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Swarm robotics reviewed

Published online by Cambridge University Press:  03 July 2012

Jan Carlo Barca  and
Y. Ahmet Sekercioglu
Jan Carlo Barca*
Affiliation:
Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Victoria 3800, Australia
Y. Ahmet Sekercioglu
Affiliation:
Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Victoria 3800, Australia
*
*Corresponding author. E-mail: Jan_carlobarca@hotmail.com
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Summary

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We present a review of recent activities in swarm robotic research, and analyse existing literature in the field to determine how to get closer to a practical swarm robotic system for real world applications. We begin with a discussion of the importance of swarm robotics by illustrating the wide applicability of robot swarms in various tasks. Then a brief overview of various robotic devices that can be incorporated into swarm robotic systems is presented. We identify and describe the challenges that should be resolved when designing swarm robotic systems for real world applications. Finally, we provide a summary of a series of issues that should be addressed to overcome these challenges, and propose directions for future swarm robotic research based on our extensive analysis of the reviewed literature.

Keywords

State-of-the-artSwarmRoboticsReview
Type
Articles
Information
Robotica , Volume 31 , Issue 3 , May 2013 , pp. 345 - 359
Copyright
Copyright © Cambridge University Press 2012

References

1.Sahin, E., Swarm Robotics: From Sources of Inspiration to Domains of Application, Lecture Notes in Computer Science 3342 (Springer-Verlag, New York, 2005) pp. 10–20.CrossRefGoogle Scholar
2.Kennedy, J. and Eberhart, C. (eds.), Swarm Intelligence. The Morgan Kaufann Series in Evolutionary Computation (Fogel, D., ed.) (Morgan Kaufman, San Fransisco, 2001) 512 pp.Google Scholar
3.Bonabeau, E., Dorigo, M. and Theraulz, G., Swarm Intelligence (Oxford University Press, New York, 1999).CrossRefGoogle Scholar
4.Mohan, Y. and Ponnambalam, S., “An Extensive Review of Research in Swarm Robotics,” Proceedings of the World Congress on Nature & Biologically Inspired Computing, Coimbatore, India (2009).Google Scholar
5.Beni, G. and Wang, J., “Swarm Intelligence,” Proceedings of the Seventh Annual Meeting of Swarm Intelligence, Tokyo, Japan (1989).Google Scholar
6.Dorf, R., Concise International Encyclopedia of Robotics: Applications and Automation (Wiley-Interscience, New York, 1990).Google Scholar
7.Holland, O. and Walter, G., “The Pioneer of Real Artificial Life,” Proceedings of the International Workshop on Artificial Life (MIT Press, Cambridge, Massachusetts, 1997).Google Scholar
8.Ducatelle, F.et al., “Mobile Stigmergic Markers for Navigation in a Heterogeneous Robotic Swarm,” In: Proceedings of the 7th International Conference on Swarm Intelligence, Brussels, Belgium, LNCS 6234 (Springer, Berlin, Germany, 2010) pp. 456–463.CrossRefGoogle Scholar
9.Pinciroli, C.et al., “Self-Organised Recruitment in a Heteregeneous Swarm,” Proceedings of 14th International Conference on Advanced Robotics, Munich, Germany (2009).Google Scholar
10.Hauert, S., Zufferey, J. and Floreano, D., “Evolved swarming without positioning information: An application in aerial communication relay,” Auton. Robots 26 (1), 21–32 (2008).CrossRefGoogle Scholar
11.Mayet, R.et al., “Antbots: A Feasible Visual Emulation of Pheromone Trails for Swarm Robots,” In: Proceedings of the 7th International Conference on Swarm Intelligence, Brussels, Belgium, LNCS 6234 (Springer, Berlin, Germany, 2010) pp. 84–94.CrossRefGoogle Scholar
12.Pini, G.et al., “Task Partitioning in Swarms of Robots: Reducing Performance Losses due to Interference at Shared Resources in Informatics in Control Automation and Robotics,” In: Proceedings of the Informatics in Control Automation and Robotics, LNEE 85 (Springer-Verlag, Berlin, Germany, 2011) pp. 217–228.CrossRefGoogle Scholar
13.Fukuda, T. and Nakagawa, S., “A Dynamically Reconfigurable Robotic System (Concept of a System and Optimal Configurations),” In: Proceedings of the International Conference on Industrial Electronics, Control, and Instrumentation, Cambridge, Massachusetts (1987) pp. 588–595.Google Scholar
14.Trianni, V.et al., “From Solitary to Collective Behaviours: Decision Making and Cooperation,” In: Proceedings of the 9th European Conference on Artificial Life (Springer-Verlag, Berlin, Germany, 2007).Google Scholar
15.Yildiz, Ö., Gokal, R. and Yilmaz, A., “Underwater robot swarms and their applications,” Int. J. Technol. Sci. 1 (3), 37–50 (2011).Google Scholar
16.Joordens, M. and Jamshidi, M., “Consensus control for a system of underwater swarm robots,” IEEE Syst. J. 4 (1), 65–73 (2010).CrossRefGoogle Scholar
17.Mills, K., “A brief survey of self-organization in wireless sensor networks,” Wirel. Commun. Mob. Comput. 7, 823–834 (2007).CrossRefGoogle Scholar
18.Trianni, V. and Nolfi, S., “Self-Organising Sync in a Robotic Swarm,” In: Proceedings of the First International Workshop on Non-Linear Dynamics and Synchronization (Shaker Verlag, Aachen, Germany, 2008).Google Scholar
19.Reynolds, C., “Flocks, herds and schools: A distributed behavioral model,” Comput. Graph. 21 (4), 25–34 (1987).CrossRefGoogle Scholar
20.Martinez, S., Cortes, J. and Bullo, F., “Motion coordination with distributed information,” IEEE Control Syst. Mag. 27 (4), 75–88 (2007).Google Scholar
21.Anderson, B., Yu, C. and Hendrickx, J., “Rigid graph control architectures for autonomous formations,” IEEE Control Syst. Mag. 28 (6), 48–63 (2008).Google Scholar
22.Lafferriere, G.et al., “Decentralized control of vehicle formations,” Syst. Control Lett. 54 (9), 899–910 (2005).CrossRefGoogle Scholar
23.Camazine, S.et al., Self-Organisation in Biological Systems (Princeton University Press, Princeton, NJ, 2001).Google Scholar
24.Bassler, B., “How bacteria talk to each other: Regulation of gene expression by quorum sensing,” Curr. Opin. Microbiol. 2 (6), 582–587 (1999).CrossRefGoogle ScholarPubMed
25.Ben-Jacob, E., “Bacterial self-organization: Co-enhancement of complexification and adaptability in a dynamic environment,” Phil. Trans. R. Soc. Lond. A 361, 1283–1312 (2003).CrossRefGoogle Scholar
26.Pugh, J. and Martinoli, A., “Distributed Adaptation in Multi-Robot Search using Particle Swarm Optimization,” Proceedings of the 10th International Conference on the Simulation of Adaptive Behaviour, Osaka, Japan (2008).Google Scholar
27.Mamei, M., Vasari, M. and Zambonelli, F., “Experiments of morphogenesis in swarms of simple mobile robots,” Appl. Artif. Intell. 18 (9–10), 903–919 (2004).CrossRefGoogle Scholar
28.Hinchey, G., Sterritt, R. and Rouff, C., “Swarms and swarm intelligence,” Computer 40 (4), 111–113 (2007).CrossRefGoogle Scholar
29.Adams, B.et al., “Humanoid robots: A new kind of tool,” IEEE Intell. Syst. Control lett. 15 (4), 25–31 (2000).Google Scholar
30.Hart, D., “Reducing Swarming Theory to Practice for UAV Control,” Proceedings of the IEEE Aerospace Conference, Big Sky, Montana (2004).Google Scholar
31.Vincent, P. and Rubin, I., “A Framework and Analysis for Cooperative Search Using UAV Swarms,” In: Proceedings of the ACM Symposium on Applied Computing, Nicosia, Cyprus (2004) 79–86.Google Scholar
32.Edwards, S., Swarming on the Battlefield (Rand, Santa Monica, California, 2000) 88 pp.Google Scholar
33.Cortes, J.et al., “Coverage control for mobile sensing networks,” IEEE Trans. Robot. Autom. 20 (2), 243–255 (2004).CrossRefGoogle Scholar
34.Lerman, K., Martinoli, A. and Galstyan, A., A Review of Probabilistic Macroscopic Models for Swarm Robotic Systems, LNCS 3342 (Springer-Verlag, New York, 2005) pp. 143–152.Google Scholar
35.Cao, Y., Fukunaga, S. and Kahng, A., “Cooperative mobile robotics: Antecedents and directions,” Auton. Robots 4 (1), 7–27 (1997).CrossRefGoogle Scholar
36.Barca, J. C., Rumantir, G. and Li, R., “A Concept for Optimizing Behavioural Effectiveness & Efficiency,” In: Intelligent Engineering Systems and Computational Cybernetics (Machado, T., Patkai, B. and Rudas, J., eds.) (Springer-Verlag, Berlin, Germany 2008) pp. 449–458.Google Scholar
37.Ma, M. and Yang, Y., “Adaptive triangular deployment algorithm for unattended mobile sensor networks,” IEEE Trans. Comput. 56 (7), 946–958 (2007).CrossRefGoogle Scholar
38.Parker, L., “Designing Control Laws for Cooperative Agent Teams,” Proceedings of the IEEE International Conference on Robotics and Automation, Hidden Valley, Pennsylvania (1993).Google Scholar
39.Lumelsky, V. and Harinarayan, K., “Decentralized Motion Planning for Multiple Mobile Robots: The Cocktail Party Model,” Journal of Autonomous Robots. 4 (1), 121–135 (1997).CrossRefGoogle Scholar
40.Gu, Y.et al., “Data harvesting with mobile elements in wireless sensor networks,” Comput. Netw. 50 (17), 3449–3465 (2006).CrossRefGoogle Scholar
41.Li, X.et al., Localized Self-Deployment of Mobile Sensors for Optimal Focused-Coverage Formation (Carleton University, Ottawa, Canada, 2008) pp. 1–18.Google Scholar
42.Cortés, J., Martínez, S. and Bullo, F., “Spatially-distributed coverage optimization and control with limited-range interactions,” ESAIM, Control Optim. Calc. Var. 11 (4), 691–719 (2005).CrossRefGoogle Scholar
43.Cortés, J. and Bullo, F., “Coordination and geometric optimization via distributed dynamical systems,” SIAM J. Control Optim. 44 (5), 1543–1574 (2005).CrossRefGoogle Scholar
44.Howard, A., Matarić, M. and Sukhatme, G., “Mobile Sensor Network Deployment Using Potential Fields: A Distributed, Scalable Solution to the Area Coverage Problem,” Proceedings of the 6th International Symposium on Distributed Autonomous Robotics Systems, Fukuoka, Japan (2002).Google Scholar
45.Penders, J.et al., “A Robot swarm assisting a human fire-fighter,” Adv. Robot. 25 (1–2), 93–117 (2011).CrossRefGoogle Scholar
46.Colorado, J., Barrientos, A., Rossi, C. and del Cerro, J., “Follow-the-Leader Formation Marching Through a Scalable O(log2n) Parallel Architecture,” Proceedings of the IEEE/RJS 2010 International Conference on Intelligent Robots and Systems, Taiwan (2010).Google Scholar
47.Heo, N. and Varshney, P., “Energy-Efficient deployment of intelligent mobile sensor networks,” IEEE Trans. Syst. Man Cybern. 35 (1), 78–92 (2005).CrossRefGoogle Scholar
48.Dudek, G.et al., “A Taxonomy for Swarm Robots,” Proceedings of the IEEE/RSJ IROS, Tokyo, Japan (1993).Google Scholar
49.Beni, G., From Swarm Intelligence to Swarm Robotics, Lecture Notes in Computer Science 3342 (Springer-Verlag, New York, 2005), pp. 1–9.Google Scholar
50.Fong, T., Nourbakhsh, I. and Dautenhahn, K., “A survey of socially interactive robots,” Robot. Auton. Syst. 42 (3–4), pp. 143–166 (2003).CrossRefGoogle Scholar
51.Parker, L., “Current state of the art in distributed autonomous mobile robotics,” Distrib. Auton. Robot. Syst. 4, 3–12 (2000).Google Scholar
52.Winfield, A., Harper, C. and Nembrini, J., “Towards the Application of Swarm Intelligence in Safety Critical Systems,” Proceedings of the 1st Institution of Engineering and Technology International Conference on System Safety, London (2006).Google Scholar
53.Großand, R.Dorigo, M., “Fifty Years of Self-Assembly Experimentation,” Proceedings of the Workshop on Self-Reconfigurable Robots/Systems and Applications (2007).Google Scholar
54.Dudek, G.et al., “A taxonomy for multi-agent robotics,” Auton. Robots 3, 375–397 (1996).CrossRefGoogle Scholar
55.Asama, H., “Distributed autonomous robotic system configurated with multiple agents and its cooperative behaviors,” J. Robot. Mechatronics 3 (4), 199–204 (1992).CrossRefGoogle Scholar
56.Karaboga, D. and Akay, B., “A survey: Algorithms simulating bee swarm intelligence,” Artifial Intell. Rev. 31, 61–85 (2009).CrossRefGoogle Scholar
57.Chen, Y. and Wang, Z., “Formation Control: A Review and a New Consideration,” Proceedings of the IEEE/RSJ International Conference on Intelligent Agents and Systems (2005).Google Scholar
58.Roberts, J.et al., “Eye-bot flyer,” available at: http://lis.epfl.ch/research/projects/Eyebot/info/eye-bot-flyer.pdf. (accessed Nov 20, 2009). [cited November 20, 2009].Google Scholar
59.Elston, J. and Frew, E., “Hierarchical Distributed Control for Search and Tracking by Heterogeneous Aerial Robot Networks,” In: Proceedings of the IEEE International Conference on Robotics and Automation (IEEE Press, Piscataway, NJ, 2008) pp. 170–175.Google Scholar
60.Christensen, A., O'Grady, R. and Dorigo, M., “Morphology control in a multirobot system,” IEEE Robot. Autom. Mag. 14 (4), 18–25 (2007).CrossRefGoogle Scholar
61.Macdonald, E., “Multi-Robot Assignment and Formation Control,” Master's Thesis (School of Electrical and Computer Engineering, Georgia Tech., Atlanta, 2011) 76 pp.Google Scholar
62.Hoff, N., Wood, R. and Nagpal, R., “Effect of Sensor and Actuator Quality on Robot Swarm Algorithm Performance,” In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, California, (2011) pp. 4989–4994.Google Scholar
63.Christensen, A., Grady, R. and Dorigo, M., “A Mechanism to Self-Assemble Patterns with Autonomous Robots,” In: Proceedings of the 9th European conference on Advances in Artificial Life (Springer, Berlin, Germany, 2007) pp. 716–725.CrossRefGoogle Scholar
64.Bowden, N.et al., “Self-Assembly of mesoscale objects into ordered two-dimensional arrays,” Science 276 (5310), 233–235 (1997).CrossRefGoogle ScholarPubMed
65.Fukada, T.et al., “Self-Organising Robots Based on Cell Structures,” IEEE Int. Workshop on Intelligent Robotics (IEEE Computer Society Press, Los Alamitos, 1988).Google Scholar
66.Gross, R.et al., “Autonomous self-assembly in swarm-bots,” IEEE Trans. Robot. 22 (6), 1115–1130 (2006).Google Scholar
67.Tuci, E.et al., “Cooperation through self-assembly in multi-robot systems,” ACM Trans. Auton. Adapt. Syst. 1 (2), 115–150 (2006).CrossRefGoogle Scholar
68.Murata, S.et al., “M-TRAN: Self-reconfigurable modular robotic system,” IEEE/ASME Trans. Mech. 7 (4), 431–441 (2002).CrossRefGoogle Scholar
69.Kotay, K. and Rus, D., “Motion Synthesis for the Self-Reconfiguring Molecule,” Proceedings of the IEEE/RSJ International Conference on Robots and Systems, Victoria, Canada (1998).Google Scholar
70.Yim, M., “A Reconfigurable Modular Robot with Many Modes of Locomotion,” In: Proceedings of the JSM International Conference on Advanced Mechatronics (1993) pp. 283–288.Google Scholar
71.Hamlin, G. and Sanderson, A., “Tetrobot, Modular Robotics: Prototype and Experiments,” Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Osaka, Japan (1996).Google Scholar
72.Garro, B., Sossa, H. and Vazquez, R., “Evolving Ant Colony System for Optimizing Path Planning in Mobile Robots,” Proceedings of the Fourth Congress of Electronics, Robotics and Automotive Mechanics, Morelos, Mexico (2007).Google Scholar
73.Clark, C., Rock, S. and Latombe, J., “Motion Planning for Multiple Mobile Robot Systems using Dynamic Networks,” Proceedings of the IEEE International Conference on Robotics and Automation (2003).Google Scholar
74.Fidan, B., Yu, C. and Anderson, B., “Acquiring and maintaining persistence of autonomous multi-vehicle formations,” IET Control Theory Appl. 1 (2), 452–460 (2007).CrossRefGoogle Scholar
75.Zhang, H., Kumar, V. and Ostrowski, J., “Motion Planning with Uncertainty,” Proceedings of the IEEE International Conference on Robotics and Automation, Leuven, Belgium (1998).Google Scholar
76.Wang, G., Cao, G. and La Porta, T., “Movement-assisted sensor deployment,” IEEE Trans. Mobile Comput. 5 (6), 640–652 (2006).CrossRefGoogle Scholar
77.Erent, T.et al., “Operations on rigid formations of autonomous agents,” Commun. Inf. Syst. 3 (4), 223–258 (2004).Google Scholar
78.Olfati-Saber, R. and Murray, R., “Distributed Cooperative Control of Multiple Vehicle Formations Using Structural Potential Functions,” In: Proceedings of the 15th IFAC World Congress, Barcelona, Spain (2002) pp. 1–6.Google Scholar
79.Saber, R. and Murray, R., “Graph rigidity and distributed formation stabilization of multi-vehicle systems,” Proceedings of the 41st IEEE Conference on Decision and Control, Las Vegas, Nevada (2002).Google Scholar
80.Mesbahi, M. and Egerstedt, M. (eds.), Graph Theoretic Methods in Multiagent Networks. Applied Mathematics (Princeton University Press, NJ, 2010) 401 pp.CrossRefGoogle Scholar
81.Ren, W., Beard, W. and Atkins, E., “Information consensus in multivehicle cooperative control,” IEEE Control Syst. Mag. 27 (2), 71–82 (2007).Google Scholar
82.Ren, W., “Consensus strategies for cooperative control of vehicle formations,” IET Control Theory Appl. 1 (2), 505–512 (2007).CrossRefGoogle Scholar
83.Arai, T. and Ota, J., “Motion planning of multiple robots,” Proceedings of the IEEE/RSJ IROS, Raleigh, North Carolina (1992) pp. 1761–1768.Google Scholar
84.Sugihara, K. and Suzuki, I., “Distributed Motion Coordination of Multiple Mobile Robots,” Proceedings of the IEEE International Symposium on Intelligent Control, Philadelphia (1990).Google Scholar
85.Desai, J., Kumar, V. and Ostrowski, J., “Control of Changes in Formation for a Team of Mobile Robots,” Proceedings of the IEEE International Conference on Robotics and Automation, Detroit, Michigan (1999).Google Scholar
86.Belta, C. and Kumar, V., “Trajectory Design for Formations of Robots by Kinetic Energy Shaping,” Proceedings of the ICRA ‘02. IEEE International Conference on Robotics and Automation, Washington, DC (2002).Google Scholar
87.Sandeep, S., Fidan, B. and Yu, C., “Decentralized Cohesive Motion Control of Multi-Agent Formations,” Proceedings of the 14th Mediterranean Conference on Control and Automation, Ancona, Italy (2006).Google Scholar
88.Cheng, L., Hou, Z. and Tan, M., “Decentralized Adaptive Leader-Follower Control of Multi-Manipulator System with Uncertain Dynamics,” Proceedings of the 34th Annual Conference of IEEE Industrial Electronics, Orlando, Florida (2008).Google Scholar
89.Baldassarre, G.et al., “Self-organised coordinated motion in groups of physically connected robots,” IEEE Trans. Syst. Man Cybern. 37 (1), 224–239 (2007).CrossRefGoogle Scholar
90.Ampatzis, C.et al., “Evolving self-assembly in autonomous homogeneous robots: Experiments with two physical robots,” Artif. Life 15 (4), 465–484 (2009).CrossRefGoogle ScholarPubMed
91.Trianni, V. and Nolfi, S., “Self-organising sync in a robotic swarm. A dynamical system view,” IEEE Trans. Evol. Comput. (special issue on Swarm Intell.) 13 (4), 722–741 (2009).Google Scholar
92.Kim, D. and Han, S., “Robust Self-Organization for Swarm Robots,” Proceedings of the International Conference on Control, Automation and Systems, Seoul (2008).Google Scholar
93.Sperati, V., Trianni, V. and Nolfi, S., “Evolution of Self-Organised Path Formation in A Swarm of Robots,” Proceedings of the 7th International Conference on Swarm ANTS'10, Mumbai, India (Dec. 2010).Google Scholar
94.Marinescu, D., Marinescu, G. and Yu, C., “Self-Organizing Sensor Networks,” In: Proceedings of the 3rd International Symposium on Wireless Pervasive Computing, Santorini (2008) pp. 288–292.Google Scholar
95.Castelli, G., Menezes, R. and Zambonelli, F., “Self-Organized Control of Knowledge Generation in Pervasive Computing Systems,” In: SAC'09. (ACM, Honolulu, Hawaii, 2009).Google Scholar
96.Jin, Y. and Meng, Y., “Morphogenetic robotics: An emerging new field in developmental robotics,” IEEE Trans. Syst. Man Cybern. C: Appl. Rev. 41 (2), 145–160 (2011).CrossRefGoogle Scholar
97.Zhang, M.et al., “Dynamic artificial potential field based multi-robot formation control,” Proceedings of the IEEE Instrumentation and Measurement Technology Conference, Austin, Texas (2010).Google Scholar
98.Lu, Y., Guo, Y. and Dong, Z., “Multiagent flocking with formation in a constrained environment,” J. Control Theory Appl. 8 (2), 151–159 (2010).CrossRefGoogle Scholar
99.Winfield, A., “Safety in numbers: Fault-tolerance in robot swarms,” Int. J. Modelling Identifi. Control 1 (1), 30–38 (2006).CrossRefGoogle Scholar
100.Bai, X.et al., “Deploying wireless sensors to achieve both coverage and connectivity,” Proceedings of the 7th ACM International Symposium on Mobile ad hoc Networking and Computing, Florence, Italy (2006).Google Scholar
101.Desai, J., Ostrowski, P. and Kumar, V., “Modeling and control of formations of nonholonomic mobile robots,” Trans. Robot. Autom. 17 (6), 905–908 (2001).CrossRefGoogle Scholar
102.Smith, B.et al., “Automatic Formation Deployment of Decentralized Heterogenous Multi-Robot Networks with Limited Sensing Capabilities,” Proceedings of the IEEE International Conference on Robotics and Automation, Kobe International Conference Center, Kobe, Japan (2009).Google Scholar
103.Jin, Y., Meng, Y. and Guo, H., “A Morphogenetic Self-Organization Algorithm for Swarm Robotic Systems Using Relative Position Information,” Proceedings of the UK Workshop on Computational Intelligence (UKCI), Colchester, UK (September 2010).Google Scholar
104.Mamei, M., Vasirani, M. and Zambonelli, F., “Experiments of morphogenesis in swarms of simple robots,” Appl. Artificial Intell. 18 (9–10), 903–919 (2004).CrossRefGoogle Scholar
105.Guo, H., Meng, Y. and Jin, Y., “Analysis of Local Communication Load in Shape Formation of a Distributed Morphogenetic Swarm Robotic System,” Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Barcelona, Spain (2010).Google Scholar
106.Barnes, L., Fields, M. and Valavanis, K., “Swarm formation control utilizing elliptical surfaces and limiting functions,” IEEE Trans. Syst. Man Cybern. 39 (6), 1434–1445 (2009).CrossRefGoogle ScholarPubMed
107.Xue, Z. and Zeng, J., “Formation Control Numerical Simulations of Geometric Patterns for Unmanned Autonomous Vehicles with Swarm Dynamical Methodologies,” Proceedings of the International Conference on Measuring Technology and Mechatronics Automation, Zhangjiajie, China (2009).Google Scholar
108.Esin, Y. and Unel, M., “Formation control of nonholonomic mobile robots using implicit polynomials and elliptic Fourier descriptors,” Turk. J. Electr. Eng. Comput. Sci. 18 (5), 765–780 (2010).Google Scholar
109.Grossman, D., Aranson, S. and Jacob, E., “Emergence of agent swarm migration and vortex formation through inelastic collisions,” New J. Phys. 10, 1–10 (2008).CrossRefGoogle Scholar
110.Tanner, H., Pappas, G. and Kumar, V., “Leader-to-formation stability,” IEEE Trans. Robot. Autom. 20 (3), 443–455 (2004).CrossRefGoogle Scholar
111.Gazi, V. and Passino, K., “Stability analysis of swarms,” IEEE Trans. Autom. Control 48 (4), 692–697 (2003).CrossRefGoogle Scholar
112.Han, X.et al., “Deploying Directional Wireless Sensors with Guaranteed Coverage and Connectivity,” Proceedings of IEEE SECON (2008).CrossRefGoogle Scholar
113.Ekici, E., Gu, Y. and Bozdag, D., “Mobility-based communication in wireless sensor networks,” IEEE Commun. Mag. 44 (7), 56–62 (2006).CrossRefGoogle Scholar
114.Cortes, J., Martinez, S. and Bullo, F., “Robust rendezvous for mobile autonomous agents via proximity graphs in arbitrary dimensions,” IEEE Trans. Autom. Control 51 (8), 1289–1298 (2006).CrossRefGoogle Scholar
115.Tanner, G., Jadbabaie, A. and Pappas, J., “Flocking in fixed and switching networks,” IEEE Trans. Autom. Control 52 (5), 863–868 (2007).CrossRefGoogle Scholar
116.Olfati-Saber, R., “Flocking for multi-agent dynamic systems: Algorithms and theory,” IEEE Trans. Autom. Control 51 (3), 401–420 (2006).CrossRefGoogle Scholar
117.Esposito, J., “Maintaining wireless connectivity constraints for robot swarms in the presence of obstacles,” J. Robot. 2011, 1–12 (2011).CrossRefGoogle Scholar
118.Kernbach, S., “Three Cases of Connectivity and Global Information Transfer in Robot Swarms,” In: Computing Research Repository. Vol. abs/1109.4221, 1–14 (2011).Google Scholar
119.Thrun, S., Robotic Mapping: A Survey in Exploring Artificial Intelligence in the New Millennium (Morgan Kaufmann, San Francisco, California, 2002).Google Scholar
120.Portugal, D. and Rocha, R., “A Survey on Multi-robot Patrolling Algorithms,” In: Proceedings of the IFIP Advances in Information and Communication Technology (2011) pp. 139–146.Google Scholar
121.Lyuba, A.et al., “An Approach to Multi-Robot Site Exploration Based on Principles of Self-Organisation,” Proceedings of the International Conference on Intelligent Robotics and Applications (2010).Google Scholar
122.Alunni, N.et al., “Hierarchical Swarm Robotics,” project number GSF113, Graduate Thesis (Worcester Polytechnic Institute, 2011) 103 pp.Google Scholar
123.O'Hara, K.et al., “Physical Path Planning Using the GNATs,” In: Proceedings of the IEEE International Conference on Robotics and Automation (2005) pp. 709–714.Google Scholar
124.Hofmeister, M., Liebsch, M. and Zell, A., “Visual Self-Localization for Small Mobile Robots with Weighted Gradient Orientation Histograms,” Proceedings of the 40th International Symposium on Robotics (2009).CrossRefGoogle Scholar
125.Jatmiko, W., Sekiyama, K. and Fukuda, T., “A pso-based mobile robot for odor source localization in dynamic advection-diffusion with obstacles environment: theory, simulation and measurement,” IEEE Comput. Intell. Mag. 2 (2), 37–51 (2007).CrossRefGoogle Scholar
126.Groß, R.et al., “Division of Labour in Self-Organized Groups,” In: SAB 2008 (Asada, M., ed.) (Springer-Verlag, Berlin, Germany, 2008) pp. 426–436.Google Scholar
127.Buniyamin, N.et al., “Robot global path planning overview and a variation of ant colony system algorithm,” Int. J. Math. Comput. Simul. 5 (1), 9–16 (2011).Google Scholar
128.Lei, B. and Li, W., “A Fuzzy Behaviours Fusion Algorithm for Mobile Robot Real-time Path Planning in Unknown Environment,” Proceedings of the 2007 IEEE International Conference on Integration Technology Shenzhen, China (2007).Google Scholar
129.Rigatos, G., “Distributed gradient and particle swarm optimization for multi-robot motion planning,” Robotica 26, 357–370 (2008).CrossRefGoogle Scholar
130.Tian, J., Gao, M. and Lu, E., “Dynamic Collision Avoidance Path Planning for Mobile Robot Based on Multi-Sensor Data Fusion by Support Vector Machine,” Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, Harbin (2007).Google Scholar
131.Hettiarachchi, S. and Spears, W., “Distributed adaptive swarm for obstacle avoidance,” Int. J. Intell. Comput. Cybern. 2 (4), 644–671 (2009).CrossRefGoogle Scholar
132.Berg, J., Ferguson, D. and Kuffner, J., “Anytime Path Planning and Replanning in Dynamic Environments,” Proceedings of the 2006 IEEE International Conference on Robotics and Automation, Orlando, Florida (2006).Google Scholar
133.Jin, Y., Meng, Y. and Guo, H., “A Morphogenetic Self-Organization Algorithm for Swarm Robotic Systems Using Relative Position Information,” Proceedings of the UK Workshop on Computational Intelligence (UKCI), Colchester, Essex, UK (2010).Google Scholar
134.Khozaee, A., Aminaiee, A. and Ghaffari, A., “A Swarm Robotic Approach to Distributed Object Pushing Using Fuzzy Controllers,” Proceedings of the IEEE International Conference on Robotics and Biomimetics, Bangkok (2008).Google Scholar
135.Tuci, E. and Ampatzis, C., “Evolution of Acoustic Communication Between two Cooperating Robots,” In: Proceedings of the 9th European Conference on Artificial Life, LNAI 4648 (Springer-Verlag, Berlin, Germany, 2007) pp. 395–404Google Scholar
136.Gross, R., Mondada, F. and Dorigo, M., “Transport of an Object by Six Pre-attached Robots Interacting via Physical Links,” Proceedings of the IEEE International Conference on Robotics and Automation (2006).Google Scholar
137.Liu, W., “Design and Modelling of Adaptive Foraging in Swarm Robotic Systems,” PhD Thesis (Faculty of Environment and Technology, University of West of England, Bristol, UK, 2008) 201 pp.Google Scholar
138.Parra-Gonzalez, E. and ýrez-Torres, J. Raḿ, “Object Path Planner for the Box Pushing Problem, In: Multi-Robot Systems, Trends and Development (Yasuda, T., ed.) (InTech, Croatia, 2011) pp. 291–306, ISBN: 978-953-307-425-2.Google Scholar
139.Vig, L. and Adams, J., “Multi-robot coalition formation,” IEEE Trans. Robot. 22 (4), 637–649 (2006).CrossRefGoogle Scholar
140.Fink, J., Hsieh, M. and Kumar, V., “Multi-Robot Manipulation via Caging in Environments with Obstacles,” Proceedings of the IEEE International Conference on Robotics and Automation, Pasedena, California (2008).Google Scholar
141.Wang, Z., Hirata, Y. and Kosuge, K., “Control a Rigid Caging Formation for Cooperative Object Transportation by Multiple Mobile Robots,” Proceedings of the IEEE International Conference on Robotics & Automation, New Orleans, Louisiana (2004).Google Scholar
142.Stirling, T. and Floreano, D., “Energy Efficient Swarm Deployment for Search in Unknown Environments in Swarm Intelligence,” In: Proceedings of the 7th International Conference on Swarm Intelligence (ANTS 2010), Brussels, Belgium, LNCS 6234 (Springer, New York, 2010) pp. 562–563.Google Scholar
143.Yang, S., Li, M. and Wu, J., “Scan-based movement-assisted sensor deployment methods in wireless sensor networks,” IEEE Trans. Parallel Distrib. Syst. 18 (8)1108–1121 (2007).CrossRefGoogle Scholar
144.Biswas, P. and Phoha, S., “Self-organizing sensor networks for integrated target surveillance,” IEEE Trans. Comput. 55 (8)1033–1047 (2006).CrossRefGoogle Scholar
145.Seyfried, J.et al., “The I-SWARM Project: Intelligent Small World Autonomous Robots for Micro-Manipulation,” In: Swarm Robotics, LNCS 3342 (Springer, Berlin, Germany, 2005) pp. 70–83.CrossRefGoogle Scholar
146.Jin, Y. and Meng, Y., “Morphogenetic robotics: An emerging new field in developmental robotics,” IEEE Trans. Syst. Man Cybern. C: Appl. Rev. 41 (2), 1–16 (2010).Google Scholar
147.Ge, S. and Fua, C., “Quenses and artificial potential trenches for multirobot formations,” IEEE Trans. Robot. Autom. 21 (4), 646–656 (2005).CrossRefGoogle Scholar
148.Barnes, L., Fields, M. and Valavanis, K., “Unmanned Ground Vehicle Swarm Formation Control Using Potential Fields,” Proceedings of the Mediterranean Conference on Control & Automation, Athens, Greece (2007).Google Scholar