Abstract
One of the major respects of the autonomous capability of underwater robots in unknown environment is to be capable of global path planning and obstacles avoiding when encountering abrupt events. For the Spherical Underwater Robot (SUR) to fulfill autonomous task execution, this paper proposed a novel fuzzy control method that incorporates multi-sensor technology to guide underwater robots in unknown environment. To attain the objective, a SUR we designed is used to design the controller. According to its kinematic model, the safety distance was calculated and sensors (US1000-21A) were arranged. The novel fuzzy control method was then explored for robot’s path planning in an unknown environment through simulation. The simulation results demonstrate the capability of the proposed method to guide the robot, and to generate a safe and smooth trajectory in an unknown environment. The effectiveness of the proposed method was further verified through experiments with a SUR in a real platform. The real environment experiments by using the novel fuzzy control method were compared with the basic control method. The experimental results show that in unknown environments, the proposed method improves the execution efficiency and flexibility of the SUR.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Xian Z W, He X F, Lian J X, Hu X P, Zhang L L. A bionic autonomous navigation system by using polarization navigation sensor and stereo camera. Autonomous Robots, 2017, 41, 1107–1118.
Francisco B F, Miquel M C, Pep L N C, Gabriel O C, Joan P B. Inertial sensor self-calibration in a visually-aided navigation approach for a micro-AUV. Sensors, 2015, 15, 1825–1860.
Kularatne D, Bhattacharya S, Hsieh M A. Going with the flow: A graph based approach to optimal path planning in general flows. Autonomous Robots, 2018, 42, 1369–1387.
Zhang Z J, Chen S Y, Li S. Compatible convex-nonconvex constrained QP-based dual neural networks for motion planning of redundant robot manipulators. IEEE Transactions on Control Systems Technology, 2019, 27, 1250–1258.
Ji J, Khajepour A, Melek W W, Huang Y J. Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints. IEEE Transactions on Vehicular Technology, 2017, 66, 952–964.
Han Y Q, Kao Y G, Gao C C. Robust sliding mode control for uncertain discrete singular systems with time-varying delays and external disturbances. Automatica, 2017, 75, 210–216.
Ma Y N, Gong Y J, Xiao C F, Gao Y. Path planning for autonomous underwater vehicles: An ant colony algorithm incorporating alarm pheromone. IEEE Transactions on Vehicular Technology, 2019, 68, 141–154.
Duan Q J, Zhang M J, Zhang J. Local path planning method for AUV based on fuzzy-neural network. Ship Engineering, 2001, 54–58. (in Chinese)
Duan Q J, Zhang M J. Research on real time path planning method for the underwater robot in unknown environment with random shape obstacle. IEEE International Conference on Mechatronics & Automation, ICMA, Luoyang, China, 2006, 757–761.
Chen M J, Lin W, Zeng B. Path planning for robot autonomous map building based on rolling window. Computer Engineering, 2017, 43, 286–292. (in Chinese)
Chen J W, Zhu H C, Zhang L, Sun Y X. Research on fuzzy control of path tracking for underwater vehicle based on genetic algorithm optimization. Ocean Engineering, 2018, 156, 217–223.
Guo J, Li C Y, Guo S X. Study on the path planning of the spherical mobile robot based on fuzzy control. IEEE International Conference on Mechatronics and Automation, ICMA, Tianjin, China, 2019, 1419–1424.
Shi L W, Guo S X, Mao S L, Yue C F, Li M X, Asaka K J. Development of an amphibious turtle-inspired, spherical mother robot. Journal of Bionic Engineering, 2013, 10, 446–455.
Guo S X, He Y L, Shi L W, Pan S W, Xiao R, Tang K, Guo P. Modeling and experimental evaluation of an improved amphibious robot with compact structure. Robot and Computer-Integrated Manufacturing, 2018, 51, 37–52.
Xing H M, Shi L W, Tang K, Guo S X, Hou X H, Liu Y, Liu H K, Hu Y. Robust RGB-D camera and IMU fusion-based cooperative and relative close-range localization for multiple turtle-inspired amphibious spherical robots. Journal of Bionic Engineering, 2019, 16, 442–454.
Guo J, Li C Y, Guo S X. A novel step optimal path planning algorithm for the spherical mobile robot based on fuzzy control. IEEE Access, 2020, 8, 1394–1405.
Gan W Y, Zhu D Q, Sun B, Luo C M. The tracking control of unmanned underwater vehicles based on QPSO-model predictive control. International Conference on Intelligent Robotics and Applications, ICIRA, Wuhan, China, 2017, 711–720.
Salumae T, Chemori A, Kruusmaa M. Motion control of a hovering biomimetic four-fin underwater robot. IEEE Journal of Oceanic Engineering, 2019, 44, 54–71.
Kim H, Lee J. Design, swimming motion planning and implementation of a legged underwater robot (CALEB10: D.BeeBot) by biomimetic approach. Ocean Engineering, 2017, 130, 310–327.
Guo S X, Yang X J, Guo J. Study on horizontal path tracking control method for the spherical amphibious robot. IEEE International Conference on Mechatronics and Automation, ICMA, Tianjin, China, 2019, 1155–1160.
Singh V, Willcox K E. Methodology for path planning with dynamic data-driven flight capability estimation. AIAA Journal, 2017, 55, 2727–2738.
Rao D C, Kabat M R, Das P K, Jena P K. Hybrid IWD-DE: A novel approach to model cooperative navigation planning for multi-robot in unknown dynamic environment. Journal of Bionic Engineering, 2019, 16, 235–252.
Michalopoulou Z H, Abdi A. Detection in an uncertain underwater waveguide. Journal of the Acoustical Society of America, 2017, 142, 2553–2553.
Liu J K. Robot Control System Design and MATLAB Simulation the Advanced Design Method, Tsinghua University Press, Beijing, China, 2017. (in Chinese)
Halin H, Khairunizam W, Ikram K, Haris H, Zunaidi I, Bakar S A, Razlan Z M, Desa H. Design simulation of a fuzzy steering wheel controller for a buggy car. International Conference on Intelligent Informatics & Biomedical Sciences IEEE Computer Society, 2018, 3, 85–89.
Acknowledgement
This work was supported in part by the National Natural Science Foundation of China (Grant No. 61703305), in part by the Key Research Program of the Natural Science Foundation of Tianjin (Grant No. 18JCZDJC38500), and in part by the Innovative Cooperation Project of Tianjin Scientific and Technological (Grant No. 18PTZWHZ00090).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Guo, J., Li, C. & Guo, S. Path Optimization Method for the Spherical Underwater Robot in Unknown Environment. J Bionic Eng 17, 944–958 (2020). https://doi.org/10.1007/s42235-020-0079-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42235-020-0079-3