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Navigation, Control and Mission Planning Advances for Safe, Efficient and...
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Navigation, Control and Mission Planning Advances for Safe, Efficient and Autonomous Drones: 2nd Edition

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

Deadline for manuscript submissions: 28 February 2027 | Viewed by 10524

Editors

Dr. Nadjim Horri

E-Mail Website
Guest Editor
School of Future Transport Engineering, Faculty of Engineering, Environment and Computing, Coventry University, Coventry CV1 5FB, UK
Interests: fault tolerant control; observers for disturbance estimation and rejection; model predictive control (MPC); Kalman filters and particle filters for navigation; control of aircraft including UAV; underactuated spacecraft control; EVTOL control
Special Issues, Collections and Topics in MDPI journals
Dr. Samir Khan

E-Mail Website
Guest Editor
Graduate School of Engineering, The University of Tokyo, Tokyo, Japan
Interests: artificial intelligence; IoT; fault tolerance; fault diagnosis; optimisation; autonomous maintenance; drones
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vaios Lappas

E-Mail Website
Guest Editor
Department of Aerospace Science & Technology, National & Kapodistrian University of Athens, 157 72 Athens, Greece
Interests: attitude determination and control; UAV; control; satellite technology; spacecraft propulsion; autonomy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit manuscripts to the MDPI Drones Special Issue entitled “Navigation, Control and Mission Planning Advances for Safe, Efficient and Autonomous Drones: 2nd Edition”.

Drones have considerably evolved over the last two decades, with an increased emphasis on safety, autonomy and performance to perform a wide range of missions. Air, ground, marine and even space vehicles are currently used for applications from land surveys to precision agriculture, disaster monitoring, forestry and other applications in society and in several industries.

Advances in navigation, control, as well as other enabling technologies from data handling to communication systems are necessary to safely meet the increased demand for autonomy. In a single drone, these challenges include the ability to maintain admissible or optimal flight performance using limited computational and power resources with the ability to handle faults, anomalies, as well as vehicle and mission constraints such as drone endurance and operational envelopes.

In UAV swarms and formations, the challenges extend to the need for mission-level architectures to coordinate path planning and path following, using centralised or decentralised navigation, control and communication systems, including ground station–vehicle communications.

The state-of-the-art methods used to address challenges in single and distributed drone systems are often based on advances in the navigation and control theory, increasingly based on machine learning, or a combination of those two approaches, such as artificial intelligence (AI)-enhanced navigation and control. Advances in new technologies such as the Internet of Things and Detect and Avoid are also increasingly exploited to enhance navigation and control safety and performance.

This Special Issue will therefore bring together papers which describe recent research in the navigation, control and mission planning of drones, including ground, air, marine or space vehicles. Papers with theoretical, simulation and practical experimental results in this field are all encouraged. This includes review papers, tutorials, as well as original research papers.

Possible topics include, but are not limited to, the following:

  • Advances in path planning and path following methods for drones;
  • Machine learning-based navigation and control or mission planning in drones;
  • Unmanned aerial vehicles (UAVs), autonomous underwater vehicles (AUVs), and spacecraft navigation and control;
  • Adaptive, optimal or robust control of drones;
  • Control under vehicle, operational and collision avoidance constraints;
  • Sensor fusion for drone navigation;
  • Hybrid and multimode navigation and control systems;
  • Linear and nonlinear motion estimation using filtering, observer-based and recursive methods;
  • Fault detection, isolation and recovery in drones;
  • Multi-vehicle networks and communication systems for coordinated drone navigation and control;
  • Coordinated navigation and control of formations and swarms of aerial, ground or space vehicles;
  • Distributed systems with different types of vehicles (e.g., ground and air vehicles, air and space vehicles);
  • Advances in computer and data handling systems for increased navigation and control autonomy;
  • Dynamical modelling and/or control for emerging drone designs (hybrid UAV designs, eVToL);
  • Internet of Things applications in drone navigation and control;
  • System identification for drones.

Dr. Nadjim Horri
Dr. Samir Khan
Prof. Dr. Vaios Lappas
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

  • drone
  • path following
  • navigation
  • control
  • machine learning
  • autonomy
  • UAV
  • spacecraft

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (5 papers)

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Research

36 pages, 1445 KB  
Article
Hierarchical Multi-Agent Navigation Through the 72-h Thermal Drift Cliff
by Mosab Alrashed, Humoud Aldaihani and Mohammad Alqattan
Drones 2026, 10(8), 561; https://doi.org/10.3390/drones10080561 - 24 Jul 2026
Viewed by 1037
Abstract
Long-endurance unmanned aerial vehicle (UAV) missions beyond 72 consecutive flight hours face a reliability boundary at which thermal gyroscope drift drives the inertial navigation system (INS) position error into rapid nonlinear divergence at a predictable threshold tc. This paper presents BAZ [...] Read more.
Long-endurance unmanned aerial vehicle (UAV) missions beyond 72 consecutive flight hours face a reliability boundary at which thermal gyroscope drift drives the inertial navigation system (INS) position error into rapid nonlinear divergence at a predictable threshold tc. This paper presents BAZ II, a simulation-validated multi-agent navigation system that extends the analytical BAZ (bifurcation-aware zonal navigation) framework. Its central idea is to treat communication quality as a planning resource and combine it with multi-agent collaboration, making the navigation cliff a manageable degradation event rather than a hard operating limit. Four contributions support this idea: a thermalhysteresis MEMS gyroscope drift model reproduces the analytical cliff in simulation and supplies its physical mechanism; a distributed collaborative simultaneous localization and mapping (SLAM) filter coupled to a stochastic continuous-time Markov chain (CTMC) interagent channel sustains GPS-denied localization within the operational accuracy budget; a 3D Gaussian process RF-aware model predictive controller (MPC) with cognitive radio frequency-hopping restores link availability under jamming, while an analytic hierarchy process (AHP)-weighted multi-objective communication cost improves latency and jitter at negligible signal-to-noise ratio cost; finally, the integrated controller executes within the onboard real-time budget of an NVIDIA Jetson Xavier NX. All results are obtained in simulation, with hardware-in-the-loop and field testing remaining as priority future work. Full article
(This article belongs to the Special Issue Navigation, Control and Mission Planning Advances for Safe, Efficient and Autonomous Drones: 2nd Edition)
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44 pages, 680 KB  
Article
Stochastically Optimal Hierarchical Control for Long-Endurance UAVs Under Communication Degradation: Theory and Validation
by Mosab Alrashed, Ali Fenjan, Humoud Aldaihani and Mohammad Alqattan
Drones 2026, 10(5), 371; https://doi.org/10.3390/drones10050371 - 13 May 2026
Cited by 1 | Viewed by 2552
Abstract
This paper establishes a theoretical framework for treating communication quality as a navigable resource in long-endurance unmanned aerial vehicle (UAV) control under stochastic degradation. We prove that a hierarchical architecture integrating communication-aware model predictive control (MPC) achieves ε-optimality with respect to the [...] Read more.
This paper establishes a theoretical framework for treating communication quality as a navigable resource in long-endurance unmanned aerial vehicle (UAV) control under stochastic degradation. We prove that a hierarchical architecture integrating communication-aware model predictive control (MPC) achieves ε-optimality with respect to the intractable stochastic dynamic programming formulation while maintaining exponential stability guarantees under switched system dynamics governed by continuous-time Markov chains. Three primary theoretical contributions were made: (1) A stochastic optimality theorem is given showing that sigmoid penalty function approximation yields bounded suboptimality of η≤0.12 under mild ergodicity conditions; (2) a formal stability result for mode switching based on hysteresis was established using multiple Lyapunov functions, and it showed exponentially fast convergence with a decay rate of λ≥0.23; and (3) bifurcation analysis showed that there is a critical time threshold of 72 h at which thermal-induced gyro-drift in the GPS sensor causes a transition in navigation error dynamics from linear to catastrophic nonlinear growth. The validation through 2430 Monte Carlo missions over 54,686 flight hours resulted in an average increase in endurance by 243% (18.2 days versus 5.3 days), while keeping CEP at approximately 8.7 m and achieving 82% mission success under extreme communication degradation (qcomm<0.3). The statistical results confirm a very strong positive relationship between the Resilience Quotient (RQ) and the length of successful missions (R2=0.89, p<0.001), supporting the theoretical model with empirical evidence. Full article
(This article belongs to the Special Issue Navigation, Control and Mission Planning Advances for Safe, Efficient and Autonomous Drones: 2nd Edition)
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37 pages, 19367 KB  
Article
MarsBird-VII: An Autonomous Stereo–Inertial Navigation System with Real-Time Optimization for a Mars Rotorcraft Space Drone
by Ju Xiao, Hanchen Qiu, Yukun Zhou, Rui Wang and Peng Liu
Drones 2026, 10(5), 346; https://doi.org/10.3390/drones10050346 - 4 May 2026
Viewed by 940
Abstract
Reliable autonomous navigation for Tianwen-3-class Mars rotorcraft must satisfy both sampling-level accuracy and hard real-time execution under severe onboard computational constraints. To address this challenge, we develop MarsBird-VII, a mission-constrained stereo visual–inertial navigation system that combines a computation-aware vision front-end with a Parity-Window [...] Read more.
Reliable autonomous navigation for Tianwen-3-class Mars rotorcraft must satisfy both sampling-level accuracy and hard real-time execution under severe onboard computational constraints. To address this challenge, we develop MarsBird-VII, a mission-constrained stereo visual–inertial navigation system that combines a computation-aware vision front-end with a Parity-Window sliding-window optimization back-end. The front-end decouples high-rate tracking from feature replenishment to bound perception latency, while the back-end alternates updates over interleaved state subsets and preserves full-window coupling through unified marginalization. Unlike simply reducing the sliding-window size, the proposed strategy reduces the per-update optimization cost without shrinking the geometric observation horizon, thereby improving the accuracy–runtime trade-off for embedded avionics. Earth-analog flight experiments demonstrate strong navigation performance under mission-relevant conditions. In full-sequence evaluation, the proposed system achieves an SE(3)-aligned translation APE of 0.31 m RMSE/0.47 m Max and further reaches 0.06 m RMSE/0.15 m Max on a nominal stable segment. Runtime profiling over 5000+ update cycles shows that the Parity-Window back-end keeps the maximum optimization latency below 58.32 ms, satisfying the 66.7 ms hard real-time deadline while maintaining accuracy close to full-window optimization. These results show that the proposed system provides a practical balance of accuracy, robustness, and deterministic real-time performance for Tianwen-3-class Mars rotorcraft navigation. Full article
(This article belongs to the Special Issue Navigation, Control and Mission Planning Advances for Safe, Efficient and Autonomous Drones: 2nd Edition)
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34 pages, 7001 KB  
Article
A Multi-Layer Resilient Architecture for Autonomous Quadcopter-Based Bridge Inspection Under Environmental Uncertainties
by Zhenyu Shi and Donghoon Kim
Drones 2026, 10(2), 136; https://doi.org/10.3390/drones10020136 - 15 Feb 2026
Cited by 1 | Viewed by 1522
Abstract
This paper presents a multi-layer architecture designed to enhance the reliable autonomous flight of single and multiple quadcopters in simulation. The architecture leverages concepts inspired by the resilient spacecraft executive to hierarchically organize trajectory planning and flight control and integrates an extended Simplex [...] Read more.
This paper presents a multi-layer architecture designed to enhance the reliable autonomous flight of single and multiple quadcopters in simulation. The architecture leverages concepts inspired by the resilient spacecraft executive to hierarchically organize trajectory planning and flight control and integrates an extended Simplex framework that employs multiple candidate algorithms to provide safety assurance at each layer, with a supervisory program that adapts Simplex behavior based on system states and environmental conditions to enable high-level mission management. The approach is evaluated in bridge-inspection simulations under environmental uncertainties, including varying wind conditions and obstacles. Across multiple operating configurations and Monte Carlo simulation runs, the architecture achieves high coverage rates; notably, under high-wind conditions, it reduces average trajectory deviation by 66.2%. The results demonstrate proactive safety through graceful degradation in both trajectory planning and flight control under stress and off-nominal conditions. Full article
(This article belongs to the Special Issue Navigation, Control and Mission Planning Advances for Safe, Efficient and Autonomous Drones: 2nd Edition)
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22 pages, 7825 KB  
Article
Enhanced Dynamic Obstacle Avoidance for UAVs Using Event Camera and Ego-Motion Compensation
by Bahar Ahmadi and Guangjun Liu
Drones 2025, 9(11), 745; https://doi.org/10.3390/drones9110745 - 25 Oct 2025
Cited by 4 | Viewed by 3221
Abstract
To navigate dynamic environments safely, UAVs require accurate, real time onboard perception, which relies on ego motion compensation to separate self-induced motion from external dynamics and enable reliable obstacle detection. Traditional ego-motion compensation techniques are mainly based on optimization processes and may be [...] Read more.
To navigate dynamic environments safely, UAVs require accurate, real time onboard perception, which relies on ego motion compensation to separate self-induced motion from external dynamics and enable reliable obstacle detection. Traditional ego-motion compensation techniques are mainly based on optimization processes and may be computationally expensive for real-time applications or lack the precision needed to handle both rotational and translational movements, leading to issues such as misidentifying static elements as dynamic obstacles and generating false positives. In this paper, we propose a novel approach that integrates an event camera-based perception pipeline with an ego-motion compensation algorithm to accurately compensate for both rotational and translational UAV motion. An enhanced warping function, integrating IMU and depth data, is constructed to compensate camera motion based on real-time IMU data to remove ego motion from the asynchronous event stream, enhancing detection accuracy by reducing false positives and missed detections. On the compensated event stream, dynamic obstacles are detected by applying a motion aware adaptive threshold to the normalized mean timestamp image, with the threshold derived from the image’s spatial mean and standard deviation and adjusted by the UAV’s angular and linear velocities. Furthermore, in conjunction with a 3D Artificial Potential Field (APF) for obstacle avoidance, the proposed approach generates smooth, collision-free paths, addressing local minima issues through a rotational force component to ensure efficient UAV navigation in dynamic environments. The effectiveness of the proposed approach is validated through simulations, and its application for UAV navigation, safety, and efficiency in environments such as warehouses is demonstrated, where real-time response and precise obstacle avoidance are essential. Full article
(This article belongs to the Special Issue Navigation, Control and Mission Planning Advances for Safe, Efficient and Autonomous Drones: 2nd Edition)
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