Obstacles Avoiding Method for Electromagnetic Actuated Robotic Fish
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摘要: 机器鱼工作的水下环境往往是复杂且无法准确预知的, 因此在遇到障碍物时, 能够及时躲避转弯尤为重要。文中提出了一种利用模糊控制实现以电磁驱动器作为动力源的机器鱼转弯避障控制方法, 并在FLUENT软件中使用用户自定义函数和动网格技术进行了转弯仿真, 结合安装红外距离传感器, 在Matlab中设置模糊控制器输入输出隶度函数, 并归纳出模糊控制的规则。最后搭建实验平台进行机器鱼水下转弯避障游动实验, 实验结果表明, 模糊控制结合红外距离传感器可以实现电磁驱动机器鱼在二维平面游动时的有效避障。Abstract: A fuzzy obstacle avoidance control method for the robotic fish actuated by electromagnetic actuator was proposed. And the software FLUENT was employed to perform turning simulation by using its user define function(UDF) and the dynamic mesh technology. Infrared distance sensor was used. The input and output membership functions of the fuzzy controller were set in Matlab, and the rules of fuzzy control were summarized. Furthermore, an experimental platform was built to conduct underwater turning for obstacle avoidance experiment of the robotic fish. Experimental results show that the fuzzy control combined with infrared distance sensor can realize obstacle avoidance effectively when the elec-tromagnetic actuated robot fish swims in a two-dimensional plane.
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Key words:
- robotic fish /
- electromagnetic actuating /
- turning for obstacle avoidance /
- fuzzy control
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[1] Shang L, Wang S, Tan M, et al. Motion Control for an Underwater Robotic Fish with Two Undulating Long-fins [C]//Proceedings of the 48th IEEE Conference on Decision and Control (CDC) Held Jointly with 2009 28th Chi- nese Control Conference. Shanghai, China: IEEE, 2009. [2] 钟宏伟. 国外无人水下航行器装备与技术现状及展望[J]. 水下无人系统学报, 2017, 25(4): 215-225.Zhong Hong-wei. Review and Prospect of Equipment and Techniques for Unmanned Undersea Vehicle in Foreign Countries[J]. Journal of Unmanned Undersea Systems, 2017, 25(4): 215-225. [3] Phuc N D, Truongthinh N. A Solution of Obstacle Collision Avoidance for Robotic Fish Based on Fuzzy Systems[C]//IEEE International Conference on Robotics & Biomimetics. Phuket, Thailand: IEEE, 2012. [4] Lee P J, Wang W J. Robotic Fish Kinectics Design Based on a Fuzzy Control[M]//Latest Advances in Robot Kinematics. Springer: Dordrecht, 2012: 67-74. [5] Zhang Q, Chen D, Chen T. An Obstacle Avoidance Method of Soccer Robot Based on Evolutionary Artificial Potential Field[J]. Energy Procedia, 2012, 16(5): 1792-1798. [6] 金久才. 无人水下自主航行器(AUV)避碰研究[D]. 呼和浩特: 内蒙古大学, 2008. [7] Huang Z, Zhu D, Bing S. A Multi-AUV Cooperative Hunting Method in 3-D Underwater Environment with Obstacle[J]. Engineering Applications of Artificial Intelligence, 2016, 50: 192-200. [8] Braginsky B, Guterman H. Obstacle Avoidance Approaches for Autonomous Underwater Vehicle: Simulation and Experimental Results[J]. IEEE Journal of Oceanic Engineering, 2016, 41(4): 882-892. [9] Sepulveda C A, Donley J M, Konstantinidis P, et al. Convergent Evolution in Mechanical Design of Lamnid Sha- rks and Tunas[J]. Nature, 2004, 429(6987): 61-65. [10] Stavridis S, Papageorgiou D, Doulgeri Z. Dynamical System Based Robotic Motion Generation with Obstacle Avoidance[J]. IEEE Robotics and Automation Letters, 2017, 2(2): 712-718. [11] Guan Z, Nong G, Gao W, et al. 3D Hydrodynamic Analysis of a Biomimetic Robot Fish[C]//International Conference on Control Automation Robotics & Vision. Singapore: IEEE, 2011. [12] Zhao Z Y, Hong Z H, Deng Y S. Angle Measurement of Robotic Fish Based on Kalman Filter[J]. Applied Mechanics & Materials, 2014, 568-570: 1049-1053. [13] Xia X, Li T. A Fuzzy Control Model Based on BP Neural Network Arithmetic for Optimal Control of Smart City Facilities[J]. Personal and Ubiquitous Computing, 2019, 23(3-4): 453-463. [14] Vafamand N, Khooban M H, Dragicevic T, et al. Robust Non-fragile Fuzzy Control of Uncertain DC Microgrids Feeding Constant Power Loads[J]. IEEE Transactions on Power Electronics, 2019, 34(11): 11300-11308. [15] Liu W, Xin Z, Chen Z. Fuzzy Rule Optimization and Reduction Based on Recursive Neural Networks[C]// International Conference on Control & Automation. Xiamen, China: IEEE, 2019.
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