|Citation:||Y. P. Xu, L. Liu, N. Gu, D. Wang, and Z. H. Peng, “Multi-ASV collision avoidance for point-to-point transitions based on heading-constrained control barrier functions with experiment,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1494–1497, Jun. 2023. doi: 10.1109/JAS.2022.105995|
Z. Peng, J. Wang, D. Wang, and Q. Han, “An overview of recent advances in coordinated control of multiple autonomous surface vehicles,” IEEE Trans. Industrial Informatics, vol. 17, no. 2, pp. 732–745, Feb. 2021.
H. Wei, Q. Sun, J. Chen, and Y. Shi, “Robust distributed model predictive platooning control for heterogeneous autonomous surface vehicles,” Control Engineering Practice, vol. 107, p. 104655, Feb. 2021.
L. Zuo, W. Yan, R. Cui, and J. Gao, “A coverage algorithm for multiple autonomous surface vehicles in flowing environments,” Int. J. Control Automation &Systems, vol. 14, no. 2, pp. 540–548, Apr. 2016.
M. Akdağ, P. Solnor, and T. A. Johansen, “Collaborative collision avoidance for maritime autonomous surface ships: A review,” Ocean Engineering, vol. 250, p. 110920, Apr. 2022.
S. Dai, S. He, M. Wang, and C. Yuan, “Adaptive neural control of underactuated surface vessels with prescribed performance guarantees,” IEEE Trans. Neural Networks and Learning Systems, vol. 30, no. 12, pp. 3686–3698, Dec. 2019. doi: 10.1109/TNNLS.2018.2876685
H. Lyu and Y. Yin, “COLREGS-constrained real-time path planning for autonomous ships using modified artificial potential fields,” J. Navigation, vol. 72, no. 3, pp. 588–608, Nov. 2018.
R. Zhang, P. Tang, Y. Su, X. Li, G. Yang, and C. Shi, “An adaptive obstacle avoidance algorithm for unmanned surface vehicle in complicated marine environments,” IEEE/CAA J. Autom. Sinica, vol. 1, no. 4, pp. 385–396, Oct. 2014. doi: 10.1109/JAS.2014.7004666
X. Ge, Q.-L. Han, J. Wang, and X.-M. Zhang, “A scalable adaptive approach to multi-vehicle formation control with obstacle avoidance,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 990–1004, Jun. 2022. doi: 10.1109/JAS.2021.1004263
J. Yan, X. Li, X. Yang, X. Luo, C. Hua, and X. Guan, “Integrated localization and tracking for AUV with model uncertainties via scalable sampling-based reinforcement learning approach,” IEEE Trans. Systems,Man,and Cybernetics: Systems, vol. 52, no. 11, pp. 6952–6967, 2022. doi: 10.1109/TSMC.2021.3129534
T. Xu, S. Zhang, Z. Jiang, Z. Liu, and H. Cheng, “Collision avoidance of high-speed obstacles for mobile robots via maximum-speed aware velocity obstacle method,” IEEE Access, vol. 8, pp. 138493–138507, Jul. 2020. doi: 10.1109/ACCESS.2020.3012513
Z. Sui, Z. Pu, J. Yi, and S. Wu, “Formation control with collision avoidance through deep reinforcement learning using model-guided demonstration,” IEEE Trans. Neural Networks and Learning Systems, vol. 32, no. 6, pp. 2358–2372, Jun. 2021. doi: 10.1109/TNNLS.2020.3004893
H. Wang, J. Peng, F. Zhang, H. Zhang, and Y. Wang, “High-order control barrier functions-based impedance control of a robotic manipulator with time-varying output constraints,” ISA Transactions, vol. 129, pp. 361–369, 2022. doi: 10.1016/j.isatra.2022.02.013