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Multi-Robot nonlinear model predictive formation control: the obstacle avoidance problem

Published online by Cambridge University Press:  01 July 2014

Tiago P. Nascimento ,
André G. S. Conceição  and
António Paulo Moreira
Tiago P. Nascimento*
Affiliation:
Department of Computer Systems, Informatics Center, Federal University of Paraiba (UFPB), Cidade Universitaria - João Pessoa - PB - Brazil
André G. S. Conceição
Affiliation:
LaR - Robotics Lab, Department of Electrical Engineering, Polytechnic School, Federal University of Bahia (UFBA), Rua Aristides Novis, 02 Federação - Salvador-BA - Brazil
António Paulo Moreira
Affiliation:
INESC TEC (formerly INESC Porto) and Faculty of Engineering, University of Porto, rua Dr. Roberto Frias, 4200-465 Porto, Portugal
*
*Corresponding author. E-mail: tiagopn@ci.ufpb.br
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Summary

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This paper discusses about a proposed solution to the obstacle avoidance problem in multi-robot systems when applied to active target tracking. It is explained how a nonlinear model predictive formation control (NMPFC) previously proposed solves this problem of fixed and moving obstacle avoidance. First, an approach is presented which uses potential functions as terms of the NMPFC. These terms penalize the proximity with mates and obstacles. A strategy to avoid singularity problems with the potential functions using a modified A* path planning algorithm was then introduced. Results with simulations and experiments with real robots are presented and discussed demonstrating the efficiency of the proposed approach.

Keywords

Obstacle avoidanceNonlinear model predictive controlMobile robots

Information

Type
Articles
Information
Robotica , Volume 34 , Issue 3 , March 2016 , pp. 549 - 567
Copyright
Copyright © Cambridge University Press 2014 

References

1.2011, R.: Robocup 2011, istanbul, turkey (2011). http://www.robocup2011.org/en/. Available from http://www.robocup2011.org/en/Google Scholar
2.Ahmad, A. and Lima, P., “Multi-Robot Cooperative Object Tracking Based on Particle Filters,” Proceedings of the 5th European Conference on Mobile Robots, Örebro, Sweden (2011), pp. 1–6.Google Scholar
3.Ahmad, A., Nascimento, T. P., Conceição, A. G. S., Moreira, A. P. and Lima, P., “Perception-Driven Multi-Robot Formation Control,” Proceedings of the 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany (2013) pp. 1851–1856.CrossRefGoogle Scholar
4.Camacho, E. F. and Bordons, C., Model Predictive Control (Springer, London, England, 2004).Google Scholar
5.Chao, Z., Ming, L., Shaolei, Z. and Wenguang, Z., “Collision-free UAV Formation Flight Control based on Nonlinear MPC,” Proceedings of the International Conference on Electronics, Communications and Control (ICECC) (2011) pp. 1951–1956.Google Scholar
6.Ding, Y. and He, Y., “Flexible Leadership in Obstacle Environment,” Proceedings of the International Conference on Intelligent Control and Information Processing, China (2010) pp. 788–791.Google Scholar
7.Ferguson, D., Likhachev, M. and Stentz, A., “A Guide to Heuristic-based Path Planning,” Proceedings of the International Workshop on Planning under Uncertainty for Autonomous Systems. International Conference on Automated Planning and Scheduling (ICAPS), Monterey, CA, U.S.A (2005) pp. 1–10.Google Scholar
8.Fukushima, H., Kon, K. and Matsuno, F., “Model predictive formation control using branch-and-bound compatible with Collision avoidance problems,” IEEE Trans. Robot. 29 (5), 1308–1317 (2013).CrossRefGoogle Scholar
9.Gamage, G. W. and Mann, G., “Formation control of multiple nonholonomic mobile robots via dynamic feedback linearization,” Proceedings of the Robotics, 2009. ICAR 2009, Munich, Germany (2009) pp. 1–6.Google Scholar
10.Kon, K., Habasaki, S., Fukushima, H. and Matsuno, F., “Model Predictive Based Multi-Vehicle Formation Control with Collision Avoidance and Localization Uncertainty,” IEEE/SICE International Symposium on System Integration (SII) Kyushu University, Fukuoka, Japan (2012) pp. 212–217.Google Scholar
11.Latombe, J. C., Robot Motion Planning (Kluwer Academic Publishers, 1991).CrossRefGoogle Scholar
12.Lee, G. and Chong, N. Y., “Decentralized formation control for small-scale robot teams with anonymity,” Mechatronics 19 (1), 85–105 (2009).CrossRefGoogle Scholar
13.Lim, H., Kang, Y., Kim, J. and Kim, C., “Formation Control of Leader Following Unmanned Ground Vehicles Using Nonlinear Model Predictive Control,” In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Singapore (2009) pp. 945–950.Google Scholar
14.Maestre, J. M., de la Pena, D. M. and Camacho, E. F., “Distributed model predictive control based on a cooperative game,” Optimal Control Appl. Methods 32 (2), 153–176 (2011).CrossRefGoogle Scholar
15.Mohammadi, A. and Menhaj, M. B., “Formation Control and Obstacle Avoidance for Nonholonomic Robots Using Decentralized MPC,” 10th IEEE International Conference on Networking, Sensing and Control (ICNSC) (2013) pp. 112–117.Google Scholar
16.Mohammadi, A., Menhaj, M. B. and Doustmohammadi, A., “Distributed model predictive control and virtual force obstacle avoidance for formation of nonholonomic agents,” 2nd International Conference on Control, Instrumentation and Automation (ICCIA) (2011) pp. 240–245.Google Scholar
17.Monteiro, S. and Bicho, E., “Robot Formations: Robots Allocation and Leader-Follower Pairs,” Proceedings of the 2008 IEEE International Conference on Robotics and Automation (ICRA2008), Pasadena, CA, USA (2008) pp. 3769–3775.CrossRefGoogle Scholar
18.Monteiro, S. and Bicho, E., “Attractor dynamics approach to robot formations: Theory and implementation,” Auton. Robots 29 (3), 331–355 (2010).CrossRefGoogle Scholar
19.Morbidi, F. and Mariottini, G. L., “On Active Target Tracking and Cooperative Localization for Multiple Aerial Vehicles,” Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco - USA (2011) pp. 2229–2234.Google Scholar
20.Morbidi, F., Ray, C. and Mariottini, G. L., “Cooperative Active Target Tracking for Heterogeneous Robots with Application to Gait Monitorin,” 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco - USA (2011) pp. 3608–3613.CrossRefGoogle Scholar
21.Nascimento, T. P., ao, A.G.S.C. and Moreira, A. P., “Multi-robot nonlinear model predictive formation control: Moving target and target absence,” Robot. Auton. Syst. 61 (12), 1502–1515 (2013).Google Scholar
22.Nascimento, T. P., Conceição, A. G. S., Costa, P. G., Costa, P., Moreira, A. P. G. M., “A set of novel modifications to improve algorithms from the A* family applied in mobile robotics,” J. Braz. Comput. Soc. 18 (4), 167–179 (2012).Google Scholar
23.Nascimento, T. P., Conceição, A. G. S., Fontes, F. A., Moreira, A. P. G. M., “Leader Following Formation Control for Omnidirectional Mobile Robots: The Target Chasing Problem,” Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics (ICINCO), Noordwijkerhout, Netherlands (2011) pp. 102–111.Google Scholar
24.Nascimento, T. P., Pinto, M. A., Sobreira, H. M., Guedes, F., Castro, A., Malheiros, P., Pinto, A., Alves, H. P., Ferreira, M., Costa, P., Costa, P. G., Souza, A., Almeida, L., Reis, L. P. and Moreira, A. P., 5dpo Robot Soccer Team Description Paper (2011). http://paginas.fe.up.pt/~robosoc/en/doku.php. Available from http://paginas.fe.up.pt/~robosoc/en/doku.php.Google Scholar
25.Oliveira, L., Almeida, L. and Santos, F., “A loose synchronisation protocol for managing RF ranging in mobile Ad-Hoc networks,” RoboCup 2011: Robot Soccer World Cup XV, Lecture Notes in Computer Science, Springer Berlin Heidelberg, Istanbul, Turkey (2012) pp. 574–585.CrossRefGoogle Scholar
26.Pathak, K. and Agrawal, S. K., “An integrated path-planning and control approach for nonholonomic unicycles using switched local potentials,” IEEE Trans. Robot. 21 (6), 1201–1208 (2005).CrossRefGoogle Scholar
27.Riedmiller, M. and Braun, H., “A Direct Adaptive Method for Faster Backpropagation Learning: The Rprop Algorithm,” IEEE International Conference on Neural Networks, San Francisco, CA, USA (1993) pp. 586–591.CrossRefGoogle Scholar
28.Shijie, Z. and Guangren, D., “Collision Avoidance in Multi-agent Formation Keeping Cooperative Control Systems,” Proceedings of the 30th Chinese Control Conference, Yantai, China (2011) pp. 4758–4762.Google Scholar
29.Xin, C., Min, W. and Yangmin, L., “Formation Control Based on Adaptive NN with Time-Varying Interaction among Robots,” Proceedings of the 27th Chinese Control Conference, Kunming, Yunnan, China (2008) pp. 341–345.Google Scholar
30.Yang, S. X., “Real-time Torque Control of Nonholonomic Mobile Robots with Obstacle Avoidance,” Proceedings of the IEEE Internatinal Symposium on Intelligent Control, Vancouver, Canada (2002) pp. 81–86.Google Scholar
31.Yang, T. T., “Formation control and obstacle avoidance for multiple mobile robots,” Acta Autom. Sin. 34 (5), 588–592 (2008).CrossRefGoogle Scholar
32.Zhou, K. and Roumeliotis, S. I., “Multirobot active target tracking with combinations of relative observations,” IEEE Trans. Robot. 27 (4), 678–695 (2011).CrossRefGoogle Scholar
33.Zhou, K. X. and Roumeliotis, S. I., “Multi-robot Active Target Tracking with Distance and Bearing Observations,” The 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, Saint Louis - USA (2009) pp. 2209–2216.Google Scholar