Advertisement

HDec-POSMDPs MRS Exploration and Fire Searching Based on IoT Cloud Robotics

  • Ayman El ShenawyEmail author
  • Khalil Mohamed
  • Hany Harb
Research Article

Abstract

The multi-robot systems (MRS) exploration and fire searching problem is an important application of mobile robots which require massive computation capability that exceeds the ability of traditional MRS’s. This paper propose a cloud-based hybrid decentralized partially observable semi-Markov decision process (HDec-POSMDPs) model. The proposed model is implemented for MRS exploration and fire searching application based on the Internet of things (IoT) cloud robotics framework. In this implementation the heavy and expensive computational tasks are offloaded to the cloud servers. The proposed model achieves a significant improvement in the computation burden of the whole task relative to a traditional MRS. The proposed model is applied to explore and search for fire objects in an unknown environment; using different sets of robots sizes. The preliminary evaluation of this implementation demonstrates that as the parallelism of computational instances increase the delay of new actuation commands which will be decreased, the mean time of task completion is decreased, the number of turns in the path from the start pose cells to the target cells is minimized and the energy consumption for each robot is reduced.

Keywords

Multi-robot systems hybrid decentralized partially observable semi-Markov decision process (HDec-POSMDPs) multi-robot systems (MRS) exploration and fire searching cloud robotics h]cloud computing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    S. M. Wen, B. Ding, H. M. Wang, B. Hu, H. Liu, P. C. Shi. Towards migrating resource-consuming robotic software packages to cloud. In Proceedings of IEEE International Conference on Real-time Computing and Robotics, IEEE, Angkor Wat, Cambodia, pp. 283–288, 2016. DOI:  https://doi.org/10.1109/RCAR.2016.7784040.
  2. [2]
    R. Janssen, R. Van De Molengraft, H. Bruyninckx, M. Steinbuch. Cloud based centralized task control for human domain multi-robot operations. Intelligent Service Robotics, vol. 9, no. 1, pp. 63–77, 2016. DOI:  https://doi.org/10.1007/s11370-015-0185-y.CrossRefGoogle Scholar
  3. [3]
    J. Salmerón-García, P. Íñigo-Blasco, F. Díaz-del-Río, D. Cagigas-Muñiz. A tradeoff analysis of a cloud-based robot navigation assistant using stereo image processing. IEEE Transactions on Automation Science and Engineering, vol. 12, no. 2, pp. 444–454, 2015. DOI:  https://doi.org/10.1109/TASE.2015.2403593.CrossRefGoogle Scholar
  4. [4]
    H. J. Li, A. G. Song. Architectural design of a cloud robotic system for upper-limb rehabilitation with multimodal interaction. Journal of Computer Science and Technology, vol. 32, no. 2, pp. 258–268, 2017. DOI:  https://doi.org/10.1007/s11390-017-1720-4.MathSciNetCrossRefGoogle Scholar
  5. [5]
    A. Manzi, L. Fiorini, R. Esposito, M. Bonaccorsi, I. Mannari, P. Dario, F. Cavallo. Design of a cloud robotic system to support senior citizens: The KuBo experience. Autonomous Robots, vol. 41, no. 3, pp. 699–709, 2017. DOI: {rs 10.1007/s10514-016-9569-x DOI}.CrossRefGoogle Scholar
  6. [6]
    C. Y. Li, I. H. Li, Y. H. Chien, W. Y. Wang, C. C. Hsu. Improved Monte Carlo localization with robust orientation estimation based on cloud computing. In Proceedings of IEEE Congress on Evolutionary Computation, IEEE, Vancouver, Canada, pp. 4522–4527, 2016. DOI:  https://doi.org/10.1109/CEC.2016.7744365.Google Scholar
  7. [7]
    E. Tosello, Z. J. Fan, A. G. Castro, E. Pagello. Cloud-based task planning for smart robots. In Proceedings of the 14th International Conference on Intelligent Autonomous Systems, Springer, Shanghai, China, pp. 285–300, 2017. DOI:  https://doi.org/10.1007/978-3-319-48036-7_21.CrossRefGoogle Scholar
  8. [8]
    R. Limosani, A. Manzi, L. Fiorini, F. Cavallo, P. Dario. Enabling global robot navigation based on a cloud robotics approach. International Journal of Social Robotics, vol. 8, no. 3, pp. 371–380, 2016. DOI:  https://doi.org/10.1007/s12369-016-0349-8.CrossRefGoogle Scholar
  9. [9]
    A. Rahman, J. Jin, A. Cricenti, A. Rahman, M. Palaniswami, T. Luo. Cloud-enhanced robotic system for smart city crowd control. Journal of Sensor and Actuator Networks, vol. 5, pp. 20–36, 2016. DOI:  https://doi.org/10.3390/jsan5040020.CrossRefGoogle Scholar
  10. [10]
    L. J. Wang, M. Liu, M. Q. H. Meng. A pricing mechanism for task oriented resource allocation in cloud robotics. Robots and Sensor Clouds, Koubaa A., Shakshuki E., Eds., Cham, Germany: Springer, vol. 36, pp. 3–31, 2016. DOI:  https://doi.org/10.1007/978-3-319-22168-7_1.CrossRefGoogle Scholar
  11. [11]
    A. Manzi, L. Fiorini, R. Limosani, P. Sinèák, P. Dario, F. Cavallo. Use case evaluation of a cloud robotics teleoperation system. In Proceedings of the 5th IEEE International Conference on Cloud Networking, IEEE, Pisa, Italy, pp. 208–211, 2016. DOI:  https://doi.org/10.1109/CloudNet.2016.49.Google Scholar
  12. [12]
    A. Rodić, M. Jovanović, M. Vujović, D. Urukalo. Application-driven cloud-based control of smart multi-robot store scenario. In Proceedings of the 25th Conference on Robotics in Alpe-Adria-Danube Region, Springer, Belgrade, Serbia, pp. 347–357, 2016. DOI:  https://doi.org/10.1007/978-3-319-49058-8_38.Google Scholar
  13. [13]
    A. G. Thallas, K. Panayiotou, E. Tsardoulias, A. L. Symeonidis, P. A. Mitkas, G. G. Karagiannis. Relieving robots from their burdens: The cloud agent concept. In Proceedings of the 5th IEEE International Conference on Cloud Networking, IEEE, Pisa, Italy, pp. 188–191, 2016. DOI:  https://doi.org/10.1109/CloudNet.2016.38.Google Scholar
  14. [14]
    A. Rahman, J. Jin, Y. W. Wong, K. S. Lam. Development of a cloud-enhanced investigative mobile robot. In Proceedings of International Conference on Advanced Mechatronic Systems, IEEE, Melbourne, Australia, pp. 104–109, 2016. DOI:  https://doi.org/10.1109/ICAMechS.2016.7813429.Google Scholar
  15. [15]
    L. J. Wang, M. Liu, M. Q. H. Meng. Real-time multisensor data retrieval for cloud robotic systems. IEEE Transactions on Automation Science and Engineering, vol. 12, no. 2, pp. 507–518, 2015. DOI:  https://doi.org/10.1109/TASE.2015.2408634.CrossRefGoogle Scholar
  16. [16]
    G. Ermacora, A. Toma, R. Antonini, S. Rosa. Leveraging open data for supporting a cloud robotics service in a smart city environment. In Proceedings of the 13th International Conference IAS-13, Springer, Padua, Italy, pp. 527–538, 2016. DOI:  https://doi.org/10.1007/978-3-319-08338-4_39.Google Scholar
  17. [17]
    D. Lorencik, J. Ondo, P. Sincak, H. Wagatsuma. Cloud-based image recognition for robots. In Proceedings of the 3rd International Conference on Robot Intelligence Technology and Applications, Springer, Beijing, China, pp. 785–796, 2015. DOI:  https://doi.org/10.1007/978-3-319-16841-8_71.Google Scholar
  18. [18]
    T. Nam Khoon, P. Sebastian, A. B. S. Saman. Autonomous fire fighting mobile platform. Procedia Engineering, vol. 41, pp. 1145–1153, 2012. DOI:  https://doi.org/10.1016/j.proeng.2012.07.294.CrossRefGoogle Scholar
  19. [19]
    Y. D. Kim, Y. G. Kim, S. H. Lee, J. H. Kang, J. An. Portable fire evacuation guide robot system. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, St. Louis, USA, pp. 2789–2794, 2009. DOI:  https://doi.org/10.1109/IROS.2009.5353970.Google Scholar
  20. [20]
    P. H. Chang, Y. H. Kang, G. R. Cho, J. H. Kim, M. L. Jin, J. Lee, J. W. Jeong, D. K. Han, J. H. Jung, W. J. Lee, Y. B. Kim. Control architecture design for a fire searching robot using task oriented design methodology. In Proceedings of SICE-ICASE International Joint Conference, IEEE, Busan, South Korea, pp. 3126–3131, 2006. DOI:  https://doi.org/10.1109/SICE.2006.314817.Google Scholar
  21. [21]
    H. S. Sucuoglu, I. Bogrekci, P. Demircioglu. Development of mobile robot with sensor fusion fire detection unit. IFAC-PapersOnLine, vol. 51, no. 30, pp. 430–435, 2018. DOI:  https://doi.org/10.1016/j.ifacol.2018.11.324.CrossRefGoogle Scholar
  22. [22]
    K. L. Su. Automatic fire detection system using adaptive fusion algorithm for fire fighting robot. In Proceedings of IEEE International Conference on Systems, Man and Cybernetics, IEEE, Taipei, China, pp. 966–971, 2006,. DOI:  https://doi.org/10.1109/ICSMC.2006.384525.Google Scholar
  23. [23]
    B. Hu, H. M. Wang, P. F. Zhang, B. Ding, H. M. Che. Cloudroid: A cloud framework for transparent and QoS-aware robotic computation outsourcing. In Proceedings of the 10th IEEE International Conference on Cloud Computing, IEEE, Honolulu, USA, pp. 114–121, 2017. DOI:  https://doi.org/10.1109/CLOUD.2017.23.Google Scholar
  24. [24]
    Y. Y. Li, H. M. Wang, B. Ding, P. C. Shi, X. Liu. Toward QoS-aware cloud robotic applications: A hybrid architecture and its implementation. In Proceedings of International IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress, IEEE, Toulouse, France, pp. 33–40, 2016. DOI:  https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0028.Google Scholar
  25. [25]
    L. Zhang, H. X. Zhang, Z. Fang, X. B. Xiang, M. Huchard, R. Zapata. Towards an architecture-centric approach to manage variability of cloud robotics. In Proceedings of DSLRob: Domain-specific Languages and Models for ROBotic Systems, HAL, Hamburg, Germany, 2015.Google Scholar
  26. [26]
    H. Z. Chen, G. H. Tian, F. Lu, G. L. Liu. A hybrid cloud robot framework based on intelligent space. In Proceedings of the 12th World Congress on Intelligent Control and Automation, IEEE, Guilin, China, pp. 2996–3001, 2016. DOI:  https://doi.org/10.1109/WCICA.2016.7578487.Google Scholar
  27. [27]
    C. Razafimandimby, V. Loscri, A. M. Vegni. Towards efficient deployment in internet of robotic things. Integration, Interconnection, and Interoperability of IoT Systems, R. Gravina, C. E. Palau, M. Manso, A. Liotta, G. Fortino, Eds., Cham, Germany: Springer, pp. 21–37, 2018. DOI:  https://doi.org/10.1007/978-3-319-61300-0_2.
  28. [28]
    P. P. Ray. Internet of robotic things: Concept, technologies, and challenges. IEEE Access, vol. 4, pp. 9489–9500, 2016. DOI:  https://doi.org/10.1109/ACCESS.2017.2647747.CrossRefGoogle Scholar
  29. [29]
    H. H. Yan, Q. S. Hua, Y. Y. Wang, W. G. Wei, M. Imran. Cloud robotics in smart manufacturing environments: Challenges and countermeasures. Computers & Electrical Engineering, vol. 63, pp. 56–65, 2017. DOI:  https://doi.org/10.1016/j.compeleceng.2017.05.024.CrossRefGoogle Scholar
  30. [30]
    R. Doriya, P. Sao, V. Payal, V. Anand, P. Chakraborty. A review on cloud robotics based frameworks to solve simultaneous localization and mapping (SLAM) problem. International Journal of Advances in Computer Science and Cloud Computing, vol. 3, no. 1, pp. 40–45, 2015.Google Scholar
  31. [31]
    L. H. Wang, X. V. Wang. Resource efficiency calculation as a cloud service. Cloud-based Cyber-physical Systems in Manufacturing, L. H. Wang, X. V. Wang, Eds., Cham, Germany: Springer, pp. 195–209, 2018. DOI:  https://doi.org/10.1007/978-3-319-67693-7_8.
  32. [32]
    R. Arumugam, V. R. Enti, L. B. B. Liu, X. J. Wu, K. Baskaran, F. F. Kong, A. S. Kumar, K. D. Meng, G. W. Kit. DAvinCi: A cloud computing framework for service robots. In Proceedings of IEEE International Conference on Robotics and Automation, IEEE, Anchorage, USA, pp. 3084–3089, 2010. DOI:  https://doi.org/10.1109/ROBOT.2010.5509469.Google Scholar
  33. [33]
    M. Garzón, J. Valente, J. J. Roldán, D. Garzón-Ramos, J. De León, A. Barrientos, J. Del Cerro. Using ROS in multi-robot systems: Experiences and lessons learned from real-world field tests. Robot Operating System (ROS), A. Koubaa, Ed., Cham, Germany: Springer, pp. 449–483, 2017. DOI:  https://doi.org/10.1007/978-3-319-54927-9_14.CrossRefGoogle Scholar
  34. [34]
    J. H. Xiao, D. Xiong, W. J. Yao, Q. H. Yu, H. M. Lu, Z. Q. Zheng. Building software system and simulation environment for RoboCup MSL soccer robots based on ROS and gazebo. Robot Operating System (ROS), A. Koubaa, Ed., Cham, Germany: Springer, pp. 597–631, 2017. DOI:  https://doi.org/10.1007/978-3-319-54927-9_18.CrossRefGoogle Scholar
  35. [35]
    P. Yun, J. H. Jiao, M. Liu. Towards a cloud robotics platform for distributed visual SLAM. In Proceedings of the 11th International Conference on Computer Vision Systems, Springer, Shenzhen, China, pp. 3–15, 2017. DOI:  https://doi.org/10.1007/978-3-319-68345-4_1.CrossRefGoogle Scholar
  36. [36]
    X. V. Wang, L. H. Wang, A. Mohammed, M. Givehchi. Ubiquitous manufacturing system based on cloud: A robotics application. Robotics and Computer-Integrated Manufacturing, vol. 45, pp. 116–125, 2017. DOI:  https://doi.org/10.1016/j.rcim.2016.01.007.CrossRefGoogle Scholar
  37. [37]
    W. H. Chen, Y. Yaguchi, K. Naruse, Y. Watanobe, K. Nakamura. QoS-aware robotic streaming workflow allocation in cloud robotics systems. IEEE Transactions on Services Computing, to be published. DOI:  https://doi.org/10.1109/TSC.2018.2803826.
  38. [38]
    J. F. Wan, S. L. Tang, H. H. Yan, D. Li, S. Y. Wang, A. V. Vasilakos. Cloud robotics: Current status and open issues. IEEE Access, vol. 4, pp. 2797–2807, 2016. DOI:  https://doi.org/10.1109/ACCESS.2016.2574979.CrossRefGoogle Scholar
  39. [39]
    E. Cardarelli, V. Digani, L. Sabattini, C. Secchi, C. Fantuzzi. Cooperative cloud robotics architecture for the coordination of multi-AGV systems in industrial warehouses. Mechatronics, vol. 45, pp. 1–13, 2017. DOI:  https://doi.org/10.1016/j.mechatronics.2017.04.005.CrossRefGoogle Scholar
  40. [40]
    G. Q. Hu, W. P. Tay, Y. G. Wen. Cloud robotics: Architecture, challenges and applications. IEEE Network, vol. 26, no. 3, pp. 21–28, 2012. DOI:  https://doi.org/10.1109/MNET.2012.6201212.CrossRefGoogle Scholar
  41. [41]
    R. Janssen, R. Van De Molengraft, H. Bruyninckx, M. Steinbuch. Cloud based centralized task control for human domain multi-robot operations. Intelligent Service Robotics, vol. 9, no. 1, pp. 63–77, 2016. DOI:  https://doi.org/10.1007/s11370-015-0185-y.CrossRefGoogle Scholar
  42. [42]
    J. F. Wan, S. L. Tang, Q. S. Hua, D. Li, C. L. Liu, J. Lloret. Context-aware cloud robotics for material handling in cognitive industrial internet of things. IEEE Internet of Things Journal, vol. 5, no. 4, pp. 2272–2281, 2018. DOI:  https://doi.org/10.1109/JIOT.2017.2728722.CrossRefGoogle Scholar
  43. [43]
    B. W. Liu, Y. Chen, E. Blasch, K. Pham, D. Shen, G. S. Chen. A holistic cloud-enabled robotics system for real-time video tracking application. Future Information Technology, J. J. Park, I. Stojmenovic, M. Choi, F. Xhafa, Eds., Berlin Heidelberg, Germany: Springer, pp. 455–468, 2014. DOI:  https://doi.org/10.1007/978-3-642-40861-8_64.CrossRefGoogle Scholar
  44. [44]
    M. Bonaccorsi, L. Fiorini, F. Cavallo, A. Saffiotti, P. Dario. A cloud robotics solution to improve social assistive robots for active and healthy aging. International Journal of Social Robotics, vol. 8, no. 3, pp. 393–408, 2016. DOI:  https://doi.org/10.1007/s12369-016-0351-1.CrossRefGoogle Scholar
  45. [45]
    M. Tenorth, K. Kamei, S. Satake, T. Miyashita, N. Hagita. Building knowledge-enabled cloud robotics applications using the ubiquitous network robot platform. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Tokyo, Japan, pp. 5716–5721, 2013. DOI:  https://doi.org/10.1109/IROS.2013.6697184.Google Scholar
  46. [46]
    G. Mohanarajah, D. Hunziker, R. D’Andrea, M. Waibel. Rapyuta: A cloud robotics platform. IEEE Transactions on Automation Science and Engineering, vol. 12, no. 2, pp. 481–493, 2015. DOI:  https://doi.org/10.1109/TASE.2014.2329556.CrossRefGoogle Scholar
  47. [47]
    D. Hunziker, M. Gajamohan, M. Waibel, R. D’Andrea. Rapyuta: The RoboEarth cloud engine. In Proceedings of IEEE International Conference on Robotics and Automation, IEEE, Karlsruhe, Germany, pp. 438–444, 2013. DOI:  https://doi.org/10.1109/ICRA.2013.6630612.Google Scholar
  48. [48]
    G. Mohanarajah, V. Usenko, M. Singh, R. D’Andrea, M. Waibel. Cloud-based collaborative 3D mapping in real-time with low-cost robots. IEEE Transactions on Automation Science and Engineering, vol. 12, no. 2, pp. 423–431, 2015. DOI:  https://doi.org/10.1109/TASE.2015.2408456.CrossRefGoogle Scholar
  49. [49]
    H. Bao, H. M. Wang, B. Ding, S. N. Shang. Cloud-based knowledge sharing in cooperative robot tracking of multiple targets with deep neural network. In Proceedings of the 24th International Conference on Neural Information Processing, Springer, Guangzhou, China, pp. 71–80, 2017. DOI:  https://doi.org/10.1007/978-3-319-70136-3_8.CrossRefGoogle Scholar
  50. [50]
    S. Kamburugamuve, H. J. He, G. Fox, D. Crandall. Cloud-based parallel implementation of SLAM for mobile robots. In Proceedings of the International Conference on Internet of things and Cloud Computing, ACM, Cambridge, UK, Article number 48, 2016. DOI:  https://doi.org/10.1145/2896387.2896433.Google Scholar
  51. [51]
    H. J. He, S. Kamburugamuve, G. C. Fox, W. Zhao. Cloud based real-time multi-robot collision avoidance for swarm robotics. International Journal of Grid and Distributed Computing, vol. 9, no. 6, pp. 339–358, 2016. DOI:  https://doi.org/10.14257/ijgdc.CrossRefGoogle Scholar
  52. [52]
    A. Rahman, J. Jin, A. L. Cricenti, A. Rahman, A. Kulkarni. Communication-aware cloud robotic task off-loading with on-demand mobility for smart factory maintenance. IEEE Transactions on Industrial Informatics, vol. 15, no. 5, pp. 2500–2511, 2019. DOI:  https://doi.org/10.1109/TII.2018.2874693.CrossRefGoogle Scholar
  53. [53]
    W. H. Chen, Y. Yaguchi, K. Naruse, Y. Watanobe, K. Nakamura, J. Ogawa. A study of robotic cooperation in cloud robotics: Architecture and challenges. IEEE Access, vol. 6, pp. 36662–36682, 2018. DOI:  https://doi.org/10.1109/ACCESS.2018.2852295.CrossRefGoogle Scholar
  54. [54]
    S. A. Miratabzadeh, N. Gallardo, N. Gamez, K. Haradi, A. R. Puthussery, P. Rad, M. Jamshidi. Cloud robotics: A software architecture: For heterogeneous large-scale autonomous robots. In Proceedings of World Automation Congress, IEEE, Rio Grande, Puerto Rico, pp. 1–6, 2016. DOI:  https://doi.org/10.1109/WAC.2016.7583017.Google Scholar
  55. [55]
    N. Tian, M. Matl, J. Mahler, Y. X. Zhou, S. Staszak, C. Correa, S. Zheng, Q. Li, R. Zhang, K. Goldberg. A cloud robot system using the dexterity network and Berkeley robotics and automation as a service (Brass). In Proceedings of IEEE International Conference on Robotics and Automation, IEEE, Singapore, pp. 1615–1622, 2017. DOI:  https://doi.org/10.1109/ICRA.2017.7989192.Google Scholar
  56. [56]
    A. Koubaa, B. Qureshi. DroneTrack: Cloud-based real-time object tracking using unmanned aerial vehicles. IEEE Access, vol. 6, pp. 13810–13824, 2018. DOI:  https://doi.org/10.1109/ACCESS.2018.2811762.CrossRefGoogle Scholar
  57. [57]
    F. Yan, Y. S. Liu, J. Z. Xiao. Path planning in complex 3D environments using a probabilistic roadmap method. International Journal of Automation and Computing, vol. 10, no. 6, pp. 525–533, 2013. DOI:  https://doi.org/10.1007/s11633-013-0750-9.CrossRefGoogle Scholar
  58. [58]
    K. Mohamed, A. Elshenawy, H. Harb. Exploration strategies of coordinated multi-robot systems: A comparative study. International Journal of Robotics and Automation, vol. 7, no. 1, pp. 48–58, 2018.Google Scholar
  59. [59]
    K. Mohamed, A. El Shenawy, H. Harb. A hybrid decentralized coordinated approach for multi-robot exploration task. The Computer Journal, to be published. DOI:  https://doi.org/10.1093/comjnl/bxy107.
  60. [60]
    A. El Shenawy, K. M. Khalil, H. M. Harb. A task decomposition using (HDec-POSMDPs) approach for multi-robot exploration and fire searching. International Journal of Imaging and Robotics, no. 19, pp. 2–2019, 2019.Google Scholar
  61. [61]
    J. Faigl, M. Kulich, L. Přeučil. Goal assignment using distance cost in multi-robot exploration. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Vilamoura, Portugal, pp. 3741–3746, 2012. DOI:  https://doi.org/10.1109/IROS.2012.6385660.Google Scholar
  62. [62]
    M. Al-khawaldah, T. M. Younes, I. Al-Adwan, M. Nisirat, M. Alshamasin. Automated multi-robot search for a stationary target. International Journal of Control Science and Engineering, vol. 4, no. 1, pp. 9–15, 2014. DOI:  https://doi.org/10.5923/j.control.20140401.02.Google Scholar
  63. [63]
    S. Omidshafiei, A. A. Agha-Mohammadi, C. Amato, S. Y. Liu, J. P. How, J. Vian. Decentralized control of multi-robot partially observable Markov decision processes using belief space macro-actions. The International Journal of Robotics Research, vol. 36, no. 2, pp. 231–258, 2017. DOI:  https://doi.org/10.1177/0278364917692864.CrossRefGoogle Scholar
  64. [64]
    Y. Kantaros, M. M. Zavlanos. Distributed intermittent connectivity control of mobile robot networks. IEEE Transactions on Automatic Control, vol. 2, no. 7, pp. 3109–3121, 2017. DOI:  https://doi.org/10.1109/TAC.2016.2626400.MathSciNetCrossRefzbMATHGoogle Scholar
  65. [65]
    A. Pal, R. Tiwari, A. Shukla. Coordinated multi-robot exploration under connectivity constraints. Journal of Information Science and Engineering, vol. 29, pp. 711–727, 2013.Google Scholar

Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Systems and Computers Engineering Department, Faculty of EngineeringAl-Azhar UniversityCairoEgypt
  2. 2.Information Technology CollegeMisr University for Science and Technology (MUST)Cairo 77Egypt

Personalised recommendations