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Edge-weighted consensus-based formation control strategy with collision avoidance

Published online by Cambridge University Press:  28 February 2014

Riccardo Falconi
Affiliation:
Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi” (DEI), University of Bologna, Italy
Lorenzo Sabattini*
Affiliation:
Department of Sciences and Methods for Engineering (DISMI), University of Modena and Reggio Emilia, Italy
Cristian Secchi
Affiliation:
Department of Sciences and Methods for Engineering (DISMI), University of Modena and Reggio Emilia, Italy
Cesare Fantuzzi
Affiliation:
Department of Sciences and Methods for Engineering (DISMI), University of Modena and Reggio Emilia, Italy
Claudio Melchiorri
Affiliation:
Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi” (DEI), University of Bologna, Italy
*
*Corresponding author. E-mail: [email protected]

Summary

In this paper, a consensus-based control strategy is presented to gather formation for a group of differential-wheeled robots. The formation shape and the avoidance of collisions between robots are obtained by exploiting the properties of weighted graphs. Since mobile robots are supposed to move in unknown environments, the presented approach to multi-robot coordination has been extended in order to include obstacle avoidance. The effectiveness of the proposed control strategy has been demonstrated by means of analytical proofs. Moreover, results of simulations and experiments on real robots are provided for validation purposes.

Type
Articles
Copyright
Copyright © Cambridge University Press 2014 

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References

1.Balch, T., Dellaert, F., Feldman, A., Guillory, A., Isbell, C. L., Khan, Z., Pratt, S. C., Stein, A. N. and Wilde, H., “How multirobot systems research will accelerate our understanding of social animal behavior,” Proc. IEEE 94 (7), 14451463 (Jul. 2006).CrossRefGoogle Scholar
2.Reznikova, Z. I. and Ryabko, B. Ya., “Using the Ideas of the Information Theory to Study Communication Systems of Social Animals,” Proceedings of the IEEE International Symposium on Information Theory, 1995 (Sep. 1995) p. 78.Google Scholar
3.Reynolds, C. W., “Flocks, Herds and Schools: A Distributed Behavioral Model,” Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH '87), Vol. 21 (ACM, New York, NY, Jul. 1987) pp. 2534.CrossRefGoogle Scholar
4.Liu, Y. and Passino, K. M., “Stable social foraging swarm in a noisy environment,” IEEE Trans. Autom. Control 48 (4), 692697 (2004).Google Scholar
5.Lindhé, M., Ögren, P. and Johansson, K. H., “Flocking with Obstacle Avoidance: A New Distributed Coordination Algorithm Based on Voronoi Partitions,” Proceedings of the IEEE International Conference on Robotics and Automation (2005) pp. 1785–1790.Google Scholar
6.Rosales, A., Scaglia, G., Mut, V. and di Sciascio, F., “Formation control and trajectory tracking of mobile robotic systems – a linear algebra approach,” Robotica 29 (3), 335349 (2011).CrossRefGoogle Scholar
7.Leonard, N. and Fiorelli, E., “Virtual Leaders, Artificial Potentials and Coordinated Control of Groups,” Proceedings of the IEEE Conference on Decision and Control (2001) pp. 2968–2973.Google Scholar
8.Fax, J. A. and Murray, R. M., “Information flow and cooperative control of vehicle formations,” IEEE Trans. Autom. Control 49 (9), 14651476 (2004).CrossRefGoogle Scholar
9.Ren, W., Beard, R. and Atkins, E., “Information consensus in multivehicle cooperative control,” IEEE Control Syst. Mag. 27 (2), 7182 (2007).Google Scholar
10.Hsieh, M. A., Kumar, V. and Chaimowicz, L., “Decentralized controllers for shape generation with robotic swarms,” Robotica 26 (5), 691701 (2008).CrossRefGoogle Scholar
11.Sabattini, L., Secchi, C. and Fantuzzi, C., “Potential-Based Control Strategy for Arbitrary Shape Formations of Mobile Robots,” Proceedings of the IEEE/RJS International Conference on Intelligent Robots and Systems (2009) pp. 3762–3767.Google Scholar
12.Sabattini, L., Secchi, C. and Fantuzzi, C., “Arbitrarily shaped formations of mobile robots: Artificial potential fields and coordinate transformation,” Auton. Robots (Springer) 30 (4), 385397 (May 2011).CrossRefGoogle Scholar
13.Balch, T. and Hybinette, M., “Social Potentials for Scalable Multi–Robot Formations,” Proceedings of the IEEE International Conference on Robotics and Automation (2000) pp. 73–80.Google Scholar
14.Bachmayer, R. and Leonard, N. E., “Vehicle Networks for Gradient Descent in a Sampled Environment,” Proceedings of the IEEE International Conference on Decision and Control (2002) pp. 112–117.Google Scholar
15.Secchi, C. and Fantuzzi, C., “Formation Control Over Delayed Communication Networks,” Proceedings of the IEEE International Conference on Robotics and Automation (2008) pp. 563–568.Google Scholar
16.Ji, M. and Egerstedt, M., “Distributed coordination control of multiagent systems while preserving connectedness,” IEEE Trans. Robot. 23 (4), 693703 (2007).CrossRefGoogle Scholar
17.Dimarogonas, D. V. and Johansson, K. H., “Stability analysis for multi-agent systems using the incidence matrix: Quantized communication and formation control,” Automatica 46 (4), 695700 (2010).CrossRefGoogle Scholar
18.Olfati–Saber, R., “Flocking for multi-agent dynamic systems: Algorithms and theory,” IEEE Trans. Autom. Control 51, 401420 (2006).CrossRefGoogle Scholar
19.Summers, T. H., Yu, C. and Anderson, B. D. O., “Robustness to Agent Loss in Vehicle Formations and Sensor Networks,” Proceedings of the IEEE Conference on Decision and Control (2008) pp. 1193–1199.Google Scholar
20.Falconi, R., Gowal, S. and Martinoli, A., “Graph-Based Distributed Control of Non-Holonomic Vehicles Endowed with Local Positioning Information Engaged in Escorting Missions,” IEEE Conference on Robotics and Automation (ICRA 2010) (2010) pp. 3207–3214.Google Scholar
21.Falconi, R., Sabattini, L., Secchi, C., Fantuzzi, C. and Melchiorri, C., “A Graph-Based Collision-Free Distributed Formation Control Strategy,” Proceedings of the IFAC World Congress (2011) pp. 6011–6016.Google Scholar
22.Royle, G. and Godsil, C., Algebraic Graph Theory (Springer, New York, NY, 2001).Google Scholar
23.Murray, R. M., Olfati-Saber, R. and Fax, J. A., “Consensus and Cooperation in Networked Multi-Agent Systems,” Proc. IEEE 27 (1), 215233 (Jan. 2007).Google Scholar
24.Lumelsky, V. J. and Skewis, T., “Incorporating range sensing in the robot navigation function,” IEEE Trans. Syst. Man Cybern. 20 (5), 10581069 (Sep./Oct. 1990).CrossRefGoogle Scholar
25.Kamon, I., Rimon, E. and Rivlin, E., “Tangentbug: A range-sensor-based navigation algorithm,” Int. J. Robot. Res. 17 (9), 934953 (Sep. 1998).CrossRefGoogle Scholar
26.Khatib, O., “Real-time Obstacle Avoidance for Manipulators and Mobile Robots,” Proceedings of the IEEE International Conference on Robotics and Automation, Vol. 2 (Mar. 1985) pp. 500505.Google Scholar
27.Borenstein, J. and Koren, Y., “The vector field histogram-fast obstacle avoidance for mobile robots,” IEEE Trans. Robot. Autom. 7 (3), 278288 (Jun. 1991).CrossRefGoogle Scholar
28.Ulrich, I. and Borenstein, J., “Vfh+: Reliable Obstacle Avoidance for Fast Mobile Robots. Proceedings of the IEEE International Conference on Robotics and Automation, 1998, Vol. 2 (May 1998) pp. 15721577.Google Scholar
29.Sabattini, L., Chopra, N. and Secchi, C., “On Decentralized Connectivity Maintenance for Mobile Robotic Systems,” Proceedings of the IEEE Conference on Decision and Control (2011) pp. 988–993.Google Scholar
30.Sabattini, L., Chopra, N. and Secchi, C., “Distributed Control of Multi-Robot Systems with Global Connectivity Maintenance,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2011) pp. 2321–2326.Google Scholar
31.Sabattini, L., Chopra, N. and Secchi, C., “Decentralized connectivity maintenance for cooperative control of mobile robotic systems,” Int. J. Robot. Res. (SAGE) 32 (12), 14111423 (Oct. 2013).CrossRefGoogle Scholar
32.Sabattini, L., Secchi, C., Chopra, N. and Gasparri, A., “Distributed control of multi-robot systems with global connectivity maintenance,” IEEE Trans. Robot. 29 (5), 13261332 (Oct. 2013).CrossRefGoogle Scholar
33.Giordano, P. R., Franchi, A., Secchi, C. and Bülthoff, H. H., “A passivity-based decentralized strategy for generalized connectivity maintenance,” Int. J. Robot. Res. 32 (3), 299323 (2013).CrossRefGoogle Scholar
34.Siciliano, B., Sciavicco, L., Villani, L. and Oriolo, G., Robotics: Modelling, Planning and Control (Springer, London, 2009).CrossRefGoogle Scholar
35.MORE-pucks, MORE-pucks: A software platform for multi-robot experiments. Available at: http://www.arscontrol.unimore.it/morepucks/ (2010). (Accessed December 30, 2013)Google Scholar
36.Mondada, F., Bonani, M., Raemy, X., Pugh, J., Cianci, C., Klaptocz, A., Magnenat, S., Zufferey, J. C., Floreano, D. and Martinoli, A., “The E-puck, a Robot Designed for Education in Engineering,” Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions (2009) pp. 59–65.Google Scholar