Analysis of Dynamic Task Allocation in Multi-Robot Systems

Dynamic task allocation is an essential requirement for multi-robot systems operating in unknown dynamic environments. It allows robots to change their behavior in response to environmental changes or actions of other robots in order to improve overall system performance. Emergent coordination algorithms for task allocation that use only local sensing and no direct communication between robots are attractive because they are robust and scalable. However, a lack of formal analysis tools makes emergent coordination algorithms difficult to design. In this paper we present a mathematical model of a general dynamic task allocation mechanism. Robots using this mechanism have to choose between two types of task, and the goal is to achieve a desired task division in the absence of explicit communication and global knowledge. Robots estimate the state of the environment from repeated local observations and decide which task to choose based on these observations. We model the robots and observations as stochastic processes and study the dynamics of the collective behavior. Specifically, we analyze the effect that the number of observations and the choice of the decision function have on the performance of the system. The mathematical models are validated in a multi-robot multi-foraging scenario. The model's predictions agree very closely with experimental results from sensor-based simulations.Comment: Preprint version of the paper published in International Journal of Robotics, March 2006, Volume 25, pp. 225-24

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-

Information fusion in multi-agent system based on reliability criterion

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-00369-6_13The paper addresses the problem of information fusion in Multi-Agent System. Since the knowledge of the process state is distributed between agents, the efficiency of the task performance depends on a proper information fusion technique applied to the agents. In this paper we study the case in which each agent has its own sensing device and is able to collect information with some certainty. Since the same information can be detected by multiple agents, the global certainty about the given fact derives from the fusion of information exchanged by interconnecting agents. The key issue in the method proposed, is an assumption that each agent is able to asses its own reliability during the task performance. The method is illustrated by the pick-up-and-collection task example. The effectiveness of the method proposed is evaluated using relevant simulation experiments.Mellado Arteche, M.; Skrzypczyk, K. (2013). Information fusion in multi-agent system based on reliability criterion. En Vision Based Systemsfor UAV Applications. Springer. 207-217. doi:10.1007/978-3-319-00369-6_13S207217Cheng, X., Shen, J., Liu, H., Gu, G.: Multi-robot Cooperation Based on Hierarchical Reinforcement Learning. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007, Part III. LNCS, vol. 4489, pp. 90–97. Springer, Heidelberg (2007)Harmati, I., Skrzypczyk, K.: Robot team coordination for target tracking using fuzzy logiccontroller in game theoretic framework. Robotics and Autonomous Systems 57(1) (2009)Jones, C., Mataric, M.: Adaptive Division of Labor in Large-Scale Minimalist Multi-Robot Systems. In: Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, pp. 1969–1974 (2003)Kaminka, G.A., Erusalimchik, D., Kraus, S.: Adaptive Multi-Robot Coordination: A Game-Theoretic Perspective. In: Proc. of IEEE International Conference on Robotics and Automation, Anchorage Convention District, Anchorage, Alaska, USA (2002)Kok, J.R., Spaan, M.T.J., Vlassis, N.: Non-communicative multi-robot coordination in dynamic environments. Robotics and Autonomous Systems 50(2-3), 99–114 (2005)Klusch, M., Gerber, A.: Dynamic coalition formation among rational agents. IEEE Intelligent Systems 17(3), 42–47 (2002)Kraus, S., Winkfeld, J., Zlotkin, G.: Multiagent negotiation under time constraints. Artificial Intelligence 75, 297–345 (1995)Kraus, S.: Negotiation and cooperation in multiagent environments. Artificial Intelligence 94(1-2), 79–98 (1997)Mataric, M., Sukhatme, G., Ostergaard, E.: Multi-Robot Task Allocation in Uncertain Environments. Autonomous Robots 14, 255–263 (2003)Schneider-Fontan, M., Mataric, M.J.: Territorial Multi-Robot Task Division. IEEE Transactionson Robotics and Automation 14(5), 815–822 (1998)Winkfeld, K.J., Zlotkin, G.: Multiagent negotiation under time constraints. Artificial Intelligence (75), 297–345 (1995)Wooldridge, M.: An Introduction to Multiagent Systems. Johnn Wiley and Sons Ltd., UK (2009) ISBN:978-0-470-51946-2Vail, D., Veloso, M.: Dynamic Multi-Robot Coordination. In: Schultz, A., et al. (eds.) Multi Robot Systems: From Swarms to Intelligent Automata, vol. II, pp. 87–98. Kluwer Academic Publishers, The Netherlands (2003)Gałuszka, A., Pacholczyk, M., Bereska, D., Skrzypczyk, K.: Planning as Artifficial Intelligence Problem-short introduction and overview. In: Nawrat, A., Simek, K., Świerniak, A. (eds.) Advanced Technologies for Intelligent Systems of National Border Security. SCI, vol. 440, pp. 95–104. Springer, Heidelberg (2013)Jędrasiak, K., Bereska, D., Nawrat, A.: The Prototype of Gyro-Stabilized UAV Gimbal for Day-Night Surveillance. In: Nawrat, A., Simek, K., Świerniak, A. (eds.) Advanced Technologies for Intelligent Systems of National Border Security. SCI, vol. 440, pp. 107–116. Springer, Heidelberg (2013)Galuszka, A., Bereska, D., Simek, K., Skrzypczyk, K., Daniec, K.: Application of graphs theory methods to criminal analysis system. Przeglad Elektrotechniczny 86(9), 278–283 (2010

A survey on multi-robot coverage path planning for model reconstruction and mapping

There has been an increasing interest in researching, developing and deploying multi-robot systems. This has been driven mainly by: the maturity of the practical deployment of a single-robot system and its ability to solve some of the most challenging tasks. Coverage path planning (CPP) is one of the active research topics that could benefit greatly from multi-robot systems. In this paper, we surveyed the research topics related to multi-robot CPP for the purpose of mapping and model reconstructions. We classified the topics into: viewpoints generation approaches; coverage planning strategies; coordination and decision-making processes; communication mechanism and mapping approaches. This paper provides a detailed analysis and comparison of the recent research work in this area, and concludes with a critical analysis of the field, and future research perspectives

Quality and productivity driven trajectory optimisation for robotic handling of compliant sheet metal parts in multi-press stamping lines

This paper investigates trajectory generation for multi-robot systems that handle compliant parts in order to minimise deformations during handling, which is important to reduce the risk of affecting the part’s dimensional quality. An optimisation methodology is proposed to generate deformation-minimal multi-robot coordinated trajectories for predefined robot paths and cycle-time. The novelty of the proposed optimisation methodology is that it efficiently estimates part deformations using a precomputed Response Surface Model (RSM), which is based on data samples generated by Finite Element Analysis (FEA) of the handled part and end-effector. The end-effector holding forces, plastic part deformations, collision-avoidance and multi-robot coordination are also considered as constraints in the optimisation model. The optimised trajectories are experimentally validated and the results show that the proposed optimisation methodology is able to significantly reduce the deformations of the part during handling, i.e. up to 12% with the same cycle-time in the case study that involves handling compliant sheet metal parts. This investigation provides insights into generating specialised trajectories for material handling of compliant parts that can systematically minimise part deformations to ensure final dimensional quality

Fault-tolerant control policies for multi-robot systems

Throughout the past decade, we have witnessed an active interest in distributed motion coordination algorithms for networked mobile autonomous robots. Often, in multi-robot systems, each robot executing a coordination task is a little cost, a disposable autonomous agent that has ad-hoc sensing or communication capability, and limited mobility. Coordination tasks that a group of multiple mobile robots might perform include formation control, rendezvous, distributed estimation, deployment, flocking, etc. Also, there are challenging tasks that are more suitable for a group of mobile robots than an individual robot, such as surveillance, exploration, or hazardous environmental monitoring. The field has been collectively investigated by many researchers in robotics, control, artificial intelligence, and distributed computing. However, relatively little work has been done on developing algorithms to provide resilience to failures that can occur. The problem is extremely difficult to handle in that any partial failure of a robot is not readily detectable. Some failures in robot resources can have an adverse effect on not only the performance of the robot itself, but also other robots, and the collective task performance as well. This study presents the development of fault-tolerant distributed control policies for multi-robot systems. We consider two problems: rendezvous and coverage. For the former, the goal is to bring all robots to a common location, while for the latter the goal is to deploy robots to achieve optimal coverage of an environment. We consider the case in which each robot is an autonomous decision maker that is anonymous (i.e., robots are indistinguishable to one another), memoryless (i.e., each robot makes decisions based upon only its current information), and dimensionless (i.e., collision checking is not considered). Each robot has a limited sensing range and can directly estimate the state of only those robots within that sensing range, which induces a network topology for the multi-robot system. We assume that it is not possible for the fault-free robots to identify the faulty robots (e.g., due to the anonymous property of the robots). For each problem, we provide an efficient computational framework and analysis of algorithms, all of which converge in the face of faulty robots under a few assumptions on the network topology and sensing abilities. A suite of experiments and simulations confirm our theoretical analysis and demonstrate that our proposed algorithms are useful in fault-prone multi-robot systems

Artificial Intelligence and Systems Theory: Applied to Cooperative Robots

This paper describes an approach to the design of a population of cooperative robots based on concepts borrowed from Systems Theory and Artificial Intelligence. The research has been developed under the SocRob project, carried out by the Intelligent Systems Laboratory at the Institute for Systems and Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the project stands both for "Society of Robots" and "Soccer Robots", the case study where we are testing our population of robots. Designing soccer robots is a very challenging problem, where the robots must act not only to shoot a ball towards the goal, but also to detect and avoid static (walls, stopped robots) and dynamic (moving robots) obstacles. Furthermore, they must cooperate to defeat an opposing team. Our past and current research in soccer robotics includes cooperative sensor fusion for world modeling, object recognition and tracking, robot navigation, multi-robot distributed task planning and coordination, including cooperative reinforcement learning in cooperative and adversarial environments, and behavior-based architectures for real time task execution of cooperating robot teams

Distributed multi-robot exploration under complex constraints

Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería InformáticaClave Programa: DBICódigo Línea: 19Mobile robots have emerged as a prime alternative to explore physical processes of interest. This is particularly relevant in situations that have a high risk for humans, like e.g. in search and rescue missions, and for applications in which it is desirable to reduce the required time and manpower to gather information, like e.g. for environmental analysis. In such context, exploration tasks can clearly benefit from multi-robot coordination. In particular, distributed multi-robot coordination strategies offer enormous advantages in terms of both system¿s efficiency and robustness, compared to single-robot systems. However, most state-of-the-art strategies employ discretization of robots¿ state and action spaces. This makes them computationally intractable for robots with complex dynamics, and limits their generality. Moreover, most strategies cannot handle complex inter-robot constraints like e.g. communication constraints. The goal of this thesis is to develop a distributed multi-robot exploration algorithm that tackles the two aforementioned issues. To achieve this goal we first propose a single-robot myopic approach, in which we build to develop a non-myopic informative path planner. In a second step, we extend our non-myopic single-robot algorithm to the multi-robot case. Our proposed algorithms build on the following techniques: (i) Gaussian Processes (GPs) to model the spatial dependencies of a physical process of interest, (ii) sampling-based planners to calculate feasible paths; (iii) information metrics to guide robots towards informative locations; and (iv) distributed constraint optimization techniques for multi-robot coordination. We validated our proposed algorithms in simulations and experiments. Specifically, we carried out the following experiments: mapping of a magnetic field with a ground-based robot, mapping of a terrain profile with two quadcopters equipped with an ultrasound sensor, and exploration of a simulated wind field with three quadcopters. Results demonstrate the effectiveness of our approach to perform exploration tasks under complex constraints.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e InformáticaPostprin