Multirobot systems (MRSs) are capable of solving task complexity, increasing performance in terms of maximizing spatial/temporal/radio coverage or minimizing mission completion time. They are also more reliable than single-robot systems as robustness is increased through redundancy. Many applications such as rescue, reconnaissance, and surveillance and communication relaying require the MRS to be able to self-organize the team members in a decentralized way. Group formation is one of the benchmark problems in MRS to study self-organization in these systems. This paper presents a hybrid approach to group formation problem in multi-robot systems. This approach combines the efficiency of the cellular automata as finite state machine, the interconnectivity of the virtual grid and its bonding technique, and last but not least the decentralization of the adaptive dynamic leadership. 1. Introduction Any group of robots in a multirobot system (MRS) moving and coordinating together will always require the ability to quickly change group formation to adapt to the environment. All the robots within this system cooperate with each other to achieve the common goal of having the best group formation with decentralized communication between the robots in that system. This means that each robot has to consider the environmental changes, positions of other robots, and the global goal. The multi-robot systems consist of either homogenous or heterogeneous robots. Homogenous robot system consists of a number of robots with the same properties, capabilities, configuration, and shape. On the other hand, heterogeneous robot system consists of robots that have different capabilities, properties, configuration, and shapes which makes task of implementing an algorithm to control their group formation without a centralized controller/coordinator difficult. Search and destroy, search and rescue, surround and conquer, and many military applications require multi-robot systems that are able to form a group and to adapt robustly. In order to solve the group formation problem in MRS, it is required to (i)model the relationship between robots in the same system, (ii)avoid clashes between robots, obstacles and goal,(iii)build all desired formations,(iv)coordinate the motion of each robot, (v)maintain formation while in motion,(vi)develop an approach that ensures adaptability of the formation.This paper presents a hybrid approach to group formation problem in multi-robot systems. This approach combines the efficiency of the cellular automata as finite state machine, the interconnectivity
References
[1]
R. C. Arkin, “Integrating behavioral, perceptual, and world knowledge in reactive navigation,” Robotics and Autonomous Systems, vol. 6, no. 1-2, pp. 105–122, 1990.
[2]
L. E. Parker, “Current research in multirobot systems,” Artif Life Robotics, no. 7, pp. 1–5, 2003.
[3]
Y. Meng, J. Nickerson, and J. Gan, “Multi-robot aggregation strategies with limited communication,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '06), pp. 2691–2696, October 2006.
[4]
I. Mas and C. Kitts, “Object manipulation using cooperative mobile multi-robot systems,” in Proceedings of the World Congress on Engineering and Computer Science (WCECS '12), San Francisco, Calif, USA, 2012.
[5]
L. E. Parker, Path Planning and Motion Coordination in Multiple Mobile Robot Teams, Encyclopedia of Complexity and System Science, Springer, 2009.
[6]
M. Luck, V. Marik, O. ？trpánková, and R. Trappl, Multi-Agent Systems and Applications, Springer, Berlin, Germany, 2001.
[7]
B. J. Nelson, “Microrobotics in medicine. white paper,” 2013, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.98.994&rep=rep1&type=pdf.
[8]
J. Leitner, “Multi-robot cooperation in space: a survey,” in Proceedings of the Advanced Technologies for Enhanced Quality of Life (AT-EQUAL '09), pp. 144–151, July 2009.
[9]
B. Nelson, I. Kaliakatsos, and J. Jake, “Microrobots for minimally invasive medicine,” Annual Review of Biomedical Engineering, vol. 12, pp. 55–85, 2010.
[10]
D. Davis and A. Pereira, Irregular Armed Forces and Their Role in Politics and State Formation, Cambridge University Press, 2008.
[11]
K. A. Hawick, C. J. Scogings, and H. A. James, “Defensive spiral emergence in a predator-prey model,” Tech. Rep., 2004.
[12]
B. Lei, W. Li, and F. Zhang, “Flocking algorithm for multi-robots formation control with a target steering agent,” in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC '08), pp. 3536–3541, October 2008.
[13]
Y. Q. Miao, A study of mobility models in mobile surveillance systems [M.S. thesis], Waterloo, Canada, 2010.
[14]
K. Fujibayashi, S. Murata, K. Sugawara, and M. Yamamura, “Self-organizing formation algorithm for active elements,” in Proceedings of the 21st IEEE Symposium on Reliable Distributed Systems (SRDS '02), pp. 416–421, October 2002.
[15]
R. Daneshvar and L. Shih, Unifying Themes in Complex Systems VII, Springer, Berlin, Germany, 2012.
[16]
Y. Cai and S. X. Yang, “A novel potential field-based pso approach to multi-robot cooperation for target searching in unknown environments,” Seminar, 2012.
[17]
J. Carroll and D. Long, Theory of Finite Automata: With an Introduction to Formal Languages, Prentice-Hall, 1989.
[18]
2013, http://www.coppeliarobotics.com/.
[19]
N. Shrivastava, R. Mudumbai, U. Madhow, and S. Suri, “Target tracking with binary proximity sensors: fundamental limits, minimal descriptions, and algorithms,” in Proceedings of the 4th International Conference on Embedded Networked Sensor Systems (SenSys '06), pp. 251–264, November 2006.
