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Drones
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Control, Planning, and Embodied Intelligence of Agile UAVs in Indoor...
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Control, Planning, and Embodied Intelligence of Agile UAVs in Indoor Environments

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: 31 August 2026 | Viewed by 7113

Editors

Dr. Minghao Han

E-Mail Website
Guest Editor
Nanyang Environment and Water Research Institute, Nanyang Technological University, Singapore 637141, Singapore
Interests: learning-based control for UAVs; UAV applications in industrial scenarios; reinforcement learning; model predictive control
Dr. Bo Cai

E-Mail Website
Guest Editor
School of Aeronautics, Harbin Institute of Technology, No. 92 Xidazhi Street, Nangang District, Harbin, China
Interests: hybrid terrestrial/aerial bicopters; specialized robotics and autonomy; stochastic systems control; UAV/UGV planning and control; switching systems and hybrid systems
Dr. Lin Zhao

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
Interests: control theory, optimization, and machine learning with applications for UAVs

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicles (UAVs) operating in confined indoor spaces represent one of the most demanding frontiers in autonomous robotics. Unlike open outdoor environments, indoor settings often present GPS-denied conditions, complex geometry, dynamic obstacles, and tight maneuvering constraints. To ensure fast, safe, and reliable navigation under such conditions, UAVs must integrate high-performance control, agile motion planning, and tightly coupled perception and decision-making modules—hallmarks of embodied intelligence.

Advancing autonomous flight in these scenarios requires addressing several intertwined challenges: precise state estimation without external localization systems, real-time obstacle avoidance at high velocity, robust control under aerodynamic disturbances and close-quarters dynamics, and the ability to perceive and adapt to cluttered, dynamic environments. Emerging methods that blend model-based control with learning-based planning, visual–inertial odometry, and onboard sensor fusion are pushing the limits of what is possible for agile UAV flight indoors.

This Special Issue will gather the latest research on enabling agile UAVs to navigate complex indoor environments with minimal human intervention. We seek contributions that explore fundamental theory, algorithmic innovation, system integration, and experimental validation. Studies demonstrating end-to-end autonomy on resource-constrained platforms are especially welcome.

This Special Issue will welcome manuscripts related to the following themes:

  • Agile flight control and disturbance rejection in constrained spaces;
  • Perception-aware motion planning and trajectory optimization;
  • Vision-based navigation and state estimation in GPS-denied environments;
  • Learning-based policies for navigation and control (e.g., imitation learning, reinforcement learning);
  • Integration of perception, control, and planning for real-time operation;
  • Collision avoidance and environment interaction at high speeds;
  • Design of lightweight, high-performance UAV platforms for indoor use;
  • Embodied intelligence and sensorimotor coupling in autonomous UAVs.

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

Dr. Minghao Han
Dr. Bo Cai
Dr. Lin Zhao
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

  • autonomous guidance and navigation
  • agile UAVs
  • indoor navigation
  • agile control
  • perception-aware planning
  • embodied intelligence

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

Published Papers (4 papers)

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Research

24 pages, 2507 KB  
Article
Map-Change-Driven Closed-Loop Replanning for UAV Navigation in Unknown Indoor Environments
by Mo Chen, Qiang Lu and Xiongding Liu
Drones 2026, 10(3), 168; https://doi.org/10.3390/drones10030168 - 28 Feb 2026
Cited by 2 | Viewed by 1177
Abstract
Autonomous Unmanned Aerial Vehicle (UAV) navigation in unknown indoor environments is challenged by incremental map revelation and non-uniform geometric changes, which frequently invalidate preplanned trajectories. Existing time-triggered replanning strategies are poorly aligned with such irregular environmental evolution, often resulting in either redundant computation [...] Read more.
Autonomous Unmanned Aerial Vehicle (UAV) navigation in unknown indoor environments is challenged by incremental map revelation and non-uniform geometric changes, which frequently invalidate preplanned trajectories. Existing time-triggered replanning strategies are poorly aligned with such irregular environmental evolution, often resulting in either redundant computation or delayed responses to critical structural variations. To overcome these limitations, this paper proposes a map-change-driven closed-loop replanning mechanism (MCR) embedded within a distance-field-based hierarchical exploration–planning–control framework. The proposed approach explicitly monitors local Euclidean Signed Distance Field (ESDF) structural changes and exploration goal updates, triggering replanning only when significant geometric or task-level variations are detected. This event-driven design enables timely trajectory adaptation while effectively suppressing unnecessary replanning. Extensive experiments conducted in a high-fidelity indoor warehouse simulation environment demonstrate that the proposed method consistently outperforms single-shot planning and fixed-interval replanning baselines in terms of task success rate, trajectory smoothness, safety margin, and replanning efficiency. These results validate the effectiveness of using map structural evolution as the core driver for replanning in unknown indoor UAV navigation. Full article
(This article belongs to the Special Issue Control, Planning, and Embodied Intelligence of Agile UAVs in Indoor Environments)
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23 pages, 698 KB  
Article
A Hamiltonian Neural Differential Dynamics Model and Control Framework for Autonomous Obstacle Avoidance in a Quadrotor Subject to Model Uncertainty
by Xu Wang, Yanfang Liu, Desong Du, Huarui Xu and Naiming Qi
Drones 2026, 10(1), 64; https://doi.org/10.3390/drones10010064 - 19 Jan 2026
Viewed by 1015
Abstract
Establishing precise and reliable quadrotor dynamics model is crucial for safe and stable tracking control in obstacle environments. However, obtaining such models is challenging, as it requires precise inertia identification and accounting for complex aerodynamic effects, which handcrafted models struggle to do. To [...] Read more.
Establishing precise and reliable quadrotor dynamics model is crucial for safe and stable tracking control in obstacle environments. However, obtaining such models is challenging, as it requires precise inertia identification and accounting for complex aerodynamic effects, which handcrafted models struggle to do. To address this, this paper proposes a safety-critical control framework built on a Hamiltonian neural differential model (HDM). The HDM formulates the quadrotor dynamics under a Hamiltonian structure over the SE(3) manifold, with explicitly optimizable inertia parameters and a neural network-approximated control input matrix. This yields a neural ordinary differential equation (ODE) that is solved numerically for state prediction, while all parameters are trained jointly from data via gradient descent. Unlike black-box models, the HDM incorporates physical priors—such as SE(3) constraints and energy conservation—ensuring a physically plausible and interpretable dynamics representation. Furthermore, the HDM is reformulated into a control-affine form, enabling controller synthesis via control Lyapunov functions (CLFs) for stability and exponential control barrier functions (ECBFs) for rigorous safety guarantees. Simulations validate the framework’s effectiveness in achieving safe and stable tracking control. Full article
(This article belongs to the Special Issue Control, Planning, and Embodied Intelligence of Agile UAVs in Indoor Environments)
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26 pages, 13353 KB  
Article
WA-LPA*: An Energy-Aware Path-Planning Algorithm for UAVs in Dynamic Wind Environments
by Fangjia Lian, Bangjie Li, Qisong Yang, Hongwei Zhu and Desong Du
Drones 2025, 9(12), 850; https://doi.org/10.3390/drones9120850 - 11 Dec 2025
Cited by 6 | Viewed by 2107
Abstract
Energy optimization is crucial for unmanned aerial vehicle (UAV) path planning, particularly in complex wind-field environments. Most existing path-planning algorithms rely on simplified energy consumption models, which often fail to adequately capture the effects of wind fields. To address this limitation, a wind-adaptive [...] Read more.
Energy optimization is crucial for unmanned aerial vehicle (UAV) path planning, particularly in complex wind-field environments. Most existing path-planning algorithms rely on simplified energy consumption models, which often fail to adequately capture the effects of wind fields. To address this limitation, a wind-adaptive lifelong planning A* algorithm (WA-LPA*) is proposed for energy-aware path planning in dynamic wind environments. WA-LPA* constructs a composite heuristic function incorporating wind-field alignment factors and integrates a hierarchical height-aware optimization strategy. Meanwhile, an adaptive replanning mechanism is designed based on the change characteristics of the wind field. Simulation experiments conducted across representative scenarios demonstrate that, compared to conventional algorithms that neglect wind-field effects, WA-LPA* achieves energy efficiency improvements of 5.9–29.4%. Full article
(This article belongs to the Special Issue Control, Planning, and Embodied Intelligence of Agile UAVs in Indoor Environments)
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22 pages, 3813 KB  
Article
Attitude Dynamics and Agile Control of a High-Mass-Ratio Moving-Mass Coaxial Dual-Rotor UAV
by Jiahui Sun, Qingfeng Du and Ke Zhang
Drones 2025, 9(9), 600; https://doi.org/10.3390/drones9090600 - 26 Aug 2025
Cited by 3 | Viewed by 1582
Abstract
This study presents the configuration design and attitude control of a moving-mass coaxial dual-rotor UAV (MMCDRUAV) for indoor applications. Compared with existing configurations, the proposed configuration avoids additional actuation mass and improves the control authority. Based on these improvements, a promising micro UAV [...] Read more.
This study presents the configuration design and attitude control of a moving-mass coaxial dual-rotor UAV (MMCDRUAV) for indoor applications. Compared with existing configurations, the proposed configuration avoids additional actuation mass and improves the control authority. Based on these improvements, a promising micro UAV platform with a high payload ability for agile indoor flight could be developed. Ground validation tests demonstrated its maneuverability, as provided by a moving-mass control (MMC) module requiring only the repositioning of existing components (e.g., battery packs) as movable masses. For trajectory tracking, an adaptive backstepping active disturbance rejection controller (ADRC) is proposed. The architecture integrates extended-state observers (ESOs) for disturbance estimation, parameter-adaptation laws for uncertainty compensation, and auxiliary systems to address control saturation. Lyapunov stability analysis proved the existence of uniformly ultimately bounded (UUB) closed-loop tracking errors. The results of the ground verification experiment confirmed enhanced tracking performance under real-world disturbances. Full article
(This article belongs to the Special Issue Control, Planning, and Embodied Intelligence of Agile UAVs in Indoor Environments)
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