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Advances in Perception, Communications, and Control for Drones: 2nd Edition

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A Special Issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: closed (16 August 2026) | Viewed by 22261

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Editors

Dr. Shihao Yan

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Guest Editor

School of Science, Edith Cowan University, Joondalup, WA 6027, Australia
Interests: UAV-aided communications; covert communications; covert sensing; location spoofing detection; physical layer security; IRS-aided wireless communications
Special Issues, Collections and Topics in MDPI journals

Dr. Zhihong Liu

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Guest Editor

College of Intelligence and Technology, National University of Defense Technology, Changsha 410073, China
Interests: multi-UAV systems; UAV swarms; cooperative decision and control
Special Issues, Collections and Topics in MDPI journals

Dr. Yirui Cong

E-Mail Website
Guest Editor

College of Intelligence and Technology, National University of Defense Technology, Changsha 410073, China
Interests: UAV; control theory; communication theory; filtering theory
Special Issues, Collections and Topics in MDPI journals

Dr. Kehao Wang

E-Mail Website
Guest Editor

School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
Interests: stochastic optimization; operation research; scheduling; wireless network communications; embedded operating system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue of Drones on “Advances in Perception, Communications, and Control for Drones: 2nd Edition”.

In recent years, the field of drones has witnessed significant advancements and has broad applications in various industries, including agriculture, delivery services, surveillance, and entertainment. Perception, communications, and control capabilities are the key aspects of autonomy for drones, which enable them to operate tasks in an efficient and intelligent manner without human intervention. Inevitably, drones will also be placed in more challenging conditions and show their great potential in the future, such as observing and understanding complex environments by their sensors on-board, operating path planning and navigation under their perception conditions, multiple or swarms of drones working in a cooperative mode under communication constraints, etc. Quite a few perception, communications, and control problems are still far from being completely solved. We believe recent advancements in this topic could bring a revolution to their capabilities and applications, opening up new possibilities for safer, more efficient, and intelligent operation.

The Special Issue solicits key theoretical and practical contributions to perception, communications, and control for drones, aiming to showcase the latest developments and cutting-edge research in this fast-evolving field.

This Special Issue will welcome manuscripts that link (but not limited to) the following themes:

Advanced perception techniques of object detection and tracking for drones;
Drones remote sensing for mapping and surveying;
Real-time collision detection and avoidance for drones;
Perception-aware target tracking of drones;
Path planning and navigation of drones;
Cooperative control of multiple drones;
Coupling mechanism between control and communication of drones;
Control theory under communication constraint of drones;
Efficient communications for drone swarms;
Robust formation control algorithms of drones;
Communication-oriented control optimization of drones;
Robust or adaptive control design for drones.

We look forward to receiving your original research articles and reviews.

Dr. Shihao Yan
Dr. Zhihong Liu
Dr. Yirui Cong
Dr. Kehao Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

perception
drone communications
autonomous control
communication-oriented control
perception-aware control
drone swarms

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Related Special Issue

Advances in Perception, Communications, and Control for Drones in Drones (12 articles)

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31 pages, 12170 KB

Open AccessArticle

Environmental Perception via Propagation-Equivalent Reconstruction from Opportunistic Cellular Signals

by Zhiang Bian, Hu Lu, Cheng Zhang, Zhisen Wang, Hongcheng Li, Xin He, Liangdong Wang and Fan Hu

Drones 2026, 10(9), 702; https://doi.org/10.3390/drones10090702 - 15 Sep 2026

Abstract

Low-altitude unmanned aerial vehicles (UAVs) need environmental evidence for perception and navigation, yet dedicated onboard sensing and prior maps may be unavailable. Existing cellular infrastructure offers persistent signals of opportunity, but a passive UAV observes only scalar RSRP, which conflates transmit power, sector response, and environmental loss. We formulate UAV environmental perception as passive, protocol-assisted sensing using non-cooperative commercial cellular downlinks and propose a protocol-anchored reconstruction of a propagation-equivalent virtual radio environment map (vREM). Decodable nominal reference power and path-loss normalization set the power scale, coarse site bearings constrain antenna directions, and multi-altitude UAV trajectories excite height dependence. A hard height-class Beer–Lambert model recovers occupancy support and a height proxy without building geometry at inference. In a controlled multi-altitude simulation based on Xi’an Bell Tower geometry, the method achieved a held-out

{RMSE}_{RSRP}

5.24

dB, compared with

15.70

dB for NoProtocol; all metrics improved across 200 paired repetitions. A separate ground-based field collection yielded 1518 observations from 82 LTE/NR sources at 41 sites and reduced relative-power mapping RMSE from

7.27

3.41

dB. The receiver uses public broadcast fields locally, while the commercial transmitters remain outside the sensing and inversion loop. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones: 2nd Edition)

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30 pages, 4079 KB

Open AccessArticle

Non-Stationary THz UAV Air-to-Ground Propagation MIMO Channel Modeling with Sensing-Communication Shared Clusters and Adaptive Sensing-Assisted Transmission

by Zican Jiang, Yongjun Li, Qin Tian, Kai Zhang, Yu Li and Jianguo Liu

Drones 2026, 10(9), 697; https://doi.org/10.3390/drones10090697 - 14 Sep 2026

Abstract

Terahertz (THz) integrated sensing and communication (ISAC) is a pivotal paradigm for enabling high-rate, ultra-reliable air-to-ground (A2G) connectivity in sixth-generation (6G) unmanned aerial vehicle (UAV) networks. From a physical propagation and environment-aware transmission perspective, current THz channel models inadequately characterize dynamic obstacle scattering, neglect sensing-communication shared clusters, and lack closed-loop frameworks leveraging sensing to assist communication. To bridge these gaps, this paper proposes a novel 3D non-stationary geometry-based stochastic model (GBSM) for THz UAV A2G MIMO channels operating at 300 GHz. The model explicitly incorporates obstacle-induced scattering with Radar Cross Section (RCS)-dependent properties, employing single-point models for small obstacles and multi-point models for large obstacles to capture multipath structures, Doppler effects, molecular absorption, and dynamic cluster evolution. Furthermore, we develop a shared cluster identification framework utilizing delay-angle similarity metrics combined with the Hungarian algorithm to match background scatterers with target-induced paths. Building upon this physical model, a closed-loop sensing-assisted adaptive transmission scheme is established, integrating obstacle-trajectory-driven Kalman channel prediction, affected subchannel avoidance, and waterfilling power allocation. Simulation results demonstrate that the proposed framework accurately characterizes physical THz propagation environments and significantly improves system capacity and reliability, demonstrating the efficacy of environment-level sensing-assisted communication in dynamic THz UAV scenarios. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones: 2nd Edition)

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34 pages, 10328 KB

Open AccessArticle

Edge-Deployed Anomaly Indicators for UAV Telemetry with Context-Aware Advisory Decision Support

by Mustafa Fenerci, Seda Arık Hatipoğlu and Mehmet Konar

Drones 2026, 10(9), 657; https://doi.org/10.3390/drones10090657 - 28 Aug 2026

Viewed by 277

Abstract

This paper presents a companion-computer chain that converts Pixhawk-class MAVLink telemetry into operator advice on an NVIDIA Jetson Nano. A twelve-channel sliding window (

L = 50

, 10 s at 5 Hz) feeds a reference long short-term memory autoencoder and an edge-evaluated [...] Read more.

This paper presents a companion-computer chain that converts Pixhawk-class MAVLink telemetry into operator advice on an NVIDIA Jetson Nano. A twelve-channel sliding window (

L = 50

, 10 s at 5 Hz) feeds a reference long short-term memory autoencoder and an edge-evaluated dense TensorFlow Lite model, which were trained with a session-level split of eight field sorties (train 14,429/validation 7281/test 6976 windows). Reconstruction mean squared error is mapped by validation-calibrated thresholds and a safe-inaction-first decision engine with

K = 3

action persistence. On a per-sortie fault-injection benchmark, One-Class SVM attains the highest pooled F1 score (0.713); the edge-evaluated dense TensorFlow Lite model reaches F1 0.524 (detector false-alarm rate 5.8%) at a 1.57 ms median on-device cost. Decision replay reports a 0.68% escalation false-alarm rate with the on-board gyro-x plausibility gate enabled, and 2.72% with the gate off. The study is a replay-profiled prototype on a single hexacopter; labelled evaluation uses synthetic injection, not concurrent live-load flight scoring. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones: 2nd Edition)

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24 pages, 9486 KB

Open AccessArticle

Robust Quadrotor Trajectory Tracking Under Multimodal Wind Disturbances via Residual-Aware Deep Reinforcement Learning

by Kunpeng Qi, Chunhong Liu and Zhihong Liu

Drones 2026, 10(8), 575; https://doi.org/10.3390/drones10080575 - 27 Jul 2026

Viewed by 372

Abstract

Robust quadrotor trajectory tracking under wind disturbances is challenging because real outdoor wind is multimodal with time-varying and heavy-tailed characteristics, whereas existing solutions suffer from insufficient disturbance observability and poor out-of-distribution robustness. To this end, this paper presents a robust quadrotor trajectory-tracking method based on residual-aware deep reinforcement learning for multimodal wind disturbances. The proposed method augments a standard tracking policy with a compact online acceleration-residual feature, which provides disturbance-related information without requiring an explicit wind sensor, a full disturbance observer, or a long-history recurrent estimator. To reduce excessive dependence on wind-specific temporal patterns, a residual-input regularization term is introduced during policy optimization. In addition, a tail-risk-aware reward is designed to balance nominal tracking accuracy, control smoothness, and suppression of large tracking deviations. The proposed method is evaluated under in-distribution wind, held-out out-of-distribution wind, and measured real-wind disturbances. The results show that the proposed method achieves the most balanced robustness under multimodal wind conditions compared with baselines. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones: 2nd Edition)

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24 pages, 3134 KB

Open AccessArticle

Towards Ubiquitous Sensing and Navigation: A Lightweight Resilient Framework for UAVs Exploiting Unknown SOPs

by Zhiang Bian, Hu Lu, Chunlei Pang, Zhisen Wang and Xin He

Drones 2026, 10(4), 246; https://doi.org/10.3390/drones10040246 - 29 Mar 2026

Viewed by 699

Abstract

GNSS-based navigation can become unreliable when signals are blocked or deliberately interfered with. For small UAV platforms operating in complex environments, this limitation motivates the exploration of alternative positioning strategies such as opportunistic navigation (OpNav). Achieving reliable high-precision positioning under a fully non-cooperative setting remains difficult in practice where no infrastructure information is available. This mode is defined by three key constraints: unknown transmitter locations, unknown environmental topology and strictly asynchronous clocks. To address this limitation, we develop a lightweight sensing and navigation framework designed for UAV platforms operating under strict hardware constraints. We model static scattering centers as environmental anchors, proving that these features restore system observability even with a single unknown emitter. To ensure real-time performance on lightweight flight controllers, a hierarchical two-stage solver is designed: Stage I derives a robust closed-form initial estimate via an algebraic differencing method that is agnostic to reflection orders; Stage II performs manifold refinement using a Clock-Null Projection (CNP) to attain the CRLB. This framework is confirmed through experiments in urban areas using commercial LTE signals. The results show that it can map unknown RF topologies with meter-level accuracy and keep navigating without prior infrastructure, offering a strong solution for UAV autonomy in environments where GNSS is unavailable. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones: 2nd Edition)

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22 pages, 4705 KB

Open AccessArticle

GeoRefGS: Towards Georeferenced 3D Gaussian Splatting from Unmanned Aerial Vehicle Platforms

by Jiahang Hou, Xinsheng Zhang, Hao Li and Siyuan Cui

Drones 2026, 10(3), 195; https://doi.org/10.3390/drones10030195 - 11 Mar 2026

Cited by 3 | Viewed by 2766

Abstract

Three-dimensional reconstruction using unmanned aerial vehicle (UAV) platforms has been extensively utilized in various fields. While conventional techniques such as oblique photogrammetry can produce mesh models with geographical references, they often require substantial computational resources. Although recent studies have attempted to incorporate camera pose parameters into the emerging 3D Gaussian Splatting (3DGS), these methods often treat georeferencing as a post-processing step or rely on global bundle adjustment, which may propagate systematic errors and compromise final accuracy. This work integrates georeferencing as an intrinsic constraint during 3DGS training, enabling simultaneous optimization of geographic and photometric accuracy. The core of our approach lies in introducing a similarity transformation matrix T connecting the local model space with the global geographic coordinate system, along with a dedicated geographic loss function. Geographic coordinates are transformed via T before reprojection to compute the loss function. It was demonstrated that GeoRefGS presents a viable solution for efficiently integrating georeferenced information into 3DGS. Indeed, the proposed framework achieves an improvement of approximately 3.31 dB in peak signal-to-noise ratio while maintaining distance errors below 0.054 m, enabling reliable geographically referenced 3D reconstruction in substantially less time compared to conventional photogrammetric approaches. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones: 2nd Edition)

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30 pages, 4543 KB

Open AccessArticle

Geometric Control with Decoupled Yaw for Quadrotor Cable-Suspended Payload Transportation with Viewpoint Control

by Sachika Masuda and Kosuke Sekiyama

Drones 2026, 10(3), 194; https://doi.org/10.3390/drones10030194 - 11 Mar 2026

Viewed by 1724

Abstract

This study proposes a cooperative aerial transportation control method for cable-suspended payloads using multiple quadrotor unmanned aerial vehicles (UAVs), considering quadrotor viewpoint control during transportation. Conventional cooperative transportation methods typically fix the yaw angles of quadrotors to ensure stability and to avoid dynamic interference with suspended payloads. The novelty of this study lies in realizing a dynamically decoupled control framework for cable-suspended cooperative aerial transportation, in which quadrotor yaw motion is decoupled from the suspended-load dynamics. In the proposed framework, payload stabilization is maintained, while quadrotor yaw-direction control is integrated with mitigation of interference to the suspended-load dynamics, preserving the geometric structure of the system. The effectiveness of the proposed method is validated through numerical simulations of trajectory-tracking transportation with viewpoint control. Under the aggressive (fast) trajectory condition, the proposed method reduces the payload height RMS error by 68.4% and the maximum quadrotor yaw tracking error by 82.5% compared to conventional geometric control. Furthermore, stable payload transportation is achieved in both slow and fast scenarios while maintaining bounded yaw-direction tracking errors. These results suggest that the proposed framework reduces design interdependence between cooperative payload stabilization and yaw-direction control, thereby alleviating design complexity and expanding the structurally available yaw maneuvering freedom within the control framework. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones: 2nd Edition)

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38 pages, 1698 KB

Open AccessArticle

Research on Integrated Decision-Control Cooperative Target Assignment for Cross-Domain Unmanned Systems Based on a Bi-Level Optimization Framework

by Aoyu Zheng, Xiaolong Liang, Zhiyang Zhang, Yuyan Xiao and Jiaqiang Zhang

Drones 2026, 10(3), 193; https://doi.org/10.3390/drones10030193 - 10 Mar 2026

Cited by 1 | Viewed by 906

Abstract

Addressing prevalent challenges in current cooperative task assignment methods for cross-domain unmanned swarm, such as the disconnection between decision-making and execution processes, and the inadequate incorporation of platform kinematic constraints, this study introduces an integrated decision-control cooperative task assignment approach based on a bi-level optimization framework. The proposed framework formulates a bi-level programming model that tightly couples upper-level task assignment with lower-level optimal control. The upper-level model aims to minimize the maximum task completion time by optimizing the assignment and visitation sequences of diverse target types across heterogeneous unmanned platforms. The lower-level model, given the task sequences from the upper level, addresses a minimum-time optimal control problem based on a comprehensive nonlinear kinematic model. This approach enables precise computation of task execution times, which are subsequently fed back to the decision-making layer, thereby establishing a closed-loop optimization mechanism. To solve this complex model efficiently, the lower-level employs differential flatness transformation to eliminate trigonometric functions in the kinematic equations and discretizes the continuous-time optimal control problem into a nonlinear programming problem via the Radau pseudospectral method. For the upper-level combinatorial optimization, an improved genetic algorithm is developed, integrating hybrid encoding, dual-archive elitism preservation, adaptive crossover and mutation strategies, and periodic local search. Simulation results demonstrate that, compared with traditional Euclidean-distance-based assignment methods, the proposed approach generates kinematically feasible and smooth trajectories while thoroughly accounting for the kinematic constraints of heterogeneous platforms, thereby demonstrating its effectiveness and superiority in improving the comprehensive mission performance of cross-domain unmanned swarms. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones: 2nd Edition)

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28 pages, 2950 KB

Open AccessArticle

Research on Simultaneous Arrival Route Planning for UAV Clusters Based on an Improved NSGA-III Algorithm

by Duo Qi, Xiaoyu Shi, Hao Li, Xingyu He and Xiaoyue Ren

Drones 2026, 10(2), 138; https://doi.org/10.3390/drones10020138 - 15 Feb 2026

Cited by 2 | Viewed by 1130

Abstract

This paper addresses the challenge of simultaneous arrival for UAV clusters and proposes a route planning method based on an enhanced Non-dominated Sorting Genetic Algorithm III (NSGA-III). Initially, the paper defines the simultaneous arrival problem and formulates the corresponding mathematical model, considering the complexity of multi-objective optimization in UAV clusters. A novel path generation framework is introduced, which incorporates multiple optimization objectives—such as time coordination, threat mitigation, and resource consumption—aimed at improving flight safety, efficiency, and resource management. To enhance the algorithm’s search performance, a hybrid approach combining the Artificial Bee Colony (ABC) algorithm with NSGA-III is proposed. This improved NSGA-III strategy overcomes the limitations of the original algorithm in managing complex constraints and multi-objective optimization problems, resulting in significant improvements in search accuracy and convergence speed. Finally, the performance of the improved algorithm is evaluated through simulations and compared with traditional methods. The results show that the proposed approach optimizes flight time, reduces resource consumption, and effectively mitigates threats, all while ensuring the simultaneous arrival of UAV clusters. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones: 2nd Edition)

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30 pages, 4820 KB

Open AccessArticle

Cooperative Navigation Framework for UAV Formations Using LSTM and Dynamic Model Fusion

by Fujun Song, Qinghua Zeng, Xiaohu Zhu, Rui Zhang, Xiaoyu Ye and Huan Zhou

Drones 2026, 10(1), 28; https://doi.org/10.3390/drones10010028 - 4 Jan 2026

Cited by 3 | Viewed by 1473

Abstract

In GNSS-denied environments, achieving accurate and reliable positioning for unmanned aerial vehicle (UAV) formations remains a major challenge. This paper presents a cooperative navigation framework for UAV formations based on LSTM and dynamic model information fusion to enhance formation navigation performance under GNSS-denial. The framework employs a dual-driven hierarchical architecture that integrates an LSTM-based dynamic state predictor with historical motion features, including velocity, acceleration, airflow angle, or thrust, thereby enhancing the robustness and positioning accuracy of the leader UAV layer. Furthermore, a multi-source optimal selection strategy based on consistency evaluation is developed to dynamically fuse pseudo-GNSS (P-GNSS), barometric altitude (BA), and wind-speed consistency information, optimizing node allocation between the leader and follower layers. In addition, an IMM-based resilient fusion filtering algorithm is introduced for the follower UAV layer, incorporating UWB, wind-speed, and external-force estimations to maintain reliable navigation under UWB outages and leader-node degradation. Experimental results demonstrate that the proposed framework significantly improves positioning accuracy and formation stability, exhibiting strong adaptability in complex GNSS-denied environments. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones: 2nd Edition)

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29 pages, 9586 KB

Open AccessArticle

L_{1}

Adaptive Nonsingular Fast Terminal Super-Twisting Control for Quadrotor UAVs Under Unknown Disturbances

by Shunsuke Komiyama, Kenji Uchiyama and Kai Masuda

Drones 2025, 9(12), 878; https://doi.org/10.3390/drones9120878 - 18 Dec 2025

Cited by 1 | Viewed by 1382

Abstract

Quadrotor UAVs benefit from control strategies that can deliver rapid convergence and strong robustness in order to fully exploit their high agility. Finite-time control based on terminal sliding modes has been recognized as an effective alternative to classical sliding mode control, which only guarantees asymptotic convergence. Its enhanced variant, nonsingular fast terminal sliding mode control, eliminates singularities and achieves accelerated convergence; however, chattering-induced high-frequency oscillations remain a major concern. To address this issue, this study introduces a hybrid control framework that combines the super-twisting algorithm with

L_{1}

adaptive control. The super-twisting component preserves the robustness of sliding mode control while mitigating chattering, whereas

L_{1}

adaptive control provides rapid online estimation and compensation of model uncertainties and unknown disturbances. The resulting scheme is implemented in a quadrotor flight-control architecture and evaluated through numerical simulations. The results show that the proposed controller offers faster convergence and enhanced robustness relative to existing approaches, particularly in the presence of wind perturbations, periodic obstacle-avoidance maneuvers, and abrupt partial loss of propeller thrust. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones: 2nd Edition)

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21 pages, 1876 KB

Open AccessArticle

Adaptive Minimum Error Entropy Cubature Kalman Filter in UAV-Integrated Navigation Systems

by Xuhang Liu, Hongli Zhao, Yicheng Liu, Suxing Ling, Xinhanyang Chen, Chenyu Yang and Pei Cao

Drones 2025, 9(11), 740; https://doi.org/10.3390/drones9110740 - 24 Oct 2025

Cited by 4 | Viewed by 2872

Abstract

Small unmanned aerial vehicles are now commonly equipped with integrated navigation systems to obtain high-precision navigation parameters. However, affected by the dual impacts of multipath effects and dynamic environmental changes, their state estimation process is vulnerable to interference from measurement outliers, which in turn leads to the degradation of navigation accuracy and poses a threat to flight safety. To address this issue, this research presents an adaptive minimum error entropy cubature Kalman filter. Firstly, the cubature Kalman filter is introduced to solve the problem of model nonlinear errors; secondly, the cubature Kalman filter based on minimum error entropy is derived to effectively curb the interference that measurement outliers impose on filtering results; finally, a kernel bandwidth adjustment factor is designed, and the kernel bandwidth is estimated adaptively to further improve navigation accuracy. Through numerical simulation experiments, the robustness of the proposed method with respect to measurement outliers is validated; further flight experiment results show that compared with existing related filters, this proposed filter can achieve more accurate navigation and positioning. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones: 2nd Edition)

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Review

Jump to: Research

27 pages, 6281 KB

Open AccessReview

Model Predictive Control for Visual-Servo Tracking in Unmanned Aerial Vehicles: A Scoping Review

by José Varela-Aldás, Renato M. Toasa, Viviana Moya, William Chamorro and Oscar Gonzales-Zurita

Drones 2026, 10(9), 667; https://doi.org/10.3390/drones10090667 - 31 Aug 2026

Viewed by 372

Abstract

Model predictive control (MPC) has become a central receding-horizon strategy for robotic systems subject to constraints, uncertainty and multiple objectives. In unmanned aerial vehicles (UAVs), its integration with visual servoing makes it possible to formulate tracking problems directly in the image space, keep targets inside the field of view, anticipate vehicle dynamics and coordinate safety, actuation and perception constraints. This scoping review characterizes the available evidence on MPC applied to visual-servo tracking in UAVs. The process identified 20 core studies, 11 strong complementary studies, 12 borderline studies and 18 non-ideal records for the defined scope. The core literature reveals four dominant lines: tracking of targets and visual features, landing or docking on moving platforms, perception-aware control with visibility constraints, and robust, distributed or learning-augmented MPC extensions. The findings show a recent consolidation of methodological directions, although experimental maturity remains uneven across the field, with a predominance of nonlinear MPC, predictive image-based visual servoing, field-of-view constraints and validation in simulation or UAV platforms. Persistent gaps remain in reproducibility, standardized comparison, visual latency, depth uncertainty, outdoor validation and formalization of perceptual costs. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones: 2nd Edition)

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36 pages, 4336 KB

Open AccessEditor’s ChoiceReview

UAV Positioning Using GNSS: A Review of the Current Status

by Chaopei Jiang, Xingyu Zhou, Hua Chen and Tianjun Liu

Drones 2026, 10(2), 91; https://doi.org/10.3390/drones10020091 - 28 Jan 2026

Cited by 18 | Viewed by 7013

Abstract

Accurate and robust positioning is a critical enabler for Unmanned Aerial Vehicle (UAV) applications, ranging from mapping and inspection to emerging Urban Air Mobility (UAM). While Global Navigation Satellite Systems (GNSS) remain the backbone of absolute positioning, their performance is severely constrained by UAV platform characteristics and complex low-altitude environments. This paper presents a system-level review of GNSS-based UAV positioning. Instead of treating GNSS in isolation, we first link mission requirements and platform constraints, such as aggressive dynamics and Size, Weight, and Power (SWaP) limitations, to specific positioning challenges. We then critically evaluate the spectrum of GNSS techniques, from standalone and Satellite-Based Augmentation System (SBAS) modes to high-precision carrier-phase methods including Real-Time Kinematic (RTK), Post-Processed Kinematic (PPK), Precise Point Positioning (PPP), and PPP-RTK. Furthermore, we discuss multi-sensor fusion with inertial, visual, and Light Detection and Ranging (LiDAR) sensors to mitigate vulnerabilities in urban canyons and GNSS-denied conditions. Finally, we outline key challenges and future directions, highlighting integrity-aware architectures, Artificial Intelligence (AI)-enhanced signal processing, and multi-layer Positioning, Navigation, and Timing (PNT) concepts. The review provides a structured framework and system-level insights to guide resilient navigation for UAV operations in low-altitude airspace. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones: 2nd Edition)

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