Navigation Control and Signal Processing Methods for Multiple Autonomous Unmanned Systems
1. Introduction
2. An Overview of the Published Articles
3. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Curtin, T.; Bellingham, J.; Catipovic, J.; Webb, D. Autonomous oceanographic sampling network. Oceanography 1993, 6, 86–94. [Google Scholar] [CrossRef]
- Fallon, M.F.; Papadopoulos, G.; Leonard, J.J.; Patrikalakis, N.M. Cooperative AUV navigation using a single maneuvering surface craft. Int. J. Robot. Res. 2010, 29, 1461–1474. [Google Scholar] [CrossRef]
- Jenkins, S.A.; D’Spain, G. Autonomous underwater glider. In Springer Handbook of Ocean Engineering; Springer: Dordrecht, The Netherlands; Heidelberg, Germany; London, UK; New York, NY, USA, 2016. [Google Scholar]
- Tan, H.P.; Diamant, R.; Seah, W.K.G.; Waldmeyer, M. A survey of techniques and challenges in underwater localization. Ocean Eng. 2011, 38, 1663–1676. [Google Scholar] [CrossRef]
- Wang, X.H.; Shirinzadeh, B.; Ang, M.H. Nonlinear double-integral observer and application to quadrotor aircraft. IEEE Trans. Ind. Electron. 2015, 62, 1189–1200. [Google Scholar] [CrossRef]
- Rogne, R.H.; Bryne, T.H.; Fossen, T.I.; Johansen, T.A. Redundant MEMS-based inertial navigation using nonlinear observers. J. Dyn. Syst. Meas. Control. 2018, 140, 071001. [Google Scholar] [CrossRef]
- Huang, Y.L.; Zhang, Y.G.; Xu, B.; Wu, Z.M.; Chambers, J.A. A new adaptive extended Kalman filter for cooperative localization. IEEE Trans. Aerosp. Electron. Syst. 2018, 54, 353–368. [Google Scholar] [CrossRef]
- Huang, Y.L.; Zhang, Y.G.; Wang, X.D. Kalman-filtering-based inmotion coarse alignment for odometer-aided SINS. IEEE Trans. Instrum. Meas. 2017, 66, 3364–3377. [Google Scholar] [CrossRef]
- Gao, W.; Liu, Y.L.; Xu, B. Robust Huber-based iterated divided difference filtering with application to cooperative localization of autonomous underwater vehicles. Sensors 2014, 14, 24523–24542. [Google Scholar] [CrossRef]
- Yao, Y.Q.; Xu, X.S.; Yang, D.R.; Xu, X. An IMM-UKF aided SINS/USBL calibration solution for underwater vehicles. EEE Trans. Veh. Technol. 2020, 69, 3740–3747. [Google Scholar] [CrossRef]
- Li, J.C.; Gao, W.; Zhang, Y. Gravitational apparent motion-based SINS self-alignment method for underwater vehicles. IEEE Trans. Veh. Technol. 2018, 67, 11402–11410. [Google Scholar] [CrossRef]
- Huang, Y.L.; Zhang, Y.G.; Li, N.; Chambers, J. Robust student’s t based nonlinear filter and smoother. IEEE Trans. IEEE Trans. Aerosp. Electron. Syst. 2016, 52, 2586–2596. [Google Scholar] [CrossRef]
- Webster, S.E.; Eustice, R.M.; Singh, H.; Whitcomb, L.L. Advances in single-beacon one-way-travel-time acoustic navigation for underwater vehicles. Int. J. Robot. Res. 2012, 31, 935–950. [Google Scholar] [CrossRef]
- Bahr, A.; Leonard, J.J.; Fallon, M.F. Cooperative localization for autonomous underwater vehicles. Int. J. Robot. Res. 2009, 28, 714–728. [Google Scholar] [CrossRef]
- Zhang, Y.G.; Huang, Y.L.; Li, N.; Zhao, L. Interpolatory cubature Kalman filters. IET Control Theory Appl. 2015, 9, 1731–1739. [Google Scholar] [CrossRef]
- Huang, Y.; Zhang, Y. Robust student’s t-based stochastic cubature filter for nonlinear systems with heavy-tailed process and measurement noises. IEEE Access 2017, 5, 7964–7974. [Google Scholar] [CrossRef]
- Ruiz, T.; De Raucourt, S.; Petillot, Y.; Lane, D.M. Concurrent mapping and localization using sidescan sonar. IEEE J. IEEE J. Ocean. Eng. 2004, 29, 442–456. [Google Scholar] [CrossRef]
- Huang, Y.L.; Zhang, Y.G.; Wang, X.X.; Zhao, L. Gaussian filter for nonlinear systems with correlated noises at the same epoch. Automatica 2015, 60, 122–126. [Google Scholar] [CrossRef]
- Zhang, Y.G.; Huang, Y.L.; Li, N.; Zhao, L. Embedded cubature Kalman filter with adaptive setting of free parameter. Signal Process. 2015, 114, 112–116. [Google Scholar] [CrossRef]
- Julier, S.; Uhlmann, J.; Whyte, H.F.D. A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Trans. Autom. Control 2000, 45, 477–482. [Google Scholar] [CrossRef]
- Arasaratnam, I.; Haykin, S. Cubature Kalman filters. IEEE Trans. Autom. Control 2009, 54, 1254–1269. [Google Scholar] [CrossRef]
- Huang, H.Q.; Zhou, J.; Zhang, J.; Yang, Y.; Song, R.; Chen, J.; Zhang, J. Attitude estimation fusing quasi-Newton and cubature Kalman filtering for inertial navigation system aided with magnetic sensors. IEEE Access 2018, 6, 28755–28767. [Google Scholar] [CrossRef]
- Huang, H.Q.; Shi, D.R.; Zhou, J.; Yang, Y.; Song, R.; Chen, J.F.; Wu, G.Q.; Zhang, J.J. Attitude determination method integrating square-root cubature Kalman filter with expectation-maximization for inertial navigation system applied to underwater glider. Rev. Sci. Instrum. Rev. Sci. Instrum. 2019, 90, 095001. [Google Scholar] [CrossRef]
- Huang, H.Q.; Chen, X.Y.; Zhou, Z.K.; Liu, H.; Lv, C.P. Study on INS/DR integration navigation system using EKF/RK4 algorithm for underwater gliders. J. Mar. Sci. Technol. 2017, 25, 84–95. [Google Scholar]
- Huang, H.Q.; Chen, X.Y.; Zhang, B.; Wang, J. High accuracy navigation information estimation for inertial system using the multi-model EKF fusing adams explicit formula applied to underwater gliders. ISA Trans. 2017, 66, 414–424. [Google Scholar] [CrossRef] [PubMed]
- Idkhajine, L.; Monmasson, E.; Maalouf, A. Fully FPGA-based sensorless control for synchronous AC drive using an extended Kalman filter. IEEE Trans. Ind. Electron. 2012, 59, 3908–3918. [Google Scholar] [CrossRef]
- Yu, M.J. INS/GPS integration system using adaptive filter for estimating measurement noise variance. IEEE Trans. Aerosp. Electron. Syst. 2012, 48, 1786–1792. [Google Scholar] [CrossRef]
- Fox, C.W.; Roberts, S.J. A tutorial on variational Bayesian inference. Artif. Intell. Rev. 2012, 38, 85–95. [Google Scholar] [CrossRef]
- Li, X.R.; Bar-Shalom, Y. Multiple-model estimation with variable structure. IEEE Trans. Autom. Control 1996, 41, 478–493. [Google Scholar]
- Dong, P.; Jing, Z.L.; Leung, H.; Shen, K. Variational Bayesian adaptive cubature information filter based on Wishart distribution. IEEE Trans. Autom. Control 2017, 62, 6051–6057. [Google Scholar] [CrossRef]
- Kim, J.S.; Yum, D.H.; Lee, P.J.; Hong, S.J.; Kim, J. A hybrid sonar algorithm for submersible cars to verify the location of senders. IEEE Trans. Veh. Technol. 2012, 61, 2707–2714. [Google Scholar] [CrossRef]
- Jagannathan, S.; Galan, G. One-layer neural-network controller with preprocessed inputs for autonomous underwater vehicles. IEEE Trans. Veh. Technol. 2003, 52, 1342–1355. [Google Scholar] [CrossRef]
- Ma, Y.N.; Gong, Y.J.; Xiao, C.F.; Gao, Y.; Zhang, J. Path planning for autonomous underwater vehicles: An ant colony algorithm incorporating alarm pheromone. IEEE Trans. Veh. Technol. 2019, 68, 141–154. [Google Scholar] [CrossRef]
- Cheng, X.Z.; Shu, H.N.; Liang, Q.H.; Du, D.H.C. Silent positioning in underwater acoustic sensor networks. IEEE Trans. Veh. Technol. 2008, 57, 1756–1766. [Google Scholar] [CrossRef]
- Isbitiren, G.; Akan, O.B. Three-dimensional underwater target tracking with acoustic sensor networks. IEEE Trans. Veh. Technol. 2011, 60, 3897–3906. [Google Scholar] [CrossRef]
- Huang, H.Q.; Tang, J.C.; Liu, C.; Zhang, B.; Wang, B. Variational Bayesian-Based Filter for Inaccurate Input in Underwater Navigation. IEEE Trans. Veh. Technol. 2021, 70, 8441–8452. [Google Scholar] [CrossRef]
- Zhao, R.; Xu, J.; Xiang, X. A Review of Path Planning and Cooperative Control for MAUV Systems. Chin. J. Ship Res. 2018, 13, 58–65. [Google Scholar]
- Khalil, H.K. Nonlinear Control; Pearson: New York, NY, USA, 2015. [Google Scholar]
- Jiang, Z.P. Global Tracking Control of Underactuated Ships by Lyapunov’s Direct Method. Automatica 2002, 38, 301–309. [Google Scholar] [CrossRef]
- Aguiar, A.P.; Hespanha, J.P. Trajectory-Tracking and PathFollowing of Underactuated Autonomous Vehicles with Parametric Modeling Uncertainty. IEEE Trans. Autom. Control 2007, 52, 1362–1379. [Google Scholar] [CrossRef]
- Cui, R.; Zhang, X.; Cui, D. Adaptive Sliding-Mode Attitude Control for Autonomous Underwater Vehicles with Input Nonlinearities. Ocean Eng. 2016, 123, 45–54. [Google Scholar] [CrossRef]
- Liu, Y.C.; Liu, S.Y.; Wang, N. Fully-Tuned Fuzzy Neural Network based Robust Adaptive Tracking Control of Unmanned Underwater Vehicle with Thruster Dynamics. Neurocomputing 2016, 196, 1–13. [Google Scholar] [CrossRef]
- Weng, Y.P.; Wang, N. SMC-based model-free tracking control of unknown autonomous surface vehicles. ISA Trans. 2022, 130, 684–691. [Google Scholar] [CrossRef] [PubMed]
- Gao, Z.Y.; Guo, G. Fixed-Time Sliding Mode Formation Control of AUVs Based on a Disturbance Observer. IEEE/CAA J. Autom. Sin. 2020, 7, 539–545. [Google Scholar] [CrossRef]
- Zhang, J.Q.; Yu, S.; Wu, D.F.; Yan, Y. Nonsingular Fixed-Time Terminal Sliding Mode Trajectory Tracking Control for Marine Surface Vessels with Anti-Disturbances. Ocean Eng. 2020, 217, 108158. [Google Scholar] [CrossRef]
- Shi, Y.; Xie, W.; Zhang, G.; Dong, H.; Zhang, W. Event-Triggered Saturation-Tolerant Control for Autonomous Underwater Vehicles with Quantitative Transient Behaviors. IEEE Trans. Veh. Technol. 2023, 72, 9857–9867. [Google Scholar] [CrossRef]
- Wang, N.; Qian, C.; Sun, J.C.; Liu, Y.C. Adaptive Robust FiniteTime Trajectory Tracking Control of Fully Actuated Marine Surface Vehicles. IEEE Trans. Control Syst. Technol. 2016, 24, 1454–1462. [Google Scholar] [CrossRef]
- Van, M.; Ceglarek, D. Robust Fault Tolerant Control of Robot Manipulators with Global Fixed-Time Convergence. J. Frankl. Inst. 2021, 358, 699–722. [Google Scholar] [CrossRef]
- Yang, Y.; Yang, X.; Li, T. A Survey of Autonomous Underwater Vehicle Formation: Performance, Formation Control, and Communication Capability. IEEE Commun. Surv. Tutor. 2021, 23, 815–841. [Google Scholar] [CrossRef]
- Das, B.; Subudhi, B.; Pati, B.B. Cooperative Formation Control of Autonomous Underwater Vehicles: An Overview. Int. J. Autom. Comput. 2016, 13, 199–255. [Google Scholar] [CrossRef]
- Antonelli, G.; Chiaverini, S.; Sarkar, N.; West, M. Adaptive Control of an Autonomous Underwater Vehicle: Experimental Results on ODIN. IEEE Trans. Control Syst. Technol. 2001, 9, 756–765. [Google Scholar] [CrossRef]
- Pettersen, K.Y.; Nijmeijer, H. Underactuated Ship Tracking Control: Theory and Experiments. Int. J. Control 2001, 74, 1435–1446. [Google Scholar] [CrossRef]
- Do, K.D.; Pan, J. Global Tracking Control of Underactuated Ships with Nonzero Off-diagonal Terms in Their System Matrices. Automatica 2005, 41, 87–95. [Google Scholar]
- Asif, M.; Khan, M.J.; Memon, A.Y. Integral Terminal Sliding Mode Formation Control of Non-Holonomic Robots Using Leader Follower Approach. Robotica 2017, 35, 1473–1487. [Google Scholar] [CrossRef]
- Sun, Z.J.; Zhang, G.Q.; Lu, Y.; Zhang, W.D. Leader-follower Formation Control of Underactuated Surface Vehicles Based on Sliding Mode Control and Parameter Estimation. ISA Trans. 2018, 72, 15–24. [Google Scholar] [CrossRef] [PubMed]
- Fossen, T.I. Nonlinear Modeling and Control of Underwater Vehicles; Norwegian Institute of Technology: Trondheim, Norway, 1991. [Google Scholar]
- Paliotta, C.; Lefeber, E.; Pettersen, K.Y.; Pinto, J.; Costa, M.; de Figueiredo Borges de Sousa, J.T. Trajectory Tracking and Path Following for Underactuated Marine Vehicles. IEEE Trans. Control Syst. Technol. 2019, 27, 1423–1437. [Google Scholar] [CrossRef]
- Zuo, Z.Y. Nonsingular Fixed-Time Consensus Tracking for SecondOrder Multi-Agent Networks. Automatica 2015, 54, 305–309. [Google Scholar] [CrossRef]
- Zuo, Z.Y.; Han, Q.L.; Ning, B.D.; Ge, X.H.; Zhang, X.M. An Overview of Recent Advances in Fixed-Time Cooperative Control of Multiagent Systems. IEEE Trans. Ind. Inform. 2018, 14, 2322–2334. [Google Scholar] [CrossRef]
- Gao, Z.Y.; Guo, G. Fixed-time Formation Control of AUVs Based on a Disturbance Observer. ACTA Autom. Sin. 2019, 45, 1094–1102. [Google Scholar]
- Gao, J.Q.; Li, Y.J.; Xu, Y.H.; Lv, S.H. A Two-Objective ILP Model of OP-MATSP for the Multi-Robot Task Assignment in an Intelligent Warehouse. Appl. Sci. 2022, 12, 4843. [Google Scholar] [CrossRef]
- Zhao, Y.X.; Wang, M.Y. The LOS/NLOS Classification Method Based on Deep Learning for the UWB Localization System in Coal Mines. Appl. Sci. 2022, 12, 6484. [Google Scholar] [CrossRef]
- Huang, H.H.; Wei, J.Y. In-Motion Coarse Alignment Method Based on Position Loci and Optimal-REQUEST for SINS. Appl. Sci. 2022, 12, 7113. [Google Scholar] [CrossRef]
- Xu, Z.R.; Wang, M.Y.; Li, Q.M.; Qian, L.F. Fault Diagnosis Method Based on Time Series in Autonomous Unmanned System. Appl. Sci. 2022, 12, 7366. [Google Scholar] [CrossRef]
- Sun, H.Y.; Wu, G.Q.; Wang, X.L.; Zhang, T.; Zhang, P.; Chen, W.; Zhu, Q.H. Research on a Measurement Method for the Ocean Wave Field Based on Stereo Vision. Appl. Sci. 2022, 12, 7447. [Google Scholar] [CrossRef]
- Li, Y.S.; Wang, B.; Chen, Y.Q. A Novel Decoupled Synchronous Control Method for Multiple Autonomous Unmanned Linear Systems: Bounded L2-Gain for Coupling Attenuation. Appl. Sci. 2022, 12, 7551. [Google Scholar] [CrossRef]
- Sun, Y.; Cui, B.B.; Ji, F.; Wei, X.H.; Zhu, Y.Y. The Full-Field Path Tracking of Agricultural Machinery Based on PSO-Enhanced Fuzzy Stanley Model. Appl. Sci. 2022, 12, 7683. [Google Scholar] [CrossRef]
- Zhao, M.J.; Zhang, T.; Wang, D. A Novel UWB Positioning Method Based on a Maximum-Correntropy Unscented Kalman Filter. Appl. Sci. 2022, 12, 12735. [Google Scholar] [CrossRef]
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Huang, H.; Wang, B.; Yang, Y. Navigation Control and Signal Processing Methods for Multiple Autonomous Unmanned Systems. Appl. Sci. 2025, 15, 7335. https://doi.org/10.3390/app15137335
Huang H, Wang B, Yang Y. Navigation Control and Signal Processing Methods for Multiple Autonomous Unmanned Systems. Applied Sciences. 2025; 15(13):7335. https://doi.org/10.3390/app15137335
Chicago/Turabian StyleHuang, Haoqian, Bing Wang, and Yuan Yang. 2025. "Navigation Control and Signal Processing Methods for Multiple Autonomous Unmanned Systems" Applied Sciences 15, no. 13: 7335. https://doi.org/10.3390/app15137335
APA StyleHuang, H., Wang, B., & Yang, Y. (2025). Navigation Control and Signal Processing Methods for Multiple Autonomous Unmanned Systems. Applied Sciences, 15(13), 7335. https://doi.org/10.3390/app15137335