SavedAdd your journal

Journals / EMO Bilimsel Dergi / 2023 / Cilt: 13 Sayı: 3 Özel Sayı

Multi-Robot Task Allocation in Robotic Wireless Sensor Networks in Smart Agricultural Applications

Akıllı Tarım Uygulamalarında Robotik Kablosuz Sensör Ağlarında Çoklu Robot Görev Tahsisi

Ömer Melih Gül(BAHÇEŞEHİR ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)
Pages
35–44
Publication Date
2023-10-18
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Its performance is examined under different energy-harvesting methods for 6-robot for 5 tasks and 11-robot scenario for 10 tasks.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This paper studies an energy-aware multi-robot task-allocation (MRTA) problem in a robot network cluster with a base station and several clusters of robots in smart agricultural applications.
📊
Key Findings & Data:Each round has M number of tasks and M+1 robots.
💡
Conclusion & Impact:Its performance is examined under different energy-harvesting methods for 6-robot for 5 tasks and 11-robot scenario for 10 tasks.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Çok robotlu sistemler · görev tahsisi · kablosuz ağlar

Abstract

This paper studies an energy-aware multi-robot task-allocation (MRTA) problem in a robot network cluster with a base station and several clusters of robots in smart agricultural applications. Each round has M number of tasks and M+1 robots. A robot is elected cluster head and assigns one duty to each robot in that round. It gathers data from the remaining M robots and sends it to the BS. This work allocates M tasks to the remaining M robots optimally or near ideally by considering each node’s distance to travel, energy required for each task, battery level, and energy-harvesting capabilities. This paper introduces a new machine learning-based algorithm. Its performance is examined under different energy-harvesting methods for 6-robot for 5 tasks and 11-robot scenario for 10 tasks.

Özet

Bu bildiri, akıllı tarım uygulamalarında bir baz istasyonu ve birkaç robot kümesi içeren bir robot ağ kümesinde enerji farkında çok robotlu görev tahsisi (ÇRGT) problemi incelemektedir. Her turda M sayıda görev ve M +1 robot bulunur. Bir robot, küme başkanı seçilir ve o turdaki her robota bir görev verir. Kalan M robotlarından veri toplar ve bunu baz istasyonuna gönderir. Bu çalışma, her bir düğümün seyahat mesafesi, her görev için gereken enerji, pil seviyesi ve enerji toplama kapasitelerini göz önünde bulundurarak M görevlerini kalan M robotlarına optimum veya ideale yakın olarak tahsis eder. Bu bildiri, makine öğrenmesi tabanlı yeni bir algoritma tanıtmaktadır. Performansı, 5 görev içeren 6-robotlu ve 10 görev içeren 11-robotlu senaryo için farklı enerji hasatlama yöntemleri altında incelenmiştir.

🔖

Cite This Article

Gül, Ö. M. (2023). Multi-Robot Task Allocation in Robotic Wireless Sensor Networks in Smart Agricultural Applications. EMO Bilimsel Dergi, Cilt: 13 Sayı: 3 Özel Sayı, 35–44. https://ajindex.com/articles/1390313

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (29)

  1. P. Samuel S., K. Malarvizhi, S. Karthik and M. Gowri S.G., "Machine Learning and Internet of Things based Smart Agriculture," 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2020, pp. 1101-1106.
  2. Baek, E.T.; Im, D.Y. ROS-Based Unmanned Mobile Robot Platform for Agriculture. Appl. Sci. 2022, 12, 4335.
  3. Devanna, R.P.; Milella, A.; Marani, R.; Garofalo, S.P.; Vivaldi, G.A.; Pascuzzi, S.; Reina, G. In-field automatic identification of pomegranates using a farmer robot. Sensors 2022, 22, 5821.
  4. Yoshida, T.; Onishi, Y.; Kawahara, T.; Fukao, T. Automated harvesting by a dual-arm fruit harvesting robot. ROBOMECH J. 2022, 9, 19.
  5. Moraitis, M.; Vaiopoulos, K.; Balafoutis, A.T. Design and Implementation of an Urban Farming Robot. Micromachines 2022, 13, 250.
  6. Chong, C.-Y.; Kumar, S.P. Sensor networks: Evolution, opportunities, and challenges. Proc. IEEE 2003, 91, 1247–1256.
  7. Akyildiz, I.F.; Su, W.; Sankarasubramaniam, Y.; Cayirci, E. A survey on sensor networks. IEEE Commun. Mag. 2002, 40, 102–114.
  8. Lukic, M.; Barnawi, A.; Stojmenovic, I. Robot Coordination for Energy-Balanced Matching and Sequence Dispatch of Robots to Events. IEEE Trans. Comput. 2015, 64, 1416–1428.
  9. Gautam, A.; Thakur, A.; Dhanania, G.; Mohan, S. A distributed algorithm for balanced multi-robot task allocation. In Proceedings of the 2016 11th International Conference on Industrial and Information Systems (ICIIS), Roorkee, India, 3–4 December 2016; pp. 622–627.
  10. Shue, S.; Conrad, J. A Survey of Robotic Applications in Wireless Sensor Networks. In Proceedings of the IEEE Southeastcon 2013, Jacksonville, FL, USA, 4–7 April 2013; pp. 1–5.
  11. Ryu, J.H.; Irfan, M.; Reyaz, A. A review on sensor network issues and robotics. J. Sens. 2015, 2015, 1–14.
  12. Ghosh, P.; Gasparri, A.; Jin, J.; Krishnamachari, B. Robotic wireless sensor networks. In Mission-Oriented Sensor Networks and Systems: Art and Science. Studies in Systems, Decision and Control; Springer: Cham, Switzerland, 2019; Volume 164.
  13. Dasgupta, P. Multi-robot task allocation for performing cooperative foraging tasks in an initially unknown environment. In Innovations in Defence Support Systems-2: Socio-Technical Systems; Jain, L.C., Aidman, E.V., Abeynayake, C., Eds.; Springer: Berlin, Germany, 2011; pp. 5–20.
  14. Lim, S.; Rus, D. Stochastic motion planning with path constraints and application to optimal agent, resource, and route planning. In Proceedings of the 2012 IEEE International Conference on Robotics and Automation, Saint Paul, MN, USA, 14–18 May 2012; pp. 4814–4821.
  15. Jones, E.G.; Dias, M.B.; Stentz, A. Time-extended multi-robot coordination for domains with intra-path constraints. Auton. Robot. 2011, 30, 41–56.
  16. Lenagh, W.; Dasgupta, P.; Munoz-Melendez, A. A spatial queuing based algorithm for multi-robot task allocation. Robotics 2015, 4, 316–340.
  17. Gerkey, B.P.; Mataric, M.J. A formal analysis and taxonomy of task allocation in multi-robot systems. Int. J. Robot. Res. 2004, 23, 939–954.
  18. Hojda, M. Task allocation in robot systems with multi-modal capabilities. IFAC-PapersOnLine 2015, 48, 2109–2114.
  19. Patil, D.D., Singh, A.K., Shrivastava, A., Bairagi, D. (2023). IOT Sensor-Based Smart Agriculture Using Agro-robot. In: Sindhwani, N., Anand, R., Niranjanamurthy, M., Chander Verma, D., Valentina, E.B. (eds) IoT Based Smart Applications. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-04524-0_20
  20. Kuhn, H.W. The hungarian method for the assignment problem. Nav. Res. Logist. Q. 1955, 2, 83–97.
  21. Trigui, S.; Koubaab, A.; Cheikhrouhoue, O.; Yousseff, H.; Bennaceurg, H.; Sritig, M.F.; Javed, Y. A Distributed Market-Based Algorithm for the Multi-Robot Assignment Problem. Procedia Comput. Sci. 2014, 32, 1108–1114.
  22. Lukic, M.; Stojmenovic, I. Energy-balanced matching and sequence dispatch of robots to events: Pairwise exchanges and sensor assisted robot coordination. In Proceedings of the 10th IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems, Hangzhou, China, 14–16 October 2013; pp. 249–253.
  23. Luo, L.; Chakraborty, N.; Sycara, K. Provably-Good Distributed Algorithm for Constrained Multi-Robot Task Assignment for Grouped Tasks. IEEE Trans. Robot. 2015, 31, 19–30.
  24. Martin, J.G.; Frejo, J.R.D.; García, R.A.; Camacho, E.F. Multi-robot task allocation problem with multiple nonlinear criteria using branch and bound and genetic algorithms. Intel. Serv. Robot. 2021, 14, 707–727.
  25. Shin, H.S.; Li, T.; Lee, H.I.; Tsourdos, A. Sample greedy based task allocation for multiple robot systems. Swarm Intell. 2022, 16, 233–260.
  26. Ashraf, N.; Faizan, M.; Asif, W.; Qureshi, H.K.; Iqbal, A.; Lestas, M. Energy management in harvesting enabled sensing nodes: Prediction and control. J. Netw. Comput. Appl. 2019,132,104–117.
  27. Watkins, C.J.C.H., Dayan, P. Q-learning. Mach Learn 8, 279–292 (1992). https://doi.org/10.1007/BF00992698
  28. Gul OM. Energy Harvesting and Task-Aware Multi-Robot Task Allocation in Robotic Wireless Sensor Networks. Sensors. 2023; 23(6):3284.
  29. Gul OM. Akıllı Tarım Uygulamalarında Robotik Kablosuz Sensör Aglarında Çoklu Robot Görev Tahsisi. TOK 2023; syf. 348-352.