Skip to main content Accessibility help
×
Home

Collaborative Control of Multiple Robots Using Genetic Fuzzy Systems

  • Anoop Sathyan (a1) and Ou Ma (a1)

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.

Copyright

Corresponding author

*Corresponding author. E-mail: sathyaap@ucmail.uc.edu

References

Hide All
1.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), 423431 (2015).
2.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), 117 (2018).
3.Kartoun, U., Stern, H. and Edan, Y., “A human-robot collaborative reinforcement learning algorithm,” J. Intell. Robot. Syst. 60(2), 217239 (2010).
4.Baranzadeh, A. and Savkin, A. V., “A distributed control algorithm for area search by a multi-robot team,” Robotica 35(6), 14521472 (2017).
5.Eoh, G., Choi, J. S. and Lee, B. H., “Faulty robot rescue by multi-robot cooperation,” Robotica 31(8), 12391249 (2013).
6.Sathyan, A., Ernest, N. D. and Cohen, K., “An efficient genetic fuzzy approach to UAV swarm routing,” Unmann. Syst. 4(02), 117127 (2016).
7.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).
8.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.
9.Hagras, H. A., “A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots,” IEEE Trans. Fuzzy Syst. 12(4), 524539 (2004).
10.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), 18691885 (2015).
11.Seraji, H. and Howard, A., “Behavior-based robot navigation on challenging terrain: A fuzzy logic approach,” IEEE Trans. Robot. Autom. 18(3), 308321 (2002).
12.Saffiotti, A., “The uses of fuzzy logic in autonomous robot navigation,” Soft Comput. 1(4), 180197 (1997).
13.He, S.-Z., Tan, S., Xu, F.-L. and Wang, P.-Z., “Fuzzy self-tuning of PID controllers,” Fuzzy Sets Syst. 56(1), 3746 (1993).
14.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), 216 (1999).
15.Jang, J.-S., “ANFIS: adaptive-network-based fuzzy inference system,” IEEE Trans. Syst. Man Cybern. 23(3), 665685 (1993).
16.Singh, R., Kainthola, A. and Singh, T., “Estimation of elastic constant of rocks using an ANFIS approach,” Appl. Soft Comput. 12(1), 4045 (2012).
17.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), 333341 (2012).
18.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), 34943506 (2012).
19.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), 295309 (1995).
20.Jain, R., Sivakumaran, N. and Radhakrishnan, T. K., “Design of self tuning fuzzy controllers for nonlinear systems,” Expert Syst. Appl. 38(4), 44664476 (2011).
21.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), 529533 (2015).
22.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), 484489 (2016).
23.Goldberg, D. E., Genetic algorithms in search, optimization, and machine learning (Addison Wesley, Boston, MA, 1989).
24.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.
25.Akbari, R. and Ziarati, K., “A multilevel evolutionary algorithm for optimizing numerical functions,” Int. J. Ind. Eng. Comput., 2(2), 419430 (2011).
26.Miller, B. L. and Goldberg, D. E., “Genetic algorithms, tournament selection, and the effects of noise,” Complex Syst. 9(3), 193212 (1995).
27.Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T., “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Comput. 6(2), 182197 (2002).
28.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.
29.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.
30.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).
31.Cordón, O. and Herrera, O. F., A general study on genetic fuzzy systems (John Wiley and Sons, Hoboken, NJ, USA, 1993) p. 125.

Keywords

Collaborative Control of Multiple Robots Using Genetic Fuzzy Systems

  • Anoop Sathyan (a1) and Ou Ma (a1)

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed