systems. John Wiley & Sons.
Publicado
14/08/2024
Como Citar
TAVARES, Carlos Joel; RALHA, Célia Ghedini.
Multi-agent System Architectural Aspects for Continuous Replanning. In: WORKSHOP-ESCOLA DE SISTEMAS DE AGENTES, SEUS AMBIENTES E APLICAÇÕES (WESAAC), 18. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 39-50.
ISSN 2326-5434.
DOI: https://doi.org/10.5753/wesaac.2024.33454.
