Real-time Trajectory Planning and Tracking Control of Bionic Underwater Robot in Dynamic Environment
), Abstract
In this article, we study the trajectory planning and tracking control of a bionic underwater robot under multiple dynamic obstacles. We first introduce the design of the bionic leopard cabinet underwater robot developed in our lab. Then, we model the trajectory planning problem of the bionic underwater robot by combining its dynamics and physical constraints. Furthermore, we conduct global trajectory planning for bionic underwater robots based on the temporal-spatial Bezier curves. In addition, based on the improved proximal policy optimization, local dynamic obstacle avoidance trajectory replanning is carried out. In addition, we design the fuzzy proportional-integral-derivative controller for tracking control of the planned trajectory. Finally, the effectiveness of the real-time trajectory planning and tracking control method is verified by comparative simulation in dynamic environment and semiphysical simulation of UWSim. Among them, the real-time trajectory planning method has advantages in trajectory length, trajectory smoothness, and planning time. The error of trajectory tracking control method is controlled around 0.2 m.
References
Cong Y, Fan B, Hou D, Fan H, Liu K, Luo J. Novel event analysis for human-machine collaborative underwater exploration. Pattern Recogn. 2019;96(S2):Article 106967.
An R, Guo S, Yu Y, Li C, Awa T. Multiple bio-inspired father–son underwater robot for underwater target object acquisition and identification. Micromachines. 2021;13(1):25.
Xu P, Zheng J, Liu J, Liu X, Wang X, Wang S, Guan T, Fu X, Xu M, Xie G, et al. Deep-learning-assisted underwater 3D tactile tensegrity. Research. 2023;6:0062.
Sfakiotakis M, Lane DM, Davies JBC. Review of fish swimming modes for aquatic locomotion. IEEE J Ocean Eng. 1999;24(1):237–252.
Cao Q, Wang R, Zhang T, Wang Y, Wang S. Hydrodynamic modeling and parameter identification of a bionic underwater vehicle: RobDact. Cyborg Bionic Syst. 2022;2022:Article 9806328.
Zhang S, Qian Y, Liao P, Qin F, Yang J. Design and control of an agile robotic fish with integrative biomimetic mechanisms. IEEE/ASME Trans Mech. 2016;21(4):1846–1857.
Wang S, Wang Y, Wei Q, Tan M, Yu J. A bio-inspired robot with undulatory fins and its control methods. IEEE/ASME Trans Mech. 2017;22(1):206–216.
Wang R, Wang S, Wang Y, Cai M, Tan M. Vision-based autonomous hovering for the biomimetic underwater robot–RobCutt-Ⅱ. IEEE Trans Ind Electron. 2019;66(11):8578–8588.
Cai M, Wang S, Wang Y, Wang R, Tan M. Coordinated control of underwater biomimetic vehicle–manipulator system for free floating autonomous manipulation. IEEE Trans Syst Man Cybern Syst. 2021;51(8):4793–4803.
Sani M, Wang J. Research onpath and trajectory planning of underwater robot based on PID algorithm. Intell Comput Appl. 2020;10(8):206–211.
Wang Y, Wang S, Tan M, Zhou C, Wei Q. Real-time dynamic Dubins-helix method for 3-D trajectory smoothing. IEEE Trans Control Syst Technol. 2015;23(2):730–736.
Li Y, Jiang Y, Zhang G, Chen P. AUV recovery path planning method considering geometrical constraints. Robot. 2015;37:478–485.
Wang R, Jiang T, Bai G, Wang Y, Wang S, Tan M. Stepwise cooperative trajectory planning for multiple BUVs based on temporal–spatial Bezier curves. IEEE Trans Instrum Meas. 2023;72:1–14.
Wang C, Wei L, Wang Z, Song M, Mahmoudian N. Reinforcement learning-based multi-AUV adaptive trajectory planning for under-ice field estimation. Sensors. 2018;18(11):3859–3859.
Hadi B, Khosravi A, Sarhadi P. Deep reinforcement learning for adaptive path planning and control of an autonomous underwater vehicle. Appl Ocean Res. 2022;129(17):Article 103326.
Oh SR, Sun J. Path following of underactuated marine surface vessels using line-of-sight based model predictive control. Ocean Eng. 2010;37(2):289–295.
Chen J, Zhu H, Zhang L, Sun Y. Research on fuzzy control of path tracking for underwater vehicle based on genetic algorithm optimization. Ocean Eng. 2018;156:217–223.
Zhang C, Cheng P, Du B, Dong B, Zhang W. AUV path tracking with real-time obstacle avoidance via reinforcement learning under adaptive constraints. Ocean Eng. 2022;256(7):Article 111453.
Wang N, Gao Y, Zhang X. Data-driven performance-prescribed reinforcement learning control of an unmanned surface vehicle. IEEE Trans NeurNetw Learn Syst. 2021;32(12):5456–5467.
Wang N, Zhang Y, Ahn CK, Xu Q. Autonomous pilot of unmanned surface vehicles: Bridging path planning and tracking. IEEE Trans Veh Technol. 2022;71(3):2358–2374.
Cui R, Yang C, Li Y, Sharma S. Adaptive neural network control of AUVs with control input nonlinearities using reinforcement learning. IEEE Trans Syst Man Cybern Syst. 2017;47(6):1019–1029.
Wang N, Gao Y, Zhao H, Ahn CK. Reinforcement learning-based optimal tracking control of an unknown unmanned surface vehicle. IEEE Trans NeurNetw Learn Syst. 2021;32(7):3034–3045.
Zhang T, Wang R, Wang Y, Cheng L, Wang S, Tan M. Design and locomotion control of a Dactylopteridae-inspired biomimetic underwater vehicle with hybrid propulsion. IEEE Trans Autom Sci Eng. 2022;19(3):2054–2066.
Wang R, Wang S, Wang Y, Tan M, Yu J. A paradigm for path following control of a ribbon-fin propelled biomimetic underwater vehicle. IEEE Trans Syst Man Cybern Syst. 2019;49(3):482–493.
Zhi Q, Mizumoto M. PID type fuzzy controller and parameters adaptive method. Fuzzy Sets Syst. 1996;78(1):23–35.
京公网安备11010802044758号