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Recent Advances on UAVs’ GN&C (Guidance, Navigation, and Control) Technology
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Recent Advances on UAVs’ GN&C (Guidance, Navigation, and Control) Technology

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

Deadline for manuscript submissions: closed (15 January 2023) | Viewed by 19632

Special Issue Editor

Prof. Dr. Hyochoong Bang

E-Mail Website
Guest Editor
School of Mechanical Aerospace and Systems Engineering, Korea Advanced Institute of Science & Technology, Yusong, Korea
Interests: spacecraft flight dynamics and control; UAV guidance and navigation

Special Issue Information

Dear Colleagues,

UAVs are expected to take on increasingly important roles in more challenging applications in the future. To meet the demands of these roles, UAV autonomy is needed, which heavily depends on guidance, navigation, and control (GN&C) technology. GN&C is one of the key technological elements accelerating advances in future UAV systems. As new types of UAV platforms are emerging rapidly, reliable GN&C systems are considered essential to achieve future UAV mission objectives. For instance, GN&C systems for small-size multicopters are already matured enough, anticipating new challenges.

This Special Issue on UAVs GN&C aims to provide an opportunity to exchange state-of-the-art approaches toward a higher level of autonomy as well as intelligence. We invite papers on new innovative ideas on UAV GN&C systems. Not only conventional GN&C but also recent emerging technologies supported by new hardware and software technologies are invited. Artificial Intelligence and machine learning technologies could be principal drivers for future UAVs. Papers on recent hot issues dealing with VTOL platforms for UAM (urban aerial mobility) are also encouraged.

The potential topics include but are not limited to:

  • UAV guidance laws
  • Adaptive guidance and control
  • Vison-based navigation
  • Terrain referenced navigation
  • Fault tolerant control
  • Redundant GN&C systems with fault management
  • Machine learning applications
  • Navigation for indoor missions
  • Artificial Intelligence for UAVs
  • Autonomy in GN&C
  • VTOL UAVs GN&C for UAV applications in particular

It is expected that this Special Issue will attract the attention of academia, industry as well as professional research institutes working on UAV research. Participation to this Special Issue will serve as a great opportunity for authors to upgrade their careers as potential leaders in this very important field. Thank you for your contribution in advance.

Prof. Dr. Hyochoong Bang
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly 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.

Published Papers (5 papers)

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Research

34 pages, 27535 KiB  
Article
Self-Scheduled LPV Control of Asymmetric Variable-Span Morphing UAV
by Jihoon Lee, Seong-Hun Kim, Hanna Lee and Youdan Kim
Sensors 2023, 23(6), 3075; https://doi.org/10.3390/s23063075 - 13 Mar 2023
Cited by 1 | Viewed by 1508
Abstract
In this study, a novel framework for the flight control of a morphing unmanned aerial vehicle (UAV) based on linear parameter-varying (LPV) methods is proposed. A high-fidelity nonlinear model and LPV model of an asymmetric variable-span morphing UAV were obtained using the NASA [...] Read more.
In this study, a novel framework for the flight control of a morphing unmanned aerial vehicle (UAV) based on linear parameter-varying (LPV) methods is proposed. A high-fidelity nonlinear model and LPV model of an asymmetric variable-span morphing UAV were obtained using the NASA generic transport model. The left and right wing span variation ratios were decomposed into symmetric and asymmetric morphing parameters, which were then used as the scheduling parameter and the control input, respectively. LPV-based control augmentation systems were designed to track the normal acceleration, angle of sideslip, and roll rate commands. The span morphing strategy was investigated considering the effects of morphing on various factors to aid the intended maneuver. Autopilots were designed using LPV methods to track commands for airspeed, altitude, angle of sideslip, and roll angle. A nonlinear guidance law was coupled with the autopilots for three-dimensional trajectory tracking. A numerical simulation was performed to demonstrate the effectiveness of the proposed scheme. Full article
(This article belongs to the Special Issue Recent Advances on UAVs’ GN&C (Guidance, Navigation, and Control) Technology)
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19 pages, 5505 KiB  
Article
MTF Measurement by Slanted-Edge Method Based on Improved Zernike Moments
by Shuo Zhang, Fengyan Wang, Xiang Wu and Kangzhe Gao
Sensors 2023, 23(1), 509; https://doi.org/10.3390/s23010509 - 2 Jan 2023
Cited by 6 | Viewed by 7470
Abstract
The modulation transfer function (MTF) is an important parameter for performance evaluation of optical imaging systems in photogrammetry and remote sensing; the slanted-edge method is one of the main methods for measuring MTF. To solve the problem of inaccurate edge detection by traditional [...] Read more.
The modulation transfer function (MTF) is an important parameter for performance evaluation of optical imaging systems in photogrammetry and remote sensing; the slanted-edge method is one of the main methods for measuring MTF. To solve the problem of inaccurate edge detection by traditional methods under the conditions of noise and blur, this paper proposes a new method of MTF measurement with a slanted-edge method based on improved Zernike moments, which firstly introduces the Otsu algorithm to automatically determine the Zernike moment threshold for sub-pixel edge detection to precisely locate the edge points, then obtains LSF through edge point projection, ESF sampling point acquisition, smoothing, fitting, taking ESF curve differential and Gaussian fitting, and finally, accurately obtaining MTF by LSF Fourier transform and modulo normalization. Based on simulation experiments and outdoor target experiments, the reliability of the proposed algorithm is verified by the deviations of slanted-edge angle and MTF measurement, and the tolerance degree of edge detection to noise and ambiguity are analyzed. The results show that compared with ISO 12233, OMNI-sine method, Hough transform method and LSD method, this algorithm has the highest edge detection accuracy, the maximum tolerance of noise and ambiguity, and also improves the accuracy of MTF measurement. Full article
(This article belongs to the Special Issue Recent Advances on UAVs’ GN&C (Guidance, Navigation, and Control) Technology)
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20 pages, 3379 KiB  
Article
Quadcopter UAVs Extended States/Disturbance Observer-Based Nonlinear Robust Backstepping Control
by Ha Le Nhu Ngoc Thanh, Tuan Tu Huynh, Mai The Vu, Nguyen Xuan Mung, Nguyen Ngoc Phi, Sung Kyung Hong and Truong Nguyen Luan Vu
Sensors 2022, 22(14), 5082; https://doi.org/10.3390/s22145082 - 6 Jul 2022
Cited by 4 | Viewed by 2770
Abstract
A trajectory tracking control for quadcopter unmanned aerial vehicle (UAV) based on a nonlinear robust backstepping algorithm and extended state/disturbance observer (ESDO) is presented in this paper. To obtain robust attitude stabilization and superior performance of three-dimension position tracking control, the construction of [...] Read more.
A trajectory tracking control for quadcopter unmanned aerial vehicle (UAV) based on a nonlinear robust backstepping algorithm and extended state/disturbance observer (ESDO) is presented in this paper. To obtain robust attitude stabilization and superior performance of three-dimension position tracking control, the construction of the proposed algorithm can be separated into three parts. First, a mathematical model of UAV negatively influenced by exogenous disturbances is established. Following, an extended state/disturbance observer using a general second-order model is designed to approximate undesirable influences of perturbations on the UAVs dynamics. Finally, a nonlinear robust controller is constructed by an integration of the nominal backstepping technique with ESDO to enhance the performance of attitude and position control mode. Robust stability of the closed-loop disturbed system is obtained and guaranteed through the Lyapunov theorem without precise knowledge of the upper bound condition of perturbations. Lastly, a numerical simulation is carried out and compared with other previous controllers to demonstrate the great advantage and effectiveness of the proposed control method. Full article
(This article belongs to the Special Issue Recent Advances on UAVs’ GN&C (Guidance, Navigation, and Control) Technology)
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26 pages, 7135 KiB  
Article
Optimal Motion Planning in GPS-Denied Environments Using Nonlinear Model Predictive Horizon
by Younes Al Younes and Martin Barczyk
Sensors 2021, 21(16), 5547; https://doi.org/10.3390/s21165547 - 18 Aug 2021
Cited by 3 | Viewed by 2309
Abstract
Navigating robotic systems autonomously through unknown, dynamic and GPS-denied environments is a challenging task. One requirement of this is a path planner which provides safe trajectories in real-world conditions such as nonlinear vehicle dynamics, real-time computation requirements, complex 3D environments, and moving obstacles. [...] Read more.
Navigating robotic systems autonomously through unknown, dynamic and GPS-denied environments is a challenging task. One requirement of this is a path planner which provides safe trajectories in real-world conditions such as nonlinear vehicle dynamics, real-time computation requirements, complex 3D environments, and moving obstacles. This paper presents a methodological motion planning approach which integrates a novel local path planning approach with a graph-based planner to enable an autonomous vehicle (here a drone) to navigate through GPS-denied subterranean environments. The local path planning approach is based on a recently proposed method by the authors called Nonlinear Model Predictive Horizon (NMPH). The NMPH formulation employs a copy of the plant dynamics model (here a nonlinear system model of the drone) plus a feedback linearization control law to generate feasible, optimal, smooth and collision-free paths while respecting the dynamics of the vehicle, supporting dynamic obstacles and operating in real time. This design is augmented with computationally efficient algorithms for global path planning and dynamic obstacle mapping and avoidance. The overall design is tested in several simulations and a preliminary real flight test in unexplored GPS-denied environments to demonstrate its capabilities and evaluate its performance. Full article
(This article belongs to the Special Issue Recent Advances on UAVs’ GN&C (Guidance, Navigation, and Control) Technology)
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18 pages, 5232 KiB  
Article
Deep Reinforcement Learning for End-to-End Local Motion Planning of Autonomous Aerial Robots in Unknown Outdoor Environments: Real-Time Flight Experiments
by Oualid Doukhi and Deok-Jin Lee
Sensors 2021, 21(7), 2534; https://doi.org/10.3390/s21072534 - 4 Apr 2021
Cited by 18 | Viewed by 4490
Abstract
Autonomous navigation and collision avoidance missions represent a significant challenge for robotics systems as they generally operate in dynamic environments that require a high level of autonomy and flexible decision-making capabilities. This challenge becomes more applicable in micro aerial vehicles (MAVs) due to [...] Read more.
Autonomous navigation and collision avoidance missions represent a significant challenge for robotics systems as they generally operate in dynamic environments that require a high level of autonomy and flexible decision-making capabilities. This challenge becomes more applicable in micro aerial vehicles (MAVs) due to their limited size and computational power. This paper presents a novel approach for enabling a micro aerial vehicle system equipped with a laser range finder to autonomously navigate among obstacles and achieve a user-specified goal location in a GPS-denied environment, without the need for mapping or path planning. The proposed system uses an actor–critic-based reinforcement learning technique to train the aerial robot in a Gazebo simulator to perform a point-goal navigation task by directly mapping the noisy MAV’s state and laser scan measurements to continuous motion control. The obtained policy can perform collision-free flight in the real world while being trained entirely on a 3D simulator. Intensive simulations and real-time experiments were conducted and compared with a nonlinear model predictive control technique to show the generalization capabilities to new unseen environments, and robustness against localization noise. The obtained results demonstrate our system’s effectiveness in flying safely and reaching the desired points by planning smooth forward linear velocity and heading rates. Full article
(This article belongs to the Special Issue Recent Advances on UAVs’ GN&C (Guidance, Navigation, and Control) Technology)
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