Deadline Missing Prediction for Mobile Robots through the Use of Historical Data
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Deadline Missing Prediction for Mobile Robots through the Use of Historical Data

Authors: Edwaldo R. B. Monteiro, Patricia D. M. Plentz, Edson R. De Pieri

Abstract:

Mobile robotics is gaining an increasingly important role in modern society. Several potentially dangerous or laborious tasks for human are assigned to mobile robots, which are increasingly capable. Many of these tasks need to be performed within a specified period, i.e, meet a deadline. Missing the deadline can result in financial and/or material losses. Mechanisms for predicting the missing of deadlines are fundamental because corrective actions can be taken to avoid or minimize the losses resulting from missing the deadline. In this work we propose a simple but reliable deadline missing prediction mechanism for mobile robots through the use of historical data and we use the Pioneer 3-DX robot for experiments and simulations, one of the most popular robots in academia.

Keywords: Deadline missing, historical data, mobile robots, prediction mechanism.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1092319

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References:


[1] T. Tsubouchi and S. Arimoto, "Behavior of a mobile robot navigated by an ldquo;iterated forecast and planning rdquo; scheme in the presence of multiple moving obstacles,” in Robotics and Automation, 1994. Proceedings., 1994 IEEE International Conference on, 1994, pp. 2470–2475 vol.3.
[2] T. Tsubouchi, A. Hirose, and S. Arimoto, "A navigation scheme with learning for a mobile robot among multiple moving obstacles,” in Intelligent Robots and Systems ’93, IROS ’93. Proceedings of the 1993 IEEE/RSJ International Conference on, vol. 3, 1993, pp. 2234–2240 vol.3.
[3] J. Miura, H. Uozumi, and Y. Shirai, "Mobile robot motion planning considering the motion uncertainty of moving obstacles,” in Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics, 1999, pp. 692–697.
[4] C. Shi, Y. Wang, and J. Yang, "A local obstacle avoidance method for mobile robots in partially known environment,” Robot. Auton. Syst., vol. 58, no. 5, pp. 425–434, May 2010.
[5] Q. Zhu, J. Hu, and L. Henschen, "A new moving target interception algorithm for mobile robots based on sub-goal forecasting and an improved scout ant algorithm,” Applied Soft Computing, vol. 13, no. 1, pp. 539 – 549, 2013.
[6] Y. Mei, Y.-H. Lu, Y. Hu, and C. S. G. Lee, "Energy-efficient motion planning for mobile robots,” in Robotics and Automation, 2004. Proceedings. ICRA ’04. 2004 IEEE International Conference on, vol. 5, 2004, pp. 4344–4349 Vol.5.
[7] M. Jia, G. Zhou, and Z. Chen, "An efficient strategy integrating grid and topological information for robot exploration,” in Robotics, Automation and Mechatronics, 2004 IEEE Conference on, vol. 2, 2004, pp. 667–672 vol.2.
[8] Y. Mei, Y.-H. Lu, C. S. G. Lee, and Y. Hu, "Energy-efficient mobile robot exploration,” in Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on, 2006, pp. 505–511.
[9] Y. Mei, Y.-H. Lu, Y. Hu, and C. S. G. Lee, "A case study of mobile robot’s energy consumption and conservation techniques,” in Advanced Robotics, 2005. ICAR ’05. Proceedings., 12th International Conference on, 2005, pp. 492–497.
[10] W. Zhang, Y.-H. Lu, and J. Hu, "Optimal solutions to a class of power management problems in mobile robots,” Automatica, vol. 45, no. 4, pp. 989 – 996, 2009.
[11] A. Sadrpour, J. Jin, and A. Ulsoy, "Mission energy prediction for unmanned ground vehicles,” in Robotics and Automation (ICRA), 2012 IEEE International Conference on, 2012, pp. 2229–2234.
[12] P. Plentz, "Mecanismos de previs˜ao de perda de deadline para sistemas baseados em threads distribu´ıdas tempo real,” Doutorado, UFSC, Florianop´olis - SC, 2008.
[13] S. Kirtane and J. Martin, "Application performance prediction in autonomic systems,” in Proceedings of the 44th annual Southeast regional conference, ser. ACM-SE 44. New York, NY, USA: ACM, 2006, pp. 566–572.
[14] E. Thereska, M. Abd-El-Malek, J. Wylie, D. Narayanan, and G. Ganger, "Informed data distribution selection in a self-predicting storage system,” in Autonomic Computing, 2006. ICAC ’06. IEEE International Conference on, 2006, pp. 187–198.
[15] C. Tatibana, C. Montez, and R. Oliveira, "Soft real-time task response time prediction in dynamic embedded systems,” in Software Technologies for Embedded and Ubiquitous Systems, ser. Lecture Notes in Computer Science, R. Obermaisser, Y. Nah, P. Puschner, and F. Rammig, Eds. Springer Berlin Heidelberg, 2007, vol. 4761, pp. 273–282.
[16] W. Smith, I. Foster, and V. Taylor, "Predicting application run times with historical information,” J. Parallel Distrib. Comput., vol. 64, no. 9, pp. 1007–1016, Sept. 2004.
[17] L. Welch, A. Stoyenko, and T. Marlowe, "Response time prediction for distributed processes specified in cart-spec,” Control Engineering Practice, vol. 3, no. 5, pp. 651 – 664, 1995.
[18] E.-N. Huh and L. R. Welch, "Adaptive resource management for dynamic distributed real-time applications,” J. Supercomput., vol. 38, no. 2, pp. 127–142, Nov. 2006.