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

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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.

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References

  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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  MathSciNet  Google Scholar 

  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}.

    Article  Google Scholar 

  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. 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.

    Chapter  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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. 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. 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. 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.

    Article  Google Scholar 

  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. 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.

    Chapter  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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. 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.

    Article  Google Scholar 

  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. 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. 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. 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. 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. 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.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Google Scholar 

  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. 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.

    Chapter  Google Scholar 

  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.

    Chapter  Google Scholar 

  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.

    Chapter  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Chapter  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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.

    Chapter  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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. 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. 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. 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.

    Article  Google Scholar 

  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.

    Article  MathSciNet  MATH  Google Scholar 

  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 

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Authors and Affiliations

  1. Systems and Computers Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo, 11751, Egypt

    Ayman El Shenawy & Khalil Mohamed

  2. Information Technology College, Misr University for Science and Technology (MUST), Cairo 77, Egypt

    Hany Harb

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  1. Ayman El Shenawy
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Correspondence to Ayman El Shenawy.

Additional information

Ayman El Shenawy received the Ph. D. degree in systems and computer engineering from Al-Azhar University, Egypt in 2013. He is currently working as a lecturer at Systems and Computers Engineering Department, Faculty of Engineering Al-Azhar University, Egypt. He already developed some breakthrough research in the mentioned areas. He made significant contributions to the stated research fields.

His research interests include artificial intelligent methods, robotics and machine learning.

Khalil Mohamed received the M. Sc. degree in control engineering from Al-Azhar University, Egypt in 2015. He is currently a Ph. D. degree candidate in robotic systems at Systems and Computers Engineering Department, Al-Azhar University, Egypt.

His research interests includes task assignment in multi-robot systems, task decomposition, predictive control and optimal control.

Hany Harb received the B. Sc. degree in computers and control engineering from Faculty of Engineering, Ain Shams University, Egypt in 1978, the M. Sc. degree in computers and systems engineering from Faculty of Engineering, Al-Azhar University, Egypt in 1981. He also received the Ph. D. degree in computer science and the M. Sc. degree in operations research (MSOR) from Institute of Technology (IIT), USA in 1986 and 1987, respectively. He is a professor of software engineering in System Engineering Department, Faculty of Engineering, Al-Azhar University, Egypt.

His research interests include artificial intelligence, cloud computing, and distributed systems.

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El Shenawy, A., Mohamed, K. & Harb, H. HDec-POSMDPs MRS Exploration and Fire Searching Based on IoT Cloud Robotics. Int. J. Autom. Comput. 17, 364–377 (2020). https://doi.org/10.1007/s11633-019-1187-6

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