Issue
Natl Sci Open
Volume 5, Number 1, 2026
Special Topic: Intelligent Materials and Devices
Article Number 20250048
Number of page(s) 16
Section Materials Science
DOI https://doi.org/10.1360/nso/20250048
Published online 08 December 2025
  • Guan X. Network system capacity: Towards integrating sensing, communication and control. Nat Sci Open 2024; 3: 20230036.[Article] [Google Scholar]
  • Susca S, Bullo F, Martinez S. Monitoring environmental boundaries with a robotic sensor network. IEEE Trans Contr Syst Technol 2008; 16: 288–296.[Article] [Google Scholar]
  • Wang G, Liu X, Xiao Y, et al. Extinction chains reveal intermediate phases between the safety and collapse in mutualistic ecosystems. Engineering 2024; 43: 89–98.[Article] [Google Scholar]
  • Wang G, Liu X, Chen G, et al. Indirect effects among biodiversity loss of mutualistic ecosystems. Nat Sci Open 2022; 1: 20220002.[Article] [Google Scholar]
  • Savkin AV, Huang H. Range-based reactive deployment of autonomous drones for optimal coverage in disaster areas. IEEE Trans Syst Man Cybern Syst 2021; 51: 4606–4610.[Article] [Google Scholar]
  • Gonzalez AGC, Alves MVS, Viana GS, et al. Supervisory control-based navigation architecture: A new framework for autonomous robots in industry 4.0 environments. IEEE Trans Ind Inf 2018; 14: 1732–1743.[Article] [Google Scholar]
  • Czimmermann T, Chiurazzi M, Milazzo M, et al. An autonomous robotic platform for manipulation and inspection of metallic surfaces in industry 4.0. IEEE Trans Automat Sci Eng 2022; 19: 1691–1706.[Article] [Google Scholar]
  • Liu B, Zhang HT, Meng H, et al. Scanning-chain formation control for multiple unmanned surface vessels to pass through water channels. IEEE Trans Cybern 2022; 52: 1850–1861.[Article] [Google Scholar]
  • Tang C, Zhang HT, Wang J. Flexible formation tracking control of multiple unmanned surface vessels for navigating through narrow channels with unknown curvatures. IEEE Trans Ind Electron 2023; 70: 2927–2938.[Article] [Google Scholar]
  • Cao H, Hu BB, Mo X, et al. The immense impact of reverse edges on large hierarchical networks. Engineering 2024; 36: 240–249.[Article] [Google Scholar]
  • Low Chang Boon, Wang Danwei. GPS-based tracking control for a car-like wheeled mobile robot with skidding and slipping. IEEE ASME Trans Mechatron 2008; 13: 480–484.[Article] [Google Scholar]
  • Minaeian S, Liu J, Son YJ. Vision-based target detection and localization via a team of cooperative UAV and UGVs. IEEE Trans Syst Man Cybern Syst 2016; 46: 1005–1016.[Article] [Google Scholar]
  • Sun N, Zhao J, Shi Q, et al. Moving target tracking by unmanned aerial vehicle: A survey and taxonomy. IEEE Trans Ind Inf 2024; 20: 7056–7068.[Article] [Google Scholar]
  • Zhang HT, Hu BB, Xu Z, et al. Visual navigation and landing control of an unmanned aerial vehicle on a moving autonomous surface vehicle via adaptive learning. IEEE Trans Neural Netw Learn Syst 2021; 32: 5345–5355.[Article] [Google Scholar]
  • Liu L, Wang D, Peng Z, et al. Bounded neural network control for target tracking of underactuated autonomous surface vehicles in the presence of uncertain target dynamics. IEEE Trans Neural Netw Learn Syst 2019; 30: 1241–1249.[Article] [Google Scholar]
  • Nabavi-Chashmi SY, Asadi D, Ahmadi K. Image-based UAV position and velocity estimation using a monocular camera. Control Eng Pract 2023; 134: 105460.[Article] [Google Scholar]
  • Zhang W, Song K, Rong X, et al. Coarse-to-fine UAV target tracking with deep reinforcement learning. IEEE Trans Automat Sci Eng 2019; 16: 1522–1530.[Article] [Google Scholar]
  • Wang S, Jiang F, Zhang B, et al. Development of UAV-based target tracking and recognition systems. IEEE Trans Intell Transp Syst 2020; 21: 3409–3422.[Article] [Google Scholar]
  • Huang T, Xue Y, Xue Z, et al. USV-tracker: A novel USV tracking system for surface investigation with limited resources. Ocean Eng 2024; 312: 119196.[Article] [Google Scholar]
  • Liu Y, Wang Q, Hu H, et al. A novel real-time moving target tracking and path planning system for a quadrotor UAV in unknown unstructured outdoor scenes. IEEE Trans Syst Man Cybern Syst 2019; 49: 2362–2372.[Article] [Google Scholar]
  • Wipf DP, Rao BD, Nagarajan S. Latent variable Bayesian models for promoting sparsity. IEEE Trans Inform Theor 2011; 57: 6236–6255.[Article] [Google Scholar]
  • Baraniuk RG, Cevher V, Duarte MF, et al. Model-based compressive sensing. IEEE Trans Inform Theor 2010; 56: 1982–2001.[Article] [Google Scholar]
  • Xu Z, He S, Zhou W, et al. Path following control with sideslip reduction for underactuated unmanned surface vehicles. IEEE Trans Ind Electron 2024; 71: 11039–11047.[Article] [Google Scholar]
  • Kou L, Chen Z, Xiang J. Cooperative fencing control of multiple vehicles for a moving target with an unknown velocity. IEEE Trans Automat Contr 2022; 67: 1008–1015.[Article] [Google Scholar]
  • Liu B, Chen Z, Zhang HT, et al. Collective dynamics and control for multiple unmanned surface vessels. IEEE Trans Contr Syst Technol 2020; 28: 2540–2547.[Article] [Google Scholar]
  • Hu BB, Zhang HT, Shi Y. Cooperative label-free moving target fencing for second-order multi-agent systems with rigid formation. Automatica 2023; 148: 110788.[Article] [Google Scholar]
  • Bertsekas DP. The auction algorithm: A distributed relaxation method for the assignment problem. Ann Oper Res 1988; 14: 105–123.[Article] [Google Scholar]
  • Zhou W, Zhang HT, Wang J. An efficient sparse Bayesian learning algorithm based on Gaussian-scale mixtures. IEEE Trans Neural Netw Learn Syst 2022; 33: 3065–3078.[Article] [Google Scholar]
  • Yuille AL, Rangarajan A. The concave-convex procedure (CCCP). In: Proceedings of the 15th International Conference on Neural Information Processing Systems: Natural and Synthetic. Vancouver, 2002. 1033–1040 [Google Scholar]
  • Zhang Z, Rao BD. Extension of SBL algorithms for the recovery of block sparse signals with intra-block correlation. IEEE Trans Signal Process 2013; 61: 2009–2015.[Article] [Google Scholar]
  • Bartle RG, Sherbert DR. Introduction to Real Analysis. New York: Wiley, 2000 [Google Scholar]
  • Shen H, Wen G, Lv Y, et al. USV parameter estimation: Adaptive unscented kalman filter-based approach. IEEE Trans Ind Inf 2023; 19: 7751–7761.[Article] [Google Scholar]
  • Thomas J, Welde J, Loianno G, et al. Autonomous flight for detection, localization, and tracking of moving targets with a small quadrotor. IEEE Robot Autom Lett 2017; 2: 1762–1769.[Article] [Google Scholar]
  • Wu Z, Hu G, Feng L, et al. Collision avoidance for mobile robots based on artificial potential field and obstacle envelope modelling. Assem Autom 2016; 36: 318–332.[Article] [Google Scholar]
  • Abeywickrama HV, Jayawickrama BA, He Y, et al. Potential field based inter-UAV collision avoidance using virtual target relocation. In: Proceedings of the 2018 IEEE 87th Vehicular Technology Conference. Porto, 2018. 1–5 [Google Scholar]
  • Wipf DP, Rao BD. Sparse Bayesian learning for basis selection. IEEE Trans Signal Process 2004; 52: 2153–2164.[Article] [Google Scholar]
  • Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Ser B-Stat Meth 1996; 58: 267–288.[Article] [Google Scholar]

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