تعداد نشریات | 27 |
تعداد شمارهها | 557 |
تعداد مقالات | 5,770 |
تعداد مشاهده مقاله | 8,024,639 |
تعداد دریافت فایل اصل مقاله | 5,393,933 |
Robot Action Space of Tractable Subsumption Architecture | ||
International Journal of Industrial Electronics Control and Optimization | ||
مقاله 4، دوره 2، شماره 4، دی 2019، صفحه 297-304 اصل مقاله (1.72 MB) | ||
نوع مقاله: Research Articles | ||
شناسه دیجیتال (DOI): 10.22111/ieco.2019.26197.1068 | ||
نویسنده | ||
farnaz sabahi ![]() | ||
Electrical and Computer Engineering Department, Urmia University | ||
چکیده | ||
Abstract—In this article, a new hybrid feedback system is introduced, which integrates the behavior- based planning by reactive agent-based control scheme through subsumption architecture. At first, subsumption protocol studies the interactions of robot with its environment which cover problems including translating of agent action into an outcome, interactions with the environment, and cooperative actions. Second considers deliberative behavior given the prevailing protocol. It determines the best and quickest response for each agent and tunes the actions based on an objective function obtained by a leader agent. More specifically, tasks are arranged as a hierarchy, where the high-level task is obstacle avoidance. Conflicting lower level tasks are removed by the leader agent decisions. Indeed, the leader agent can adjust the priority of all action to provide an optimal behavior. In other words, our new agent-based method optimizes the subsumption architecture by producing an approximating objective function that made not only behaviors but also optimization done in incremental procedure. We also define an emergency avoidance factor that made higher speed still stable and better interaction of robot in the presence of obstacles. For obstacles avoidance, the leader agent projects a plane to investigate the space ahead and continues. Finally, the leader agent makes a basic stand by task sharing behaviors in decentralized manner using subsumption architecture to draw an optimal path. Simulation results show that although the proposed apporach has little knowledge about the unexpected and adhoc situation in the robot’s environment, it is able to provide suitable performance. | ||
کلیدواژهها | ||
Agent؛ Obstacle Avoidance؛ Robot؛ Subsumption | ||
مراجع | ||
[1] Y. Zuo, Y. Wu, G. Min, and L. Cui, "Learning-based network path planning for traffic engineering," Future Generation Computer Systems, vol. 92, pp. 59-67, 2019. [2] E. Oland, T. S. Andersen, and R. Kristiansen, "Subsumption architecture applied to flight control using composite rotations," Automatica, vol. 69, pp. 195-200, 2016. [3] M. Nazarahari, E. Khanmirza, and S. Doostie, "Multi-objective multi-robot path planning in continuous environment using an enhanced genetic algorithm," Expert Systems with Applications, vol. 115, pp. 106-120, 2019. [4] U. Orozco-Rosas, O. Montiel, and R. Sepúlveda, "Mobile robot path planning using membrane evolutionary artificial potential field," Applied Soft Computing, vol. 77, pp. 236-251, 2019. [5] G. Schouten and J. Steckel, "A Biomimetic Radar System for Autonomous Navigation," IEEE Transactions on Robotics, pp. 1-10, 2019. [6] R. Brooks, "Intelligence Without Reason," in Proceedings of 12th Int.Joint Conf. on Artificial Intelligence, , Sydney, Australia,, 1991, pp. 569–595. [7] J. A. Meyer and S. W. Wilson, "Towards a Theory of Emergent Functionality," in From Animals to Animats:Proceedings of the First International Conference on Simulation of Adaptive Behavior, ed: MIT Press, 1991, pp. 451-461. [8] N. R. Jennings and S. Bussmann, "Agent-based control systems: Why are they suited to engineering complex systems?," IEEE Control Systems, vol. 23, pp. 61-73, 2003. [9] J. Simpson, C. L. Jacobsen, and M. C. Jadud, "Mobile Robot Control The Subsumption Architecture and occam-pi " in Communicating Process Architectures, IOS Press, 2006. [10] R. Brooks, "A robust layered control system for a mobile robot," IEEE Journal on Robotics and Automation, vol. 2, pp. 14-23, 1986. [11] P. Bosetti, M. D. Lio, and A. Saroldi, "On Curve Negotiation: From Driver Support to Automation," IEEE Transactions on Intelligent Transportation Systems, vol. 16, pp. 2082-2093, 2015. [12] J. E. Arnold, "Experiences with the subsumption architecture," in Artificial Intelligence Applications, 1989. Proceedings., Fifth Conference on, 1989, pp. 93-100. [13] H. Nakashima and I. Noda, "Dynamic subsumption architecture for programming intelligent agents," in Multi Agent Systems, 1998. Proceedings. International Conference on, 1998, pp. 190-197. [14] Y. Yongjie, Z. Qidan, and C. Chengtao, "Hybrid Control Architecture of Mobile Robot Based on Subsumption Architecture," in Mechatronics and Automation, Proceedings of the 2006 IEEE International Conference on, 2006, pp. 2168-2172. [15] C. Tan Tiong and M. N. Mahyuddin, "Implementation of behaviour-based mobile robot for obstacle avoidance using a single ultrasonic sensor," in Innovative Technologies in Intelligent Systems and Industrial Applications, 2009. CITISIA 2009, 2009, pp. 244-248. [16] E. Dehghan and G. Mitchell, "Cooperative Multi-Agent Robot Soccer Team," ed. Albuquerque, NM: NASA ACE Center, Univ. New Mexico,. 1999. [17] M. Qingchun, Z. Xiaodong, Z. Changjin, X. Jianshe, W. Yulin, W. Tao, et al., "Game strategy based on fuzzy logic for soccer robots," in Systems, Man, and Cybernetics, 2000IEEE International Conference on, 2000, pp. 3758-3763 vol.5. [18] P. Vadakkepat, M. Ooi Chia, P. Xiao, and L. Tong Heng, "Fuzzy behavior-based control of mobile robots," IEEE Transactions on Fuzzy Systems, vol. 12, pp. 559-565, 2004. [19] M. Colledanchise and P. Ögren, "How Behavior Trees Modularize Hybrid Control Systems and Generalize Sequential Behavior Compositions, the Subsumption Architecture, and Decision Trees," IEEE Transactions on Robotics, vol. 33, pp. 372-389, 2017. [20] D. Windridge, M. Felsberg, and A. Shaukat, "A Framework for Hierarchical Perception–Action Learning Utilizing Fuzzy Reasoning," IEEE Transactions on Cybernetics, vol. 43, pp. 155-169, 2013. [21] S. Russell and P. Norvig Artificial Intelligence: A Modern Approach (3rd Edition): Pearson, 2009. | ||
آمار تعداد مشاهده مقاله: 434 تعداد دریافت فایل اصل مقاله: 193 |