This paper presents a team-theoretic approach to cooperative multirobot systems. The individual actions of the robots are controlled by the Belief-Desire-Intention model to endow the robots with the know-how needed to execute these actions deliberately. The cooperative behaviors between the heterogeneous robots are governed by the Team-Log theory to endow all the robots in the team with the know-how-to-cooperate and determine the team members’ commitments to each other despite their different types, properties, and goals. The proposed approach is tested for validity with the real life problem of minefield mapping. Different minefield sweeping strategies are studied to control the mobility of the mobile sweepers within the minefield in order to maximize the area coverage and improve picture compilation capability of the multirobot system. 1. Introduction Developing a robust and cooperative team of robots capable of solving complex tasks is an interesting area of research that attracts many researchers nowadays. Achieving robust and productive cooperation between various system components is inspired by different domains such as biology, artificial life, psychology, and cognitive science in order to build artificially cooperative intelligent systems. Cooperation is defined in [1] as a purposive positive interference of agents to further the achievement of a common goal or goals compatible with their own. To achieve this effective cooperation in multirobot systems (MRS), the robots must have know-how for solving simple problems in an autonomous way and a know-how-to-cooperate by which agents can share common interests and interact with each other to solve complex problems cooperatively. In recent years, scientific community has seen a great number of research works dedicated to cooperative multirobot systems and their applications in different areas such as search and rescue [2, 3], distributed surveillance [4], communication relaying [5], agriculture [6], sorting [7], emergency services [8], and landmine detection [9]. Minefield reconnaissance and mapping is one of the most promising applications of cooperative multirobot systems. In the context of humanitarian demining, cooperative multirobot systems can be beneficial for deminers, civilians, and government. The design of an accurate sensor may reduce the amount of time needed to determine whether a landmine exists, but does not increase the safety of the deminer. Since the safety issues during the eradication process are of great concern, the use and integration of cheap and simple mobile sweepers in
References
[1]
H. Irandoust, A. Benaskeur, and A. Khamis, “Cooperation in distributed surveillance: concepts, theories, models and technology enablers,” Tech. Rep., Valcartier, 2010.
[2]
L. Parker, “Multiple mobile robot systems,” in Handbook of Roboitcs, pp. 921–941, Springer, 2008.
[3]
D. Floreano and C. Mattiussi, Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies, MIT Press, 2008.
[4]
A. Khamis, “Cooperative sensor and actor networks in distributed surveillance context,” in Proceedings of the 10th International Conference on Practical Applications of Agent and Multiagent Systems (PAAM '12), Spain, 2012.
[5]
U. Witkowski, M. El-Habbal, S. Herbrechtsmeier et al., “Ad-hoc network communication infrastructure for multi-robot systems in disaster scenarios,” in Proceedings of the EURON/IARP International Workshop on Robotics for Risky Interventions and Surveillance of the Environment, Benicassim, Spain, January 2008.
[6]
J. Conesa-Munoz, A. Ribeiro, and G. Pajares, “A multi-path planning approach based on a genetic algorithm for a robot fleet working in arable crops,” in Proceedings of the Portuguese Conference on Artificial Intelligence (EPIA '11), 2011.
[7]
S. Verret, “Current state of the art in multirobot system,” Tech. Rep. DRDC Suffield TM 2005-241, Technical Memorandum, 2005.
[8]
P. Doherty and F. Heintz, “A delegation-based cooperative robotic framework,” in Cooperative Information Agents, 2007.
[9]
R. Cassinis, G. Bianco, A. Cavagi, and W. Ransenigo, “Landmines detection methods using swarms of simple robots,” in Proceedings of the 6th International Conference on Intelligent Autonomous Systems, pp. 212–218, 2000.
[10]
A. Khamis, “Minesweepers: towards a landmine-free Egypt,” The Journal of ERW and Mine Action, no. 17.1, 2013.
[11]
R. Keeley, “Understanding landmines and mine action,” June 3, 2012, http://mit.edu/demining/assignments/understanding-landmines.pdf.
[12]
A. Khamis, “16374- cooperative multi-robot systems,” Master course, Carlos III University of Madrid, 2011-2012.
[13]
L. Giraldeau and T. Caraco, Social Foraging Theory, Monographs in Behavior and Ecology, Princeton University Press, 2000.
[14]
H. Najjaran and A. A. Goldenberg, “Landmine detection using an autonomous terrain-scanning robot,” Industrial Robot, vol. 32, no. 3, pp. 240–247, 2005.
[15]
J. Vidal, Fundamentals of Multiagent Systems with NetLogo Examples, 2010.
[16]
A. Benaskeur, A. Khamis, and H. Irandoust, “Augmentative cooperation in distributed surveillance systems for dense regions,” International Journal of Intelligent Defence Support Systems, vol. 4, no. 1, pp. 20–49, 2011.
[17]
A. Bond and L. Gasser, Readings in Distributed Artificial Intelligence, Morgan Kaufmann, 1988.
[18]
N. R. Jennings, K. Sycara, and M. Wooldridge, “A roadmap of agent research and development,” Autonomous Agents and Multi-Agent Systems, vol. 1, no. 1, pp. 7–38, 1998.
[19]
G. Weiss, Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, MIT Press, 1999.
[20]
M. Singh, A Theoretical Framework for Intetions, Know-How and Communication, Springer, 1994.
[21]
M. P. Pacaux-Lemoine and S. Debernard, “Common work space for human-machine cooperation in air traffic control,” Control Engineering Practice, vol. 10, no. 5, pp. 571–576, 2002.
[22]
M. Bratman, Intention, Plans, and Practical Reason, CSLI Publications, 1999.
[23]
M. Georgeff, P. B. Pell, M. Pollack, M. Tambe, and M. Wooldridge, “The belief-desire-intention model of agency,” in Proceedings of the International Workshop on Intelligent Agents, 1998.
[24]
M. Bratman, “What is intention?” in Intentions in Communication, p. 1532, MIT Press, Cambridge, Mass, USA, 1990.
[25]
S. Bruecjner and H. V. D. Parunak, “Extrapolation of the opponent’s Pas behaviours,” in Adversarial Reasoning: Computational Approaches to Reading the Opponents Mined, 2008.
[26]
N. Jennings, “Towards a cooperation knowlege level for collaborative problem solving,” in Proceedings of the 10th European Conference on Artificial Intelligence, pp. 224–228, Vienna, Austria, 1992.
[27]
F. Brazier, C. Jonker, and J. Treur, Formalization of a Cooperation Model Based on Joint Intentions, Lecture Notes in AI, Springer, 1997.
[28]
V. Hilaire, O. Simonin, A. Koukam, and J. Ferber, “A formal approach to design and reuse agent and multiagent models,” in Proceedings of the 5th International Workshop on Agent-Oriented Software Engineering V (AOSE '04), pp. 142–157, July 2004.
[29]
J. Lehman, J. Laird, and P. Rosenbloom, “A gentle introduction to soar: an architecture for human cognition,” in Invitation to Cognitive Science, MIT Press, 1996.
[30]
B. Dunin-Keplicz and R. Verbrugge, Teamwork in Multi-Agent Systems: A Formal Approach, Wiley, 2010.
[31]
R. Fagin, J. Halpern, Y. Moses, and M. Vardi, Reasoning about Knowledge, MIT Press, 1995.
[32]
J.-C. Meyer and W. van der Hoek, Epistemic Logic for AI and Theoretical Computer Science, Cambridge University Press, Cambridge, UK, 1995.
[33]
P. R. Cohen and H. J. Levesque, “Intention is choice with commitment,” Artificial Intelligence, vol. 42, no. 2-3, pp. 213–261, 1990.
[34]
B. Dunin-K？plicz and R. Verbrugge, “Collective intentions,” Fundamenta Informaticae, vol. 51, no. 3, pp. 271–295, 2002.
[35]
E. Graedel, “Why is modal logic so robustly decidable?” Bulletin of the European Association for Theoretical Computer Science, vol. 68, pp. 90–103, 1999.
[36]
B. Horling and V. Lesser, “A survey of multi-agent organizational paradigms,” Knowledge Engineering Review, vol. 19, no. 4, pp. 281–316, 2004.
[37]
D. Gage, “Command control for many-robot systems,” in Proceedings of 19th Annual AUVS Technical Symposium, pp. 22–24, 1992.
[38]
D. Gage, “Randomized search strategies with imperfect sensors,” in Mobile Robots VIII, vol. 2058 of Proceedings of SPIE, pp. 270–279, 1993.
[39]
Y. Q. Miao, A. Khamis, and M. Kamel, “Coordinated motion control of mobile sensors in surveillance systems,” in Proceedings of the 3rd International Conference on Signals, Circuits and Systems (SCS '09), November 2009.
[40]
M. Yun-Qian, A study of mobility models in mobile surveillance systems [M.S. thesis], University of Waterloo, 2010.
[41]
M. Cardei and J. Wu, Coverage in Wireless Sensor Networks, CRC Press, 2004.
[42]
S. Meguerdichian, F. Koushanfar, M. Potkonjak, and M. B. Srivastava, “Coverage problems in wireless ad-hoc sensor networks,” in Proceedings of the 20th Annual Joint Conference of the IEEE Computer and Communications Societies, pp. 1380–1387, April 2001.
[43]
B. Liu, P. Brass, O. Dousse, P. Nain, and D. Towsley, “Mobility improves coverage of sensor networks,” in Proceedings of the 6th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MOBIHOC '05), pp. 300–308, May 2005.
[44]
B. Liu and D. Towsley, “On the coverage and detectability of large-scale wireless sensor networks,” in Proceedings of the Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks Conference, 2003.
[45]
A. T. Nghiem, F. Bremond, M. Thonnat, and V. Valentin, “ETISEO, performance evaluation for video surveillance systems,” in Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS '07), pp. 476–481, September 2007.
[46]
R. Duda, P. Hart, and D. Stork, Pattern Classification, Wiley-Interscience, 2000.
[47]
W. D. O. of the Chief of Engineers, German minefields at alamein 1943, http://cgsc.cdmhost.com/cdm/singleitem/collection/p4013coll8/id/1336/rec/14.
