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Drones
Special Issues
Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition
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Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition

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

Deadline for manuscript submissions: 15 December 2025 | Viewed by 2950

Special Issue Editors

Dr. Heng Shi

E-Mail Website
Guest Editor
Department of Precision Instruments, Tsinhgua University, Beijing 100190, China
Interests: cooperative guidance; intelligent guidance; trajectory planning; aircraft control; sensor fusion; aircraft simulations; reinforcement learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jihong Zhu

E-Mail Website
Guest Editor
Department of computer science and technology, Tsinghua University, Beijing 100190, China
Interests: automatic control; flight control; unmanned system
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zheng Chen

E-Mail Website
Guest Editor
School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310058, China
Interests: guidance, navigation, and control; flight dynamics and simulations; optimal control and optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Minchi Kuang

E-Mail Website
Guest Editor
Department of Precision Instrument, Tsinghua University, Beijing 100190, China
Interests: flight control; un-manned system; intelligent control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Path planning, trajectory tracking, and guidance are essential for the autonomous operations of Unmanned Aerial Vehicles (UAVs). These processes involve determining the optimal path, implementing the planned path, and making real-time adjustments to ensure accurate tracking and obstacle avoidance. The ability to plan efficient and safe paths for UAVs is crucial for the successful completion of missions, especially in complex environments. Moreover, implementing planned paths while considering external factors such as wind and turbulence, along with real-time guidance adjustment, ensures UAV safety and stability. Research in this area focuses on developing advanced algorithms and control systems that enable UAVs to operate autonomously and effectively in complex environments.

This Special Issue will collect the latest research results for path planning, trajectory tracking, and guidance of UAVs, which are fundamentally important for the autonomous operations of UAVs.

Papers are solicited in areas directly related to topics including, but not limited to, the following:

  • Path planning and task assignment for UAV swarms;
  • Autonomous navigation and localization (both outdoor and indoor);
  • Autonomous decision making;
  • Trajectory planning and optimization;
  • Guidance for individual UAV or for multiple cooperative UAVs;
  • Control algorithms;
  • Data-driven guidance and control;
  • AI-based planning.

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

Dr. Heng Shi
Prof. Dr. Jihong Zhu
Prof. Dr. Zheng Chen
Dr. Minchi Kuang
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 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. 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

  • path planning
  • trajectory tracking
  • guidance, navigation, and control
  • autonomous control
  • trajectory optimization
  • formation and reconfiguration

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (5 papers)

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Research

24 pages, 41034 KB  
Article
A Novel Design of a Sliding Mode Controller Based on Modified ERL for Enhanced Quadcopter Trajectory Tracking
by Ahmed Abduljabbar Mahmood, Fernando García and Abdulla Al-Kaff
Drones 2025, 9(11), 737; https://doi.org/10.3390/drones9110737 - 23 Oct 2025
Abstract
This paper introduces a new approach to obtain robust tracking performance, disturbance resistance, and input variation resistance, and eliminate chattering phenomena in the control signal and output responses of an unmanned aerial vehicle (UAV) quadcopter with parametric uncertainty. This method involves a modified [...] Read more.
This paper introduces a new approach to obtain robust tracking performance, disturbance resistance, and input variation resistance, and eliminate chattering phenomena in the control signal and output responses of an unmanned aerial vehicle (UAV) quadcopter with parametric uncertainty. This method involves a modified exponential reaching law (ERL) of the sliding mode control (SMC) based on a Gaussian kernel function with a continuous nonlinear Smoother Signum Function (SSF). The smooth continuous signum function is proposed as a substitute for the signum function to prevent the chattering effect caused by the switching sliding surface. The closed-loop system’s stability is ensured according to Lyapunov’s stability theory. Optimal trajectory tracking is attained based on particle swarm optimization (PSO) to select the controller parameters. A comparative analysis with a classical hierarchical SMC based on different ERLs (sign function, saturation function, and SSF) is presented to further substantiate the superior performance of the proposed controller. The outcomes of the simulation prove that the suggested controller has much better effectiveness, unknown disturbance resistance, input variation resistance, and parametric uncertainty than the other controllers, which produce chattering and make the control signal range fall within unrealistic values. Furthermore, the suggested controller outperforms the classical SMC by reducing the tracking integral mean squared errors by 96.154% for roll, 98.535% for pitch, 44.81% for yaw, and 22.8% for altitude under normal flight conditions. It also reduces the tracking mean squared errors by 99.05% for roll, 99.26% for pitch, 40.18% for yaw, and 99.998% for altitude under trajectory tracking flight conditions in the presence of external disturbances. Therefore, the proposed controller can efficiently follow paths in the presence of parameter uncertainties, input variation, and external disturbances. . Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition)
21 pages, 2522 KB  
Article
A Reinforcement Learning-Based Adaptive Grey Wolf Optimizer for Simultaneous Arrival in Manned/Unmanned Aerial Vehicle Dynamic Cooperative Trajectory Planning
by Wei Jia, Lei Lv, Ruizhi Duan, Tianye Sun and Wei Sun
Drones 2025, 9(10), 723; https://doi.org/10.3390/drones9100723 - 17 Oct 2025
Viewed by 527
Abstract
Addressing the challenge of high-precision time-coordinated path planning for manned and unmanned aerial vehicle (UAV) clusters operating in complex dynamic environments during missions like high-level autonomous coordination, this paper proposes a reinforcement learning-based Adaptive Grey Wolf Optimizer (RL-GWO) method. We formulate a comprehensive [...] Read more.
Addressing the challenge of high-precision time-coordinated path planning for manned and unmanned aerial vehicle (UAV) clusters operating in complex dynamic environments during missions like high-level autonomous coordination, this paper proposes a reinforcement learning-based Adaptive Grey Wolf Optimizer (RL-GWO) method. We formulate a comprehensive multi-objective cost function integrating total flight distance, mission time, time synchronization error, and collision penalties. To solve this model, we design multiple improved GWO strategies and employ a Q-Learning framework for adaptive strategy selection. The RL-GWO algorithm is embedded within a dual-layer “global planning + dynamic replanning” framework. Simulation results demonstrate excellent convergence and robustness, achieving second-level time synchronization accuracy while satisfying complex constraints. In dynamic scenarios, the method rapidly generates safe evasion paths while maintaining cluster coordination, validating its practical value for heterogeneous UAV operations. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition)
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23 pages, 12369 KB  
Article
Dual-Objective Model Predictive Control for Longitudinal Tracking and Connectivity-Aware Trajectory Optimization of Fixed-Wing UAVs
by Abdurrahman Talha Yildiz and Kemal Keskin
Drones 2025, 9(10), 719; https://doi.org/10.3390/drones9100719 - 16 Oct 2025
Viewed by 288
Abstract
This paper presents a dual-objective Model Predictive Control (MPC) framework for fixed-wing unmanned aerial vehicles (UAVs). The framework was designed with two goals in mind: improving longitudinal motion control and optimizing the flight trajectory when connectivity and no-fly zone constraints are present. A [...] Read more.
This paper presents a dual-objective Model Predictive Control (MPC) framework for fixed-wing unmanned aerial vehicles (UAVs). The framework was designed with two goals in mind: improving longitudinal motion control and optimizing the flight trajectory when connectivity and no-fly zone constraints are present. A multi-input–multi-output model derived from NASA’s Generic Transport Model (T-2) was used and linearized for controller design. We compared the MPC controller with a Linear Quadratic Regulator (LQR) in MATLAB simulations. The results showed that MPC reached the reference values faster, with less overshoot and phase error, particularly under sinusoidal reference inputs. These differences became even more evident when the UAV had to fly in windy conditions. Trajectory optimization was carried out using the CasADi framework, which allowed us to evaluate paths that balance two competing requirements: reaching the target quickly and maintaining cellular connectivity. We observed that changing the weights of the cost function had a strong influence on the trade-off between direct flight and reliable communication, especially when multiple base stations and no-fly zones were included. Although the study was limited to simulations at constant altitude, the results suggest that MPC can serve as a practical tool for UAV missions that demand both accurate flight control and robust connectivity. Future work will extend the framework to more complete models and experimental validation. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition)
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21 pages, 5727 KB  
Article
Model-in-the-Loop Design and Flight Test Validation of Flight Control Laws for a Small Fixed-Wing UAV
by Ting-Ju Shen and Chieh-Li Chen
Drones 2025, 9(9), 624; https://doi.org/10.3390/drones9090624 - 4 Sep 2025
Cited by 1 | Viewed by 741
Abstract
This study provides an experimentally validated workflow for the development and model-in-the-loop (MIL) validation of flight control laws for a small, low-cost fixed-wing UAV within a model-based design (MBD) framework, addressing the limitation that previous workflow demonstrations largely remain conceptual or simulation-only and [...] Read more.
This study provides an experimentally validated workflow for the development and model-in-the-loop (MIL) validation of flight control laws for a small, low-cost fixed-wing UAV within a model-based design (MBD) framework, addressing the limitation that previous workflow demonstrations largely remain conceptual or simulation-only and that systematic processes for low-cost UAVs are lacking. A key advantage is that control law methods or parameters can be determined prior to flight testing, avoiding on-site tuning, a major challenge in UAV deployment. The Skysurfer X8 UAV served as the experimental platform. Linearized dynamic models were derived to design rate and attitude controllers using frequency-domain techniques, where loop shaping was applied to meet U.S. military flight quality standards. The control algorithms were validated in an MIL environment, enabling early evaluation of control logic, dynamic response, and robustness under idealized and perturbed conditions. Following MIL verification, the control logic was generated via Simulink Coder and deployed on a Pixhawk 6C flight controller with the PX4 autopilot. Flight test results on the Skysurfer X8 showed good agreement with MIL simulations, confirming the reliability and consistency of the proposed methodology in both simulated and real domains, while also demonstrating a systematic workflow that fills a practical gap in low-cost UAV development. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition)
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19 pages, 9202 KB  
Article
Fuzzy Adaptive Fixed-Time Bipartite Consensus Self-Triggered Control for Multi-QUAVs with Deferred Full-State Constraints
by Chenglin Wu, Shuai Song, Xiaona Song and Heng Shi
Drones 2025, 9(8), 591; https://doi.org/10.3390/drones9080591 - 20 Aug 2025
Viewed by 549
Abstract
This paper investigates the interval type-2 (IT2) fuzzy adaptive fixed-time bipartite consensus self-triggered control for multiple quadrotor unmanned aerial vehicles with deferred full-state constraints and input saturation under cooperative-antagonistic interactions. First, a uniform nonlinear transformation function, incorporating a shifting function, is constructed to [...] Read more.
This paper investigates the interval type-2 (IT2) fuzzy adaptive fixed-time bipartite consensus self-triggered control for multiple quadrotor unmanned aerial vehicles with deferred full-state constraints and input saturation under cooperative-antagonistic interactions. First, a uniform nonlinear transformation function, incorporating a shifting function, is constructed to achieve the deferred asymmetric constraints on the vehicle states and eliminate the restrictions imposed by feasibility criteria. Notably, the proposed framework provides a unified solution for unconstrained, constant/time-varying, and symmetric/asymmetric constraints without necessitating controller reconfiguration. By employing interval type-2 fuzzy logic systems and an improved self-triggered mechanism, an IT2 fuzzy adaptive fixed-time self-triggered controller is designed to allow the control signals to perform on-demand self-updating without the need for additional hardware monitors, effectively mitigating bandwidth over-consumption. Stability analysis indicates that all states in the closed-loop attitude system are fixed-time bounded while strictly adhering to deferred time-varying constraints. Finally, illustrative examples are presented to validate the effectiveness of the proposed control scheme. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition)
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