Towards adaptive multi-robot systems: self-organization and self-adaptation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible

The human in the loop Perspectives and challenges for RoboCup 2050

Robotics researchers have been focusing on developing autonomous and human-like intelligent robots that are able to plan, navigate, manipulate objects, and interact with humans in both static and dynamic environments. These capabilities, however, are usually developed for direct interactions with people in controlled environments, and evaluated primarily in terms of human safety. Consequently, human-robot interaction (HRI) in scenarios with no intervention of technical personnel is under-explored. However, in the future, robots will be deployed in unstructured and unsupervised environments where they will be expected to work unsupervised on tasks which require direct interaction with humans and may not necessarily be collaborative. Developing such robots requires comparing the effectiveness and efficiency of similar design approaches and techniques. Yet, issues regarding the reproducibility of results, comparing different approaches between research groups, and creating challenging milestones to measure performance and development over time make this difficult. Here we discuss the international robotics competition called RoboCup as a benchmark for the progress and open challenges in AI and robotics development. The long term goal of RoboCup is developing a robot soccer team that can win against the world’s best human soccer team by 2050. We selected RoboCup because it requires robots to be able to play with and against humans in unstructured environments, such as uneven fields and natural lighting conditions, and it challenges the known accepted dynamics in HRI. Considering the current state of robotics technology, RoboCup’s goal opens up several open research questions to be addressed by roboticists. In this paper, we (a) summarise the current challenges in robotics by using RoboCup development as an evaluation metric, (b) discuss the state-of-the-art approaches to these challenges and how they currently apply to RoboCup, and (c) present a path for future development in the given areas to meet RoboCup’s goal of having robots play soccer against and with humans by 2050.</p

The human in the loop Perspectives and challenges for RoboCup 2050

© 2024 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Robotics researchers have been focusing on developing autonomous and human-like intelligent robots that are able to plan, navigate, manipulate objects, and interact with humans in both static and dynamic environments. These capabilities, however, are usually developed for direct interactions with people in controlled environments, and evaluated primarily in terms of human safety. Consequently, human-robot interaction (HRI) in scenarios with no intervention of technical personnel is under-explored. However, in the future, robots will be deployed in unstructured and unsupervised environments where they will be expected to work unsupervised on tasks which require direct interaction with humans and may not necessarily be collaborative. Developing such robots requires comparing the effectiveness and efficiency of similar design approaches and techniques. Yet, issues regarding the reproducibility of results, comparing different approaches between research groups, and creating challenging milestones to measure performance and development over time make this difficult. Here we discuss the international robotics competition called RoboCup as a benchmark for the progress and open challenges in AI and robotics development. The long term goal of RoboCup is developing a robot soccer team that can win against the world’s best human soccer team by 2050. We selected RoboCup because it requires robots to be able to play with and against humans in unstructured environments, such as uneven fields and natural lighting conditions, and it challenges the known accepted dynamics in HRI. Considering the current state of robotics technology, RoboCup’s goal opens up several open research questions to be addressed by roboticists. In this paper, we (a) summarise the current challenges in robotics by using RoboCup development as an evaluation metric, (b) discuss the state-of-the-art approaches to these challenges and how they currently apply to RoboCup, and (c) present a path for future development in the given areas to meet RoboCup’s goal of having robots play soccer against and with humans by 2050.Peer reviewe

Architecting centralized coordination of soccer robots based on principle solution

This is an Accepted Manuscript of an article published by Taylor & Francis in Advanced Robotics on 2015, available online:http://www.tandfonline.com/10.1080/01691864.2015.1017534Coordination strategy is a relevant topic in multi-robot systems, and robot soccer offers a suitable domain to conduct research in multi-robot coordination. Team strategy collects and uses environmental information to derive optimal team reactions, through cooperation among individual soccer robots. This paper presents a diagrammatic approach to architecting the coordination strategy of robot soccer teams by means of a principle solution. The proposed model focuses on robot soccer leagues that possess a central decision-making system, involving the dynamic selection of the roles and behaviors of the robot soccer players. The work sets out from the conceptual design phase, facilitating cross-domain development efforts, where different layers must be interconnected and coordinated to perform multiple tasks. The principle solution allows for intuitive design and the modeling of team strategies in a highly complex robot soccer environment with changing game conditions. Furthermore, such an approach enables systematic realization of collaborative behaviors among the teammates.This work was partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under the CICYT project Mission Based Control (COBAMI): DPI2011-28507-C02-01/02. Jose G. Guarnizo was supported by a scholarship from the Administrative Department of Science, Technology and Innovation COLCIENCIAS, Colombia.Guarnizo Marín, JG.; Mellado Arteche, M.; Low, CY.; Blanes Noguera, F. (2015). Architecting centralized coordination of soccer robots based on principle solution. Advanced Robotics. 29(15):989-1004. https://doi.org/10.1080/01691864.2015.1017534S98910042915Farinelli, A., Iocchi, L., & Nardi, D. (2004). Multirobot Systems: A Classification Focused on Coordination. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 34(5), 2015-2028. doi:10.1109/tsmcb.2004.832155Tews, A., & Wyeth, G. (2000). MAPS: a system for multi-agent coordination. Advanced Robotics, 14(1), 37-50. doi:10.1163/156855300741429Stulp, F., Utz, H., Isik, M., & Mayer, G. (2010). Implicit Coordination with Shared Belief: A Heterogeneous Robot Soccer Team Case Study. Advanced Robotics, 24(7), 1017-1036. doi:10.1163/016918610x496964Guarnizo, J. G., Mellado, M., Low, C. Y., & Aziz, N. (2013). Strategy Model for Multi-Robot Coordination in Robotic Soccer. Applied Mechanics and Materials, 393, 592-597. doi:10.4028/www.scientific.net/amm.393.592Riley, P., & Veloso, M. (2002). Recognizing Probabilistic Opponent Movement Models. Lecture Notes in Computer Science, 453-458. doi:10.1007/3-540-45603-1_59Ros, R., Arcos, J. L., Lopez de Mantaras, R., & Veloso, M. (2009). A case-based approach for coordinated action selection in robot soccer. Artificial Intelligence, 173(9-10), 1014-1039. doi:10.1016/j.artint.2009.02.004Atkinson, J., & Rojas, D. (2009). On-the-fly generation of multi-robot team formation strategies based on game conditions. Expert Systems with Applications, 36(3), 6082-6090. doi:10.1016/j.eswa.2008.07.039Costelha, H., & Lima, P. (2012). Robot task plan representation by Petri nets: modelling, identification, analysis and execution. Autonomous Robots, 33(4), 337-360. doi:10.1007/s10514-012-9288-xAbreu, P. H., Silva, D. C., Almeida, F., & Mendes-Moreira, J. (2014). Improving a simulated soccer team’s performance through a Memory-Based Collaborative Filtering approach. Applied Soft Computing, 23, 180-193. doi:10.1016/j.asoc.2014.06.021Duan, Y., Liu, Q., & Xu, X. (2007). Application of reinforcement learning in robot soccer. Engineering Applications of Artificial Intelligence, 20(7), 936-950. doi:10.1016/j.engappai.2007.01.003Hwang, K.-S., Jiang, W.-C., Yu, H.-H., & Li, S.-Y. (2011). Cooperative Reinforcement Learning Based on Zero-Sum Games. Mobile Robots - Control Architectures, Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training. doi:10.5772/26620Gausemeier, J., Dumitrescu, R., Kahl, S., & Nordsiek, D. (2011). Integrative development of product and production system for mechatronic products. Robotics and Computer-Integrated Manufacturing, 27(4), 772-778. doi:10.1016/j.rcim.2011.02.005Klančar, G., Zupančič, B., & Karba, R. (2007). Modelling and simulation of a group of mobile robots. Simulation Modelling Practice and Theory, 15(6), 647-658. doi:10.1016/j.simpat.2007.02.002Gausemeier, J., Frank, U., Donoth, J., & Kahl, S. (2009). Specification technique for the description of self-optimizing mechatronic systems. Research in Engineering Design, 20(4), 201-223. doi:10.1007/s00163-008-0058-