Robot swarming applications

This paper discusses the different modes of operation of a swarm of robots: (i) non-communicative swarming, (ii) communicative swarming, (iii) networking, (iv) olfactory-based navigation and (v) assistive swarming. I briefly present the state of the art in swarming and outline the major techniques applied for each mode of operation and discuss the related problems and expected results

A robot swarm assisting a human fire-fighter

Emergencies in industrial warehouses are a major concern for fire-fighters. The large dimensions, together with the development of dense smoke that drastically reduces visibility, represent major challenges. The GUARDIANS robot swarm is designed to assist fire-fighters in searching a large warehouse. In this paper we discuss the technology developed for a swarm of robots assisting fire-fighters. We explain the swarming algorithms that provide the functionality by which the robots react to and follow humans while no communication is required. Next we discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also the means to locate the robots and humans. Thus, the robot swarm is able to provide guidance information to the humans. Together with the fire-fighters we explored how the robot swarm should feed information back to the human fire-fighter. We have designed and experimented with interfaces for presenting swarm-based information to human beings

GUARDIANS final report

Emergencies in industrial warehouses are a major concern for firefghters. The large dimensions together with the development of dense smoke that drastically reduces visibility, represent major challenges. The Guardians robot swarm is designed to assist fire fighters in searching a large warehouse. In this report we discuss the technology developed for a swarm of robots searching and assisting fire fighters. We explain the swarming algorithms which provide the functionality by which the robots react to and follow humans while no communication is required. Next we discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also one of the means to locate the robots and humans. Thus the robot swarm is able to locate itself and provide guidance information to the humans. Together with the re ghters we explored how the robot swarm should feed information back to the human fire fighter. We have designed and experimented with interfaces for presenting swarm based information to human beings

Multi-robot task planning problem with uncertainty in game theoretic framework

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-31665-4_6An efficiency of an multi-robot systems depends on proper coordinating tasks of all robots. This paper presents a game theoretic approach to modelling and solving the pick-up and collection problem. The classical form of this problem is modified in order to introduce the aspect of an uncertainty related to an information about the workspace inside of which robots are intended to perform the task. The process of modelling the problem in game theoretic framework, as well as cooperative solution to the problem is discussed in these paper. Results of exemplary simulations are presented to prove the suitability of the approach presented.Skrzypczyk, K.; Mellado Arteche, M. (2013). Multi-robot task planning problem with uncertainty in game theoretic framework. En Advanced Technologies for Intelligent Systems of National Border Security. Springer. 69-80. doi:10.1007/978-3-642-31665-4_6S6980Alami, R., et al.: Toward human-aware robot task planning. In: Proc. of AAAI Spring Symposium, Stanford (USA), pp. 39–46 (2006)Baioletti, M., Marcugini, S., Milani, A.: Task Planning and Partial Order Planning: A Domain Transformation Approach. In: Steel, S. (ed.) ECP 1997. LNCS, vol. 1348. Springer, Heidelberg (1997)Desouky, S.F., Schwartz, H.M.: Self-learning Fuzzy logic controllers for pursuit-evasion differential games. Robotics and Autonomous Systems (2010), doi:10.1016/j.robot.2010.09.006Harmati, I., Skrzypczyk, K.: Robot team coordination for target tracking using fuzzy logic controller in game theoretic framework. Robotics and Autonomous Systems 57(1) (2009)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, 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)Meng, Y.: Multi-Robot Searching using Game-Theory Based Approach. International Journal of Advanced Robotic Systems 5(4) (2008)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, USA, pp. 1969–1974 (2003)Sariel, S., Balch, T., Erdogan, N.: Incremental Multi-Robot Task Selection for Resource Constrained and Interrelated Tasks. In: Proc. of 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, CA, USA (2007)Schneider-Fontan, M., Mataric, M.J.: Territorial Multi-Robot Task Division. IEEE Transactions on Robotics and Automation 14(5), 815–822 (1998)Song, M., Gu, G., Zhang, R., Wang, X.: A method of multi-robot formation with the least total cost. International Journal of Information and System Science 1(3-4), 364–371 (2005)Cheng, X., Shen, J., Liu, H., Gu, G.-c.: Multi-robot Cooperation Based on Hierarchical Reinforcement Learning. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007. LNCS, vol. 4489, pp. 90–97. Springer, Heidelberg (2007

GUARDIANS final report part 1 (draft): a robot swarm assisting a human fire fighter

Emergencies in industrial warehouses are a major concern for fire fighters. The large dimensions together with the development of dense smoke that drastically reduces visibility, represent major challenges. The Guardians robot swarm is designed to assist re ghters in searching a large warehouse. In this paper we discuss the technology developed for a swarm of robots assisting re ghters. We explain the swarming algorithms which provide the functionality by which the robots react to and follow humans while no communication is required. Next we discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also the means to locate the robots and humans. Thus the robot swarm is able to provide guidance information to the humans. Together with the fire fighters we explored how the robot swarm should feed information back to the human fire fighter. We have designed and experimented with interfaces for presenting swarm based information to human beings

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

Multi-robot team formation control in the GUARDIANS project

Purpose The GUARDIANS multi-robot team is to be deployed in a large warehouse in smoke. The team is to assist firefighters search the warehouse in the event or danger of a fire. The large dimensions of the environment together with development of smoke which drastically reduces visibility, represent major challenges for search and rescue operations. The GUARDIANS robots guide and accompany the firefighters on site whilst indicating possible obstacles and the locations of danger and maintaining communications links. Design/methodology/approach In order to fulfill the aforementioned tasks the robots need to exhibit certain behaviours. Among the basic behaviours are capabilities to stay together as a group, that is, generate a formation and navigate while keeping this formation. The control model used to generate these behaviours is based on the so-called social potential field framework, which we adapt to the specific tasks required for the GUARDIANS scenario. All tasks can be achieved without central control, and some of the behaviours can be performed without explicit communication between the robots. Findings The GUARDIANS environment requires flexible formations of the robot team: the formation has to adapt itself to the circumstances. Thus the application has forced us to redefine the concept of a formation. Using the graph-theoretic terminology, we can say that a formation may be stretched out as a path or be compact as a star or wheel. We have implemented the developed behaviours in simulation environments as well as on real ERA-MOBI robots commonly referred to as Erratics. We discuss advantages and shortcomings of our model, based on the simulations as well as on the implementation with a team of Erratics.</p