Mobil robotlar ile dağıtık kapsama kontrolü: Bir potansiyel oyun yaklaşımı

Öz

Endüstriyel uygulamalarda mobil robotların kullanımı, gürbüz ve dağıtık algoritma içeren otonom çoklu-robot sistemlerine bir gereksinim oluşturmuştur. Bir robot takımının sınırlı bir alanda uzaysal-zamansal olaylara cevap vermesi anlamına gelen kapsama kontrolü bu tür sistemlerde kritik bir hedeftir. Bu çalışmada, bir grup mobil robotun doğrusal bir iş istasyonunun iki tarafında belirli lokasyonları kapsamakla görevli olduğu özel bir kapsama problemini ele alıyoruz. Problemi iyi kurgulanmış oyuncu stratejileri ile mobil robotlar arasında oynanan bir oyun olarak formalize ediyor ve ortaya çıkan yapının eşit paylaşılan fayda temelli bir potansiyel oyun olduğunu gösteriyoruz. Sunulan yapı, robotlarda anonim kimlikler ve kısıtlı algılama yeteneklerine izin veren dağıtık ve merkezi olmayan bir yapıdır. Bir grup simülasyon çalışması yaklaşımımızı doğrulamaktadır.

Anahtar Kelimeler

• Kapsama kontrolü
• Çoklu-robot sistemleri
• Öğrenme algoritmaları

Proje Numarası

118C348

Distributed coverage control with mobile robots: A potential game approach

Öz

The use of mobile robots in industrial applications has led to a demand for autonomous multi-robot systems with robust and distributed algorithms. A critical objective in such systems is coverage control, where a team of mobile robots need to respond to spatiotemporal events in a bounded region. Here, we address a specific coverage problem, where a group of mobile robots are tasked with responding to events by covering specific locations on two sides of a linear workstation. We formulate the problem as a game played by the mobile robots with well-designed player strategies, and we demonstrate that the resulting framework is a potential game based on equally shared utilities among the robots. The proposed framework is distributed and decentralized, allowing for anonymous identities and constrained sensing capabilities in the robots. A set of simulation studies verify our approach.

Anahtar Kelimeler

• Coverage control
• Multi-robot systems
• Learning algorithms

Destekleyen Kurum

TÜBİTAK

Proje Numarası

118C348

Teşekkür

This paper has been produced benefiting from the 2232 International Fellowship for Outstanding Researchers Program of TÜBİTAK (Project No: 118C348). However, the entire responsibility of the paper belongs to the owner of the paper. The financial support received from TÜBİTAK does not mean that the content of the publication is approved in a scientific sense by TÜBİTAK.

Kaynakça

1. F. Chen, M. Selvaggio and D. G. Caldwell, Dexterous grasping by manipulability selection for mobile manipulator with visual guidance, IEEE Transactions on Industrial Informatics. 15 (2), 1202-1210,2019, https://doi.org/10.1109/TII.2018.2879426.
2. G.B. Dai and Y.C. Liu, Distributed coordination and cooperation control for networked mobile manipulators, IEEE Transactions on Industrial Electronics, 64 (6), 5065-5074, 2017, https://doi.org/10.1109/TIE.2016.2642880.
3. H. Engemann, S. Du, S. Kallweit, P. Cönen, and H. Dawar, OMNIVIL—An Autonomous mobile manipulator for flexible production. Sensors, 20 (24), 7249, https://doi.org/10.3390/s20247249.
4. J. R. Marden, G. Arslan and J. S. Shamma, Cooperative control and potential games. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39 (6), 1393-1407, 2009, https://doi.org/10.1109/ TSMCB.2009.2017273.
5. J. Cortes, S. Martinez, T. Karatas and F. Bullo, Coverage control for mobile sensing networks. IEEE Transactions on Robotics and Automation, 20 (2), 243-255, 2004, https://doi.org/10.1109/TRA.2004.824698.
6. M. R. Olfati-Saber, J. A. Fax and R. M. Murray, Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE, 95 (1), 215-233, 2007, https://doi.org/10.1109/JPROC.2006.887293.
7. J. R. Marden and J. S. Shamma, Game theory and control, annual review of control. Robotics, and Autonomous Systems, 1(1), 105-134, 2018, https://doi.org/10.1146/annurev-control-060117 105102.
8. G. Arslan, J. R. Marden and J. S. Shamma, Autonomous vehicle-target assignment: a game-theoretical formulation, ASME. J. Dyn. Sys., Meas., Control, 129 (5), 584–596, 2007. https://doi.org/10.1115/1.2766722.

9. D. Monderer, L.S. Shapley, Potential games. Games and Economic Behavior, 14 (1), 124-143, 1996, https://doi.org/10.1006/game.1996.0044.
10. P. Li, H. Duan, A potential game approach to multiple UAV cooperative search and surveillance. Aerospace Science and Technology, 68 (1), 403-415, 2017, https://doi.org/10.1016/j.ast.2017.05.031.
11. U.O. Candogan, I. Menache, A. Ozdaglar and P.A. Parrilo, Near-optimal power control in wireless networks: a potential game approach. 2010 Proceedings IEEE INFOCOM, 2010,1-9, San Diego, CA, USA, https://doi.org/10.1109/ INFCOM.2010.5462017.
12. P. Yi, Y. Zhang and Y. Hong, Potential game design for a class of distributed optimization problems. Journal of Control and Decision, 1 (2), 166-179, 2014, https://doi.org/10.1080/23307706.2014.899111.
13. J. R. Marden, State based potential games. Automatica, 48 (12), 3075-3088, 2012, https://doi.org/10.1016/ j.automatica.2012.08.037.
14. S. Rahili and W. Ren, Game theory control solution for sensor coverage problem in unknown environment. 53rd IEEE Conference on Decision and Control, 1173-1178, Los Angeles, CA, USA, 2014, https://doi.org/10.1109/CDC.2014.7039540.
15. M. Zhu and S. Martinez, Distributed coverage games for energy-aware mobile sensor networks. SIAM Journal on Control and Optimization, 51 (1), 1-27, 2013, https://doi.org/10.1137/100784163.
16. S. Rahili, J. Lu, W. Ren and U. M. Al-Saggaf, Distributed coverage control of mobile sensor networks in unknown environment using game theory: algorithms and experiments. IEEE Transactions on Mobile Computing, 17 (6), 1303-1313, June 2018, https://doi.org/10.1109/TMC.2017.2761351.
17. E. Paraskevas, D. Maity and J. S. Baras, Distributed energy-aware mobile sensor coverage: A game theoretic approach. 2016 IEEE American Control Conference (ACC), 6259-6264, Boston, MA, USA, 2016, https://doi.org/10.1109/ACC.2016.7526653.
18. J. Ni, G. Tang, Z. Mo, W. Cao and S. X. Yang, An improved potential game theory based method for multi-uav cooperative search. IEEE Access, 8, 47787-47796, 2020, https://doi.org/10.1109/ access.2020.2978853.
19. J. R. Marden and J. S. Shamma, Revisiting log-linear learning: Asynchrony, completeness and payoff-based implementation. Games and Economic Behavior, 75 (2), 788-808, 2012, https://doi.org/ 10.1016/j.geb.2012.03.006.