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Smart Sensing and Control for Autonomous Intelligent Unmanned Systems

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 10 April 2026 | Viewed by 19209

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Special Issue Editors

Prof. Dr. Yabin Gao

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Guest Editor

Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150000, China
Interests: multi-agent systems; path planning and decision; state estimation; intelligent systems
Special Issues, Collections and Topics in MDPI journals

Dr. Yanxu Su

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Guest Editor

School of Artificial Intelligence, Anhui University, Hefei 230601, China
Interests: distributed optimization; distributed MPC

Dr. Xiuyu He

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Guest Editor

School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: distributed parameter systems; intelligent control; vibration control; flexible systems; robotics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Bo Xu

E-Mail Website
Guest Editor

Department of Control Science and Engineering, Harbin Engineering University, Harbin 150001, China
Interests: inertial navigation; cooperative navigation

Special Issue Information

Dear Colleagues,

Autonomous intelligent unmanned systems, such as unmanned (aerial) vehicles, autonomous underwater vehicles, service robots, space robots, marine robots, smart factories, and smart grids, have become a research hotspot in both academia and industry. Currently, methods of smart sensing and control are becoming increasingly important for such systems. Smart sensing supported by intelligent sensors combined with sensor integration and microprocessors can collect, process, and exchange data or information. Intelligent sensors, for example, machine vision sensors, have the virtue of low-cost and high-precision information collection and processing. They provide a certain space for programming automation for diversified functions. Meanwhile, intelligent control produced by the controller or actuator plays a key role in the operation of the unmanned systems with autonomy and intelligence, by using the necessary sensing information. Currently, intelligent sensors are mainly used to solve the control problems of complex systems with the characteristics of uncertain dynamics, high nonlinearity, and multitasking requirements.

This Special Issue focuses on the methodology and technology of smart sensing and intelligent control for autonomous intelligent unmanned systems, as introduced above. It ultimately aims to encourage the development and application of unmanned systems in artificial intelligence. Original research and review papers in this scope are encouraged.

Dr. Yabin Gao
Dr. Yanxu Su
Dr. Xiuyu He
Prof. Dr. Bo Xu
Guest Editors

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Keywords

smart sampling
machine vision
machine learning
distributed filtering
intelligent detection
autonomous decision making
optimal control
cooperation control
game-based control
smart fault tolerant
safety and security

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Published Papers (12 papers)

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Research

25 pages, 6629 KB

Open AccessArticle

A Study of a GNSS/IMU System for Object Localization and Spatial Position Estimation

by Rosen Miletiev, Peter Z. Petkov and Rumen Yordanov

Sensors 2025, 25(22), 6968; https://doi.org/10.3390/s25226968 - 14 Nov 2025

Viewed by 543

Abstract

Today, navigation systems are commonly used in a variety of applications such as autonomous vehicles, image stabilization, object detection and tracking, and virtual reality (VR) or artificial reality (AR) systems. These systems require not only the precise location but also the accurate tracking of the orientation of rigid bodies moving in a three-dimensional (3D) space. This study introduces the integration of GNSS and a 10DoF IMU system to solve the navigation task and calculation of the object position, attitude, and heading. As the location and the attitude calculations require different states but use the same data from the INS sensors, the sensor data fusion in two Kalman filters is proposed. As the filters’ performance is critical, according to the initial states, we study in detail the Allan Variance and normal distribution parameters of three different MEMS IMU sensors. The GNSS system performance and statistics are examined using two commercial and three proposed single or dual-band GNSS antennas. An experimental study is conducted, and the KF output of the heading angle is compared with other sources. Full article

(This article belongs to the Special Issue Smart Sensing and Control for Autonomous Intelligent Unmanned Systems)

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23 pages, 11997 KB

Open AccessArticle

Deep Learning-Driven Automatic Segmentation of Weeds and Crops in UAV Imagery

by Jianghan Tao, Qian Qiao, Jian Song, Shan Sun, Yijia Chen, Qingyang Wu, Yongying Liu, Feng Xue, Hao Wu and Fan Zhao

Sensors 2025, 25(21), 6576; https://doi.org/10.3390/s25216576 - 25 Oct 2025

Viewed by 862

Abstract

Accurate segmentation of crops and weeds is essential for enhancing crop yield, optimizing herbicide usage, and mitigating environmental impacts. Traditional weed management practices, such as manual weeding or broad-spectrum herbicide application, are labor-intensive, environmentally harmful, and economically inefficient. In response, this study introduces a novel precision agriculture framework integrating Unmanned Aerial Vehicle (UAV)-based remote sensing with advanced deep learning techniques, combining Super-Resolution Reconstruction (SRR) and semantic segmentation. This study is the first to integrate UAV-based SRR and semantic segmentation for tobacco fields, systematically evaluate recent Transformer and Mamba-based models alongside traditional CNNs, and release an annotated dataset that not only ensures reproducibility but also provides a resource for the research community to develop and benchmark future models. Initially, SRR enhanced the resolution of low-quality UAV imagery, significantly improving detailed feature extraction. Subsequently, to identify the optimal segmentation model for the proposed framework, semantic segmentation models incorporating CNN, Transformer, and Mamba architectures were used to differentiate crops from weeds. Among evaluated SRR methods, RCAN achieved the optimal reconstruction performance, reaching a Peak Signal-to-Noise Ratio (PSNR) of 24.98 dB and a Structural Similarity Index (SSIM) of 69.48%. In semantic segmentation, the ensemble model integrating Transformer (DPT with DINOv2) and Mamba-based architectures achieved the highest mean Intersection over Union (mIoU) of 90.75%, demonstrating superior robustness across diverse field conditions. Additionally, comprehensive experiments quantified the impact of magnification factors, Gaussian blur, and Gaussian noise, identifying an optimal magnification factor of 4

\times

, proving that the method was robust to common environmental disturbances at optimal parameters. Overall, this research established an efficient, precise framework for crop cultivation management, offering valuable insights for precision agriculture and sustainable farming practices. Full article

(This article belongs to the Special Issue Smart Sensing and Control for Autonomous Intelligent Unmanned Systems)

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29 pages, 7823 KB

Open AccessArticle

Real-Time Detection Sensor for Unmanned Aerial Vehicle Using an Improved YOLOv8s Algorithm

by Fuhao Lu, Chao Zeng, Hangkun Shi, Yanghui Xu and Song Fu

Sensors 2025, 25(19), 6246; https://doi.org/10.3390/s25196246 - 9 Oct 2025

Viewed by 1342

Abstract

This study advances the unmanned aerial vehicle (UAV) localization technology within the framework of a low-altitude economy, with particular emphasis on the accurate and real-time identification and tracking of unauthorized (“black-flying”) drones. Conventional YOLOv8s-based target detection algorithms often suffer from missed detections due to their reliance on single-frame features. To address this limitation, this paper proposes an improved detection algorithm that integrates a long-short-term memory (LSTM) network into the YOLOv8s framework. By incorporating time-series modeling, the LSTM module enables the retention of historical features and dynamic prediction of UAV trajectories. The loss function combines bounding box regression loss with binary cross-entropy and is optimized using the Adam algorithm to enhance training convergence. The training data distribution is validated through Monte Carlo random sampling, which improves the model’s generalization to complex scenes. Simulation results demonstrate that the proposed method significantly enhances UAV detection performance. In addition, when deployed on the RK3588-based embedded system, the method achieves a low false negative rate and exhibits robust detection capabilities, indicating strong potential for practical applications in airspace management and counter-UAV operations. Full article

(This article belongs to the Special Issue Smart Sensing and Control for Autonomous Intelligent Unmanned Systems)

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19 pages, 9302 KB

Open AccessArticle

Real-Time Face Gesture-Based Robot Control Using GhostNet in a Unity Simulation Environment

by Yaseen

Sensors 2025, 25(19), 6090; https://doi.org/10.3390/s25196090 - 2 Oct 2025

Viewed by 928

Abstract

Unlike traditional control systems that rely on physical input devices, facial gesture-based interaction offers a contactless and intuitive method for operating autonomous systems. Recent advances in computer vision and deep learning have enabled the use of facial expressions and movements for command recognition in human–robot interaction. In this work, we propose a lightweight, real-time facial gesture recognition method, GhostNet-BiLSTM-Attention (GBA), which integrates GhostNet and BiLSTM with an attention mechanism, is trained on the FaceGest dataset, and is integrated with a 3D robot simulation in Unity. The system is designed to recognize predefined facial gestures such as head tilts, eye blinks, and mouth movements with high accuracy and low inference latency. Recognized gestures are mapped to specific robot commands and transmitted to a Unity-based simulation environment via socket communication across machines. This framework enables smooth and immersive robot control without the need for conventional controllers or sensors. Real-time evaluation demonstrates the system’s robustness and responsiveness under varied user and lighting conditions, achieving a classification accuracy of 99.13% on the FaceGest dataset. The GBA holds strong potential for applications in assistive robotics, contactless teleoperation, and immersive human–robot interfaces. Full article

(This article belongs to the Special Issue Smart Sensing and Control for Autonomous Intelligent Unmanned Systems)

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23 pages, 4197 KB

Open AccessArticle

Position and Attitude Control of Multi-Modal Underwater Robots Using an Improved LADRC Based on Sliding Mode Control

by Luze Wang, Yu Lu, Lei Zhang, Bowei Cui, Fengluo Chen, Bingchen Liang, Liwei Yu and Shimin Yu

Sensors 2025, 25(19), 6010; https://doi.org/10.3390/s25196010 - 30 Sep 2025

Cited by 1 | Viewed by 896

Abstract

This paper focuses on the control problems of a multi-modal underwater robot, which is designed mainly for the task of detecting the working environment in deep-sea mining. To tackle model uncertainty and external disturbances, an improved linear active disturbance rejection control scheme based [...] Read more.

f h a n

function is introduced into the tracking differentiator (TD). This design retains the system’s fast nonlinear tracking characteristics outside the boundary layer while leveraging linear damping within it to achieve effective overshoot suppression. Secondly, two key enhancements are made to the SMC: an integral sliding surface is designed to improve steady-state accuracy, and a saturation function replaces the sign function to suppress high-frequency chattering. Furthermore, the SMC integrates the total disturbance estimate from the linear extended state observer (LESO) for feedforward compensation. Finally, the simulation experiment verification is completed. The simulation results show that the SM-ADRC scheme significantly improves the dynamic response and disturbance suppression ability of the system and simultaneously suppresses the chattering problem of SMC. Full article

(This article belongs to the Special Issue Smart Sensing and Control for Autonomous Intelligent Unmanned Systems)

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25 pages, 674 KB

Open AccessArticle

Sensor Fault Detection and Reliable Control of Singular Stochastic Systems with Time-Varying Delays

by Yunling Shi, Haosen Yang, Gang Liu, Xiaolin He and Jajun Wang

Sensors 2025, 25(15), 4667; https://doi.org/10.3390/s25154667 - 28 Jul 2025

Cited by 1 | Viewed by 568

Abstract

In unmanned systems, especially in large-scale and complex ones, sensor and communication failures occur from time to time and are hard to avoid. Therefore, this paper studies the fault detection problem of a class of unknown nonlinear singular uncertain time-varying delay Markov jump systems (UNSUTVDMJSs). Firstly, the corresponding sliding mode controller (SMC) is designed by using the equivalent control principle, and the unknown nonlinearity is equivalently replaced by changing the system input. Then, a fault detection filter adapted to this system is designed, thereby obtaining the unknown nonlinear stochastic singular uncertain Augmented filter residual system (UNSSUAFRS) model. To obtain the sufficient conditions for the random admissibility of this augmented system, a weak infinitesimal generator was used to design the required Lyapunov-Krasovskii functional. With the help of the Lyapunov principle and

H_{\infty}

performance analysis method, the sufficient conditions for the random admissibility of UNSSUAFRS under the

H_{\infty}

performance index

γ

were derived. Finally, with the aid of the designed residual evaluation function and threshold, simulation analysis was conducted on the examples of DC servo motors and numerical calculation examples to verify the effectiveness and practicability of this fault detection filter. Full article

(This article belongs to the Special Issue Smart Sensing and Control for Autonomous Intelligent Unmanned Systems)

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33 pages, 4824 KB

Open AccessArticle

Risk Assessment of Hydrogen-Powered Aircraft: An Integrated HAZOP and Fuzzy Dynamic Bayesian Network Framework

by Xiangjun Dang, Yongxuan Shao, Haoming Liu, Zhe Yang, Mingwen Zhong, Huimin Zhao and Wu Deng

Sensors 2025, 25(10), 3075; https://doi.org/10.3390/s25103075 - 13 May 2025

Cited by 5 | Viewed by 1598

Abstract

To advance the hydrogen energy-driven low-altitude aviation sector, it is imperative to establish sophisticated risk assessment frameworks tailored for hydrogen-powered aircraft. Such methodologies will deliver fundamental guidelines for the preliminary design phase of onboard hydrogen systems by leveraging rigorous risk quantification and scenario-based analytical models to ensure operational safety and regulatory compliance. In this context, this study proposes a comprehensive hazard and operability analysis-fuzzy dynamic Bayesian network (HAZOP-FDBN) framework, which quantifies risk without relying on historical data. This framework systematically maps the risk factor relationships identified in HAZOP results into a dynamic Bayesian network (DBN) graphical structure, showcasing the risk propagation paths between subsystems. Expert knowledge is processed using a similarity aggregation method to generate fuzzy probabilities, which are then integrated into the FDBN model to construct a risk factor relationship network. A case study on low-altitude aircraft hydrogen storage systems demonstrates the framework’s ability to (1) visualize time-dependent failure propagation mechanisms through bidirectional probabilistic reasoning, and (2) quantify likelihood distributions of system-level risks triggered by component failures. Results validate the predictive capability of the model in capturing emergent risk patterns arising from subsystem interactions under low-altitude operational constraints, thereby providing critical support for safety design optimization in the absence of historical failure data. Full article

(This article belongs to the Special Issue Smart Sensing and Control for Autonomous Intelligent Unmanned Systems)

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17 pages, 8704 KB

Open AccessArticle

Event-Trigger Reinforcement Learning-Based Coordinate Control of Modular Unmanned System via Nonzero-Sum Game

by Yebao Liu, Tianjiao An, Jianguo Chen, Luyang Zhong and Yuhan Qian

Sensors 2025, 25(2), 314; https://doi.org/10.3390/s25020314 - 7 Jan 2025

Cited by 1 | Viewed by 1388

Abstract

Decreasing the position error and control torque is important for the coordinate control of a modular unmanned system with less communication burden between the sensor and the actuator. Therefore, this paper proposes event-trigger reinforcement learning (ETRL)-based coordinate control of a modular unmanned system (MUS) via the nonzero-sum game (NZSG) strategy. The dynamic model of the MUS is established via joint torque feedback (JTF) technology. Based on the NZSG strategy, the existing coordinate control problem is transformed into an RL issue. With the help of the ET mechanism, the periodic communication mechanism of the system is avoided. The ET-critic neural network (NN) is used to approximate the performance index function, thus obtaining the ETRL coordinate control policy. The stability of the closed-loop system is verified via Lyapunov’s theorem. Experiment results demonstrate the validity of the proposed method. The experimental results show that the proposed method reduces the position error by 30% and control torque by 10% compared with the existing control methods. Full article

(This article belongs to the Special Issue Smart Sensing and Control for Autonomous Intelligent Unmanned Systems)

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18 pages, 5931 KB

Open AccessArticle

Disturbance Estimation and Predefined-Time Control Approach to Formation of Multi-Spacecraft Systems

by Zhicheng Zhang, Weimin Bao, Qimin Hou, Yinhao Ju and Yabin Gao

Sensors 2024, 24(17), 5671; https://doi.org/10.3390/s24175671 - 31 Aug 2024

Cited by 3 | Viewed by 2059

Abstract

Accurate sensing and control are important for high-performance formation control of spacecraft systems. This paper presents a strategy of disturbance estimation and distributed predefined-time control for the formation of multi-spacecraft systems with uncertainties based on a disturbance observer. The process begins by formulating a kinematics model for the relative motion of spacecraft, with the formation’s communication topology represented by a directed graph for the formation system of the spacecraft. A disturbance observer is then developed to estimate the disturbances, and the estimation errors can be convergent in fixed time. Following this, a disturbance-estimation-based sliding mode control is proposed to guarantee the predefined-time convergence of the multi-spacecraft formation system, regardless of initial conditions. It allows each spacecraft to reach its desired position within a set time frame. The results of the analysis of the multi-spacecraft formation system are also provided. Finally, an example simulation of a five-spacecraft formation flying system is provided to demonstrate the presented formation control method. Full article

(This article belongs to the Special Issue Smart Sensing and Control for Autonomous Intelligent Unmanned Systems)

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18 pages, 3621 KB

Open AccessArticle

Enhanced Predefined-Time Control for Spacecraft Attitude Tracking: A Dynamic Predictive Approach

by Jinhe Yang, Tongjian Guo, Yi Yu, Quanliang Dong and Yifan Jia

Sensors 2024, 24(16), 5127; https://doi.org/10.3390/s24165127 - 8 Aug 2024

Cited by 2 | Viewed by 2090

Abstract

This study presents a predefined-time control strategy for rigid spacecraft, employing dynamic predictive techniques to achieve robust and precise attitude tracking within predefined time constraints. Advanced predictive algorithms are used to effectively mitigate system uncertainties and environmental disturbances. The main contributions of this work are introducing adaptive global optimization for period updates, which relaxes the original restrictive conditions; ensuring easier parameter adjustments in predefined-time control, providing a nonconservative upper bound on system stability; and developing a continuous, robust control law through terminal sliding mode control and predictive methods. Extensive simulations confirm the control scheme reduces attitude tracking errors to less than 0.01 degrees at steady state, demonstrating the effectiveness of the proposed control strategy. Full article

(This article belongs to the Special Issue Smart Sensing and Control for Autonomous Intelligent Unmanned Systems)

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16 pages, 3430 KB

Open AccessArticle

Environmental-Driven Approach towards Level 5 Self-Driving

by Mohammad Hurair, Jaeil Ju and Junghee Han

Sensors 2024, 24(2), 485; https://doi.org/10.3390/s24020485 - 12 Jan 2024

Cited by 3 | Viewed by 2544

Abstract

As technology advances in almost all areas of life, many companies and researchers are working to develop fully autonomous vehicles. Such level 5 autonomous driving, unlike levels 0 to 4, is a driverless vehicle stage and so the leap from level 4 to level 5 autonomous driving requires much more research and experimentation. For autonomous vehicles to safely drive in complex environments, autonomous cars should ensure end-to-end delay deadlines of sensor systems and car-controlling algorithms including machine learning modules, which are known to be very computationally intensive. To address this issue, we propose a new framework, i.e., an environment-driven approach for autonomous cars. Specifically, we identify environmental factors that we cannot control at all, and controllable internal factors such as sensing frequency, image resolution, prediction rate, car speed, and so on. Then, we design an admission control module that allows us to control internal factors such as image resolution and detection period to determine whether given parameters are acceptable or not for supporting end-to-end deadlines in the current environmental scenario while maintaining the accuracy of autonomous driving. The proposed framework has been verified with an RC car and a simulator. Full article

(This article belongs to the Special Issue Smart Sensing and Control for Autonomous Intelligent Unmanned Systems)

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19 pages, 3729 KB

Open AccessArticle

Optimal-Damage-Effectiveness Cooperative-Control Strategy for the Pursuit–Evasion Problem with Multiple Guided Missiles

by Xiang Ma, Keren Dai, Man Li, Hang Yu, Weichen Shang, Libo Ding, He Zhang and Xiaofeng Wang

Sensors 2022, 22(23), 9342; https://doi.org/10.3390/s22239342 - 30 Nov 2022

Cited by 3 | Viewed by 2590

Abstract

In this paper, an optimal-damage-effectiveness cooperative-control strategy based on a damage-efficiency model and a virtual-force method is proposed to solve the pursuit–evasion problem with multiple guided missiles. Firstly, different from the overly ideal assumption in the traditional pursuit–evasion problem, an optimization problem that maximizes the damage efficiency is established and solved, making the optimal-damage-effectiveness strategy more meaningful for practical applications. Secondly, a modified virtual-force method is proposed to obtain this optimal-damage-effectiveness control strategy, which solves the numerical solution challenges brought by the high-complexity damage function. Thirdly, adaptive gain is designed in this strategy based on guidance-integrated fuze technology to achieve robust maximum damage efficiency in unpredictable interception conditions. Finally, the effectiveness and robustness of the proposed strategy are verified by numerical simulations. Full article

(This article belongs to the Special Issue Smart Sensing and Control for Autonomous Intelligent Unmanned Systems)

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