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Collaborative Control of Multiple Robots Using Genetic Fuzzy Systems

Published online by Cambridge University Press:  15 April 2019

Anoop Sathyan Open the ORCID record for Anoop Sathyan [Opens in a new window]  and
Ou Ma
Anoop Sathyan*
Affiliation:
Department of Aerospace Engineering, University of Cincinnati, Cincinnati, OH 45221, USA. E-mail: [email protected]
Ou Ma
Affiliation:
Department of Aerospace Engineering, University of Cincinnati, Cincinnati, OH 45221, USA. E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

This paper introduces an approach of collaborative control for individual robots to collaboratively perform a common task, without the need for a centralized controller to coordinate the group. The approach is illustrated by an application example involving multiple robots performing a collaborative task to achieve a common goal. The objective of this example problem is to control multiple robots that are connected to an object through elastic cables in order to bring the object to a target position. There is no communication between the robots, and hence each robot is unaware of how the other robots are going to react at any instant. Only the information pertaining to the object and the target is available to all the robots at any instant. Genetic fuzzy system (GFS) is used to develop controller for each of the robots. The nonlinearity of fuzzy logic systems coupled with the search capability of genetic algorithms provides a tool to design controllers for such collaborative tasks. A set of training scenarios are developed to train the individual robot controllers for this task. The trained controllers are then tested on an extensive set of scenarios. This paper describes the development process of GFS controllers for dynamic case involving systems consisting of three robots. It is also shown that the GFS controllers are scalable for the more complex systems involving more than three robots.

Keywords

Collaborative controlGenetic fuzzy systemDecentralized controlIntelligent systemsMachine learningCable robot
Type
Articles
Information
Robotica , Volume 37 , Issue 11 , November 2019 , pp. 1922 - 1936
Copyright
© Cambridge University Press 2019 

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References

Mohanarajah, G., Usenko, V., Singh, M., D’Andrea, R. and Waibel, M., “Cloud-based collaborative 3d mapping in real-time with low-cost robots,” IEEE Trans. Autom. Sci. Eng. 12(2), 423–431 (2015).CrossRefGoogle Scholar
Realmuto, J., Warrier, R. B. and Devasia, S., “Data-inferred personalized human-robot models for iterative collaborative output tracking,” J. Intell. Robot. Syst. 91(2), 1–17 (2018).CrossRefGoogle Scholar
Kartoun, U., Stern, H. and Edan, Y., “A human-robot collaborative reinforcement learning algorithm,” J. Intell. Robot. Syst. 60(2), 217–239 (2010).CrossRefGoogle Scholar
Baranzadeh, A. and Savkin, A. V., “A distributed control algorithm for area search by a multi-robot team,” Robotica 35(6), 1452–1472 (2017).CrossRefGoogle Scholar
Eoh, G., Choi, J. S. and Lee, B. H., “Faulty robot rescue by multi-robot cooperation,” Robotica 31(8), 1239–1249 (2013).CrossRefGoogle Scholar
Sathyan, A., Ernest, N. D. and Cohen, K., “An efficient genetic fuzzy approach to UAV swarm routing,” Unmann. Syst. 4(02), 117–127 (2016).CrossRefGoogle Scholar
Ernest, N., Carroll, D., Schumacher, C., Clark, M., Cohen, K. and Lee, G., “Genetic fuzzy based artificial intelligence for unmanned combat aerial vehicle control in simulated air combat missions,” J. Def. Manag. 6(144), 2167–0374 (2016).Google Scholar
Sathyan, A., Ernest, N., Lavigne, L., Cazaurang, F., Kumar, M. and Cohen, K., “A Genetic Fuzzy Logic Based Approach to Solving the Aircraft Conflict Resolution Problem,” In: AIAA Information Systems-AIAA Infotech@Aerospace (2017) pp. 1751.Google Scholar
Hagras, H. A., “A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots,” IEEE Trans. Fuzzy Syst. 12(4), 524–539 (2004).CrossRefGoogle Scholar
Mobadersany, P., Khanmohammadi, S. and Ghaemi, S., “A fuzzy multi-stage path-planning method for a robot in a dynamic environment with unknown moving obstacles,” Robotica 33(9), 1869–1885 (2015).CrossRefGoogle Scholar
Seraji, H. and Howard, A., “Behavior-based robot navigation on challenging terrain: A fuzzy logic approach,” IEEE Trans. Robot. Autom. 18(3), 308–321 (2002).CrossRefGoogle Scholar
Saffiotti, A., “The uses of fuzzy logic in autonomous robot navigation,” Soft Comput. 1(4), 180–197 (1997).CrossRefGoogle Scholar
He, S.-Z., Tan, S., Xu, F.-L. and Wang, P.-Z., “Fuzzy self-tuning of PID controllers,” Fuzzy Sets Syst. 56(1), 37–46 (1993).CrossRefGoogle Scholar
Mudi, R. K. and Pal, N. R., “A robust self-tuning scheme for PI-and PD-type fuzzy controllers,” IEEE Trans. Fuzzy Syst. 7(1), 2–16 (1999).CrossRefGoogle Scholar
Jang, J.-S., “ANFIS: adaptive-network-based fuzzy inference system,” IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993).CrossRefGoogle Scholar
Singh, R., Kainthola, A. and Singh, T., “Estimation of elastic constant of rocks using an ANFIS approach,” Appl. Soft Comput. 12(1), 40–45 (2012).CrossRefGoogle Scholar
Khuntia, S. R. and Panda, S., “Simulation study for automatic generation control of a multi-area power system by ANFIS approach,” Appl. Soft Comput. 12(1), 333–341 (2012).CrossRefGoogle Scholar
Melin, P., Soto, J., Castillo, O. and Soria, J., “A new approach for time series prediction using ensembles of ANFIS models,” Expert Syst. Appl. 39(3), 3494–3506 (2012).CrossRefGoogle Scholar
Shimojima, K., Fukuda, T. and Hasegawa, Y., “Self-tuning fuzzy modeling with adaptive membership function, rules, and hierarchical structure based on genetic algorithm,” Fuzzy Sets Syst. 71(3), 295–309 (1995).CrossRefGoogle Scholar
Jain, R., Sivakumaran, N. and Radhakrishnan, T. K., “Design of self tuning fuzzy controllers for nonlinear systems,” Expert Syst. Appl. 38(4), 4466–4476 (2011).CrossRefGoogle Scholar
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S. and Hassabis, D., “Human-level control through deep reinforcement learning,” Nature 518(7540), 529–533 (2015).CrossRefGoogle ScholarPubMed
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T. and Hassabis, D., “Mastering the game of Go with deep neural networks and tree search,” Nature 529(7587), 484–489 (2016).CrossRefGoogle ScholarPubMed
Goldberg, D. E., Genetic algorithms in search, optimization, and machine learning (Addison Wesley, Boston, MA, 1989).Google Scholar
Ernest, N. and Cohen, K., “Fuzzy Clustering Based Genetic Algorithm for the Multi-Depot Polygon Visiting dubzins Multiple Traveling Salesman Problem,” In: Infotech@Aerospace 2012 (2012), Garden Grove, CA, p. 2562.Google Scholar
Akbari, R. and Ziarati, K., “A multilevel evolutionary algorithm for optimizing numerical functions,” Int. J. Ind. Eng. Comput., 2(2), 419–430 (2011).Google Scholar
Miller, B. L. and Goldberg, D. E., “Genetic algorithms, tournament selection, and the effects of noise,” Complex Syst. 9(3), 193–212 (1995).Google Scholar
Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T., “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Comput. 6(2), 182–197 (2002).CrossRefGoogle Scholar
Sathyan, A. and Ma, O., “Collaborative Control of Multiple Robots Using Genetic Fuzzy Systems Approach,” In: ASME 2018 Dynamic Systems and Control Conference, American Society of Mechanical Engineers (2018) p. V001T03A002.Google Scholar
Sathyan, A., Ma, O. and Cohen, K., “Intelligent Approach for Collaborative Space Robot Systems,” In: 2018 AIAA SPACE and Astronautics Forum and Exposition (2018), Orlando, FL, p. 5119.Google Scholar
Yager, R. R. and Zadeh, L. A., An Introduction to Fuzzy Logic Applications in Intelligent Systems, vol. 165 (Springer Science & Business Media, New York, 2012).Google Scholar
Cordón, O. and Herrera, O. F., A general study on genetic fuzzy systems (John Wiley and Sons, Hoboken, NJ, USA, 1993) p. 1–25.Google Scholar