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Drones
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Path Planning, Trajectory Tracking and Guidance for UAVs: 2nd Edition
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Path Planning, Trajectory Tracking and Guidance for UAVs: 2nd Edition

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drone Communications".

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 9590

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 aspects for the autonomous operations of Unmanned Aerial Vehicles (UAVs). These processes involve the determination of the optimal path, implementation of the planned path, and 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, the implementation of planned paths while considering external factors such as wind and turbulence, along with real-time guidance adjustment, ensures UAV’s 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 aims to 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
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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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (10 papers)

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Research

Jump to: Review

23 pages, 2902 KiB  
Article
The Equal-Time Waypoint Method: A Multi-AUV Path Planning Approach That Is Based on Velocity Variation
by Chenxin Yin, Kai Shi and Hailong Wang
Drones 2025, 9(5), 336; https://doi.org/10.3390/drones9050336 - 29 Apr 2025
Viewed by 242
Abstract
In collaborative operations of multiple autonomous underwater vehicles (AUVs), the complexity of underwater environments and limited onboard energy make environmental adaptation and energy efficiency critical metrics for evaluating path quality. This paper addresses path conflict resolution in multi-AUV path planning by proposing an [...] Read more.
In collaborative operations of multiple autonomous underwater vehicles (AUVs), the complexity of underwater environments and limited onboard energy make environmental adaptation and energy efficiency critical metrics for evaluating path quality. This paper addresses path conflict resolution in multi-AUV path planning by proposing an equal-time waypoint planning method. The approach involves randomly selecting equal-time waypoints in free space and generating path encoding sequences for each AUV. These path encodings are then optimized through four modules, considering both path smoothness and adaptability to ocean currents. The resulting paths comply with kinematic constraints while achieving reduced energy consumption. The method enables velocity adjustments across different segments to prevent conflicts. Simulation results demonstrate the feasibility of this approach in resolving multi-AUV path conflicts with low energy expenditure. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 2nd Edition)
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24 pages, 6970 KiB  
Article
Two-Stage Hierarchical 4D Low-Risk Trajectory Planning for Urban Air Logistics
by Yuan Zheng, Yichao Li, Jie Cheng, Chenglong Li and Shichen Hu
Drones 2025, 9(4), 267; https://doi.org/10.3390/drones9040267 - 31 Mar 2025
Viewed by 419
Abstract
The rapid development of the drone industry has facilitated the emergence of concepts such as urban air mobility (UAM), driving a wave of air logistics in urban very low-level (VLL) airspace. However, existing trajectory planning algorithms do not adequately consider the ground risks [...] Read more.
The rapid development of the drone industry has facilitated the emergence of concepts such as urban air mobility (UAM), driving a wave of air logistics in urban very low-level (VLL) airspace. However, existing trajectory planning algorithms do not adequately consider the ground risks and secondary conflicts arising from high-density operations in urban VLL airspace. To address these challenges, this paper proposes a two-stage hierarchical 4D trajectory planning method to minimize multiple risks. Specifically, the method consists of a risk-aware global planning module (RAGPM) for preflight trajectory planning and a non-secondary conflict local planning module (NCLPM) for in-flight conflict avoidance. Consequently, low-risk trajectory without secondary conflict can be found in complex environments with high-density operations, as illustrated by extensive experiments. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 2nd Edition)
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19 pages, 2535 KiB  
Article
A Novel HGW Optimizer with Enhanced Differential Perturbation for Efficient 3D UAV Path Planning
by Lei Lv, Hongjuan Liu, Ruofei He, Wei Jia and Wei Sun
Drones 2025, 9(3), 212; https://doi.org/10.3390/drones9030212 - 16 Mar 2025
Viewed by 328
Abstract
In general, path planning for unmanned aerial vehicles (UAVs) is modeled as a challenging optimization problem that is critical to ensuring efficient UAV mission execution. The challenge lies in the complexity and uncertainty of flight scenarios, particularly in three-dimensional scenarios. In this study, [...] Read more.
In general, path planning for unmanned aerial vehicles (UAVs) is modeled as a challenging optimization problem that is critical to ensuring efficient UAV mission execution. The challenge lies in the complexity and uncertainty of flight scenarios, particularly in three-dimensional scenarios. In this study, one introduces a framework for UAV path planning in a 3D environment. To tackle this challenge, we develop an innovative hybrid gray wolf optimizer (GWO) algorithm, named SDPGWO. The proposed algorithm simplifies the position update mechanism of GWO and incorporates a differential perturbation strategy into the search process, enhancing the optimization ability and avoiding local minima. Simulations conducted in various scenarios reveal that the SDPGWO algorithm excels in rapidly generating superior-quality paths for UAVs. In addition, it demonstrates enhanced robustness in handling complex 3D environments and outperforms other related algorithms in both performance and reliability. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 2nd Edition)
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20 pages, 3184 KiB  
Article
Adaptive Path Guidance Law for a Small Fixed-Wing UAS with Bounded Bank Angle
by Suhyeon Kim and Dongwon Jung
Drones 2025, 9(3), 180; https://doi.org/10.3390/drones9030180 - 28 Feb 2025
Viewed by 578
Abstract
This study deals with the path-following guidance of a fixed-wing unmanned aerial system (UAS) in conjunction with parameter adaptation. Utilizing a backstepping control design approach, a path-following control algorithm is formulated for the roll command, accounting for the approximated closed-loop roll control. The [...] Read more.
This study deals with the path-following guidance of a fixed-wing unmanned aerial system (UAS) in conjunction with parameter adaptation. Utilizing a backstepping control design approach, a path-following control algorithm is formulated for the roll command, accounting for the approximated closed-loop roll control. The inaccurate time constant is estimated by employing a parameter adaptation algorithm. The proposed guidance algorithm is first validated via the hardware-in-the-loop simulation environment, followed by flight tests on an actual UAV platform to demonstrate that both tracking performance and control robustness are improved over various shape of reference paths. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 2nd Edition)
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21 pages, 707 KiB  
Article
Integrative Path Planning for Multi-Rotor Logistics UAVs Considering UAV Dynamics, Energy Efficiency, and Obstacle Avoidance
by Kunpeng Wu, Juncong Lan, Shaofeng Lu, Chaoxian Wu, Bingjian Liu and Zenghao Lu
Drones 2025, 9(2), 93; https://doi.org/10.3390/drones9020093 - 25 Jan 2025
Cited by 1 | Viewed by 717
Abstract
Due to their high flexibility, low cost, and energy-saving advantages, applying Unmanned Aerial Vehicles (UAVs) in logistics is a promising field to achieve better social and economic benefits. Since UAVs’ energy storage capacity is generally low, it is essential to reduce energy costs [...] Read more.
Due to their high flexibility, low cost, and energy-saving advantages, applying Unmanned Aerial Vehicles (UAVs) in logistics is a promising field to achieve better social and economic benefits. Since UAVs’ energy storage capacity is generally low, it is essential to reduce energy costs to improve their system’s energy efficiency. In this paper, we proposed a novel trajectory planning framework to achieve the optimal trajectory with the minimum amount of energy consumption under the constraints of obstacles in a static environment. Based on UAV dynamics, we first derived the required power functions of multi-rotor UAVs in vertical and horizontal flight. To generate a feasible trajectory, we first adopted the A* algorithm to find a path and developed a safe flight corridor for the UAV to fly across by expanding the waypoints against the environment, and then proposed a time-discretization method to formulate the trajectory generation problem and solve it by the convex optimization algorithm. The optimization results in a static environment with obstacles demonstrated that the proposed method could efficiently and effectively obtain the optimal trajectory with the minimum amount of energy consumption under different allowed mission times and payloads. The framework would promote a variety of logistics UAV applications relevant to trajectory planning. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 2nd Edition)
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39 pages, 10058 KiB  
Article
Utilizing the Finite Fourier Series to Generate Quadrotor Trajectories Through Multiple Waypoints
by Yevhenii Kovryzhenko and Ehsan Taheri
Drones 2025, 9(1), 77; https://doi.org/10.3390/drones9010077 - 20 Jan 2025
Viewed by 1016
Abstract
Motion planning is critical for ensuring precise and efficient operations of unmanned aerial vehicles (UAVs). While polynomial parameterization has been the prevailing approach, its limitations in handling complex trajectory requirements have motivated the exploration of alternative methods. This paper introduces a finite Fourier [...] Read more.
Motion planning is critical for ensuring precise and efficient operations of unmanned aerial vehicles (UAVs). While polynomial parameterization has been the prevailing approach, its limitations in handling complex trajectory requirements have motivated the exploration of alternative methods. This paper introduces a finite Fourier series (FFS)-based trajectory parameterization for UAV motion planning, highlighting its unique capability to produce piecewise infinitely differentiable trajectories. The proposed approach addresses the challenges of fixed-time minimum-snap trajectory optimization by formulating the problem as a quadratic programming (QP) problem, with an analytical solution derived for unconstrained cases. Additionally, we compare the FFS-based parameterization with the polynomial-based minimum-snap algorithm, demonstrating comparable performance across several representative trajectories while uncovering key differences in higher-order derivatives. Experimental validation of the FFS-based parameterization using an in-house quadrotor confirms the practical applicability of the FFS-based minimum-snap trajectories. The results indicate that the proposed FFS-based parameterization offers new possibilities for motion planning, especially for scenarios requiring smooth and higher-order derivative continuity at the expense of minor increase in computational cost. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 2nd Edition)
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29 pages, 3312 KiB  
Article
Enhancing Automated Maneuvering Decisions in UCAV Air Combat Games Using Homotopy-Based Reinforcement Learning
by Yiwen Zhu, Yuan Zheng, Wenya Wei and Zhou Fang
Drones 2024, 8(12), 756; https://doi.org/10.3390/drones8120756 - 13 Dec 2024
Viewed by 900
Abstract
In the field of real-time autonomous decision-making for Unmanned Combat Aerial Vehicles (UCAVs), reinforcement learning is widely used to enhance their decision-making capabilities in high-dimensional spaces. These enhanced capabilities allow UCAVs to better respond to the maneuvers of various opponents, with the win [...] Read more.
In the field of real-time autonomous decision-making for Unmanned Combat Aerial Vehicles (UCAVs), reinforcement learning is widely used to enhance their decision-making capabilities in high-dimensional spaces. These enhanced capabilities allow UCAVs to better respond to the maneuvers of various opponents, with the win rate often serving as the primary optimization metric. However, relying solely on the terminal outcome of victory or defeat as the optimization target, but without incorporating additional rewards throughout the process, poses significant challenges for reinforcement learning due to the sparse reward structure inherent in these scenarios. While algorithms enhanced with densely distributed artificial rewards show potential, they risk deviating from the primary objectives. To address these challenges, we introduce a novel approach: the homotopy-based soft actor–critic (HSAC) method. This technique gradually transitions from auxiliary tasks enriched with artificial rewards to the main task characterized by sparse rewards through homotopic paths. We demonstrate the consistent convergence of the HSAC method and its effectiveness through deployment in two distinct scenarios within a 3D air combat game simulation: attacking horizontally flying UCAVs and a combat scenario involving two UCAVs. Our experimental results reveal that HSAC significantly outperforms traditional algorithms, which rely solely on using sparse rewards or those supplemented with artificially aided rewards. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 2nd Edition)
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17 pages, 2739 KiB  
Article
MPC-Based Dynamic Trajectory Spoofing for UAVs
by Bo Hou, Zhongjie Yin, Xiaolong Jin, Zhiliang Fan and Haiyang Wang
Drones 2024, 8(10), 602; https://doi.org/10.3390/drones8100602 - 19 Oct 2024
Cited by 1 | Viewed by 1273
Abstract
Navigation spoofing has been widely utilized in unmanned aircraft vehicle (UAV) countermeasures, due to its advantages of covertness, effectiveness, and dynamic trajectory control ability. However, existing research faces two primary challenges. Firstly, sudden changes in the target UAV’s trajectory can result in a [...] Read more.
Navigation spoofing has been widely utilized in unmanned aircraft vehicle (UAV) countermeasures, due to its advantages of covertness, effectiveness, and dynamic trajectory control ability. However, existing research faces two primary challenges. Firstly, sudden changes in the target UAV’s trajectory can result in a significant degradation in the spoofing performance, which may enable the onboard inertial components to detect and identify the ongoing spoofing attempts. Secondly, gradual accumulation of control errors over time degenerates the spoofing effect. To address these problems, we propose a dynamic trajectory spoofing approach for UAVs based on model predictive control (MPC), which progressively steers the UAVs towards the predetermined trajectory of the spoofer. Simulation results demonstrate a substantial enhancement in dynamic trajectory control performance and decrease in accumulation error compared to the existing methods. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 2nd Edition)
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Review

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46 pages, 9673 KiB  
Review
Advances in UAV Path Planning: A Comprehensive Review of Methods, Challenges, and Future Directions
by Wenlong Meng, Xuegang Zhang, Lvzhuoyu Zhou, Hangyu Guo and Xin Hu
Drones 2025, 9(5), 376; https://doi.org/10.3390/drones9050376 - 16 May 2025
Viewed by 29
Abstract
Unmanned aerial vehicles (UAVs) have revolutionized fields such as monitoring, cargo delivery, precision farming, and emergency response, demonstrating remarkable flexibility and operational effectiveness. A fundamental aspect of UAV autonomy lies in route optimization, which determines efficient paths while considering factors like mission goals, [...] Read more.
Unmanned aerial vehicles (UAVs) have revolutionized fields such as monitoring, cargo delivery, precision farming, and emergency response, demonstrating remarkable flexibility and operational effectiveness. A fundamental aspect of UAV autonomy lies in route optimization, which determines efficient paths while considering factors like mission goals, safety, and power consumption. This article presents an extensive overview of methodologies for UAV route planning, including deterministic models, stochastic sampling techniques, biologically inspired methods, and integrated algorithmic frameworks. The discussion extends to their performance in various operational contexts, including stationary, moving, and three-dimensional settings. Innovative methods utilizing artificial intelligence, particularly machine learning and neural networks, are emphasized for their promise in facilitating adaptive responses to intricate, evolving environments. Furthermore, strategies focused on reducing energy usage and enabling coordinated operations among multiple drones are analyzed, addressing issues such as prolonged operation, distribution of assignments, and navigation around obstacles. Although notable advancements have been achieved, challenges like high computational demands and the need for immediate responsiveness persist. By consolidating the latest progress, this survey provides meaningful perspectives and guidance for the ongoing evolution of UAV route planning solutions. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 2nd Edition)
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52 pages, 13117 KiB  
Review
UAV Path Planning Trends from 2000 to 2024: A Bibliometric Analysis and Visualization
by Qiwu Wu, Yunchen Su, Weicong Tan, Renjun Zhan, Jiaqi Liu and Lingzhi Jiang
Drones 2025, 9(2), 128; https://doi.org/10.3390/drones9020128 - 10 Feb 2025
Cited by 1 | Viewed by 2405
Abstract
UAV path planning, as a key technology in the field of automatic control and intelligent systems, has demonstrated significant potential in various applications, including logistics and distribution, environmental monitoring, and emergency rescue. A comprehensive reassessment of the existing representative literature reveals that most [...] Read more.
UAV path planning, as a key technology in the field of automatic control and intelligent systems, has demonstrated significant potential in various applications, including logistics and distribution, environmental monitoring, and emergency rescue. A comprehensive reassessment of the existing representative literature reveals that most reviews in this field focus on specific aspects and are largely confined to methodological investigations, primarily qualitative analyses that lack empirical data to support their conclusions. To address this gap, this study employs the mapping knowledge domain (MKD) method of bibliometrics, utilizing CiteSpace, VOSviewer, and Bibliometrix R package to analyze a total of 4416 documents from the Web of Science Core Collection (WOSCC) spanning from 2000 to 2024. Through retrospective analysis and scientific knowledge mapping, we first review the development of UAV path planning and categorize it into four distinct stages. Secondly, we identify key external features of the field. Using techniques such as co-citation analysis and keyword clustering, we then identify research trends, burst papers, and hotspots. Finally, we highlight five typical application scenarios of UAV path planning. The results of the study indicate that the field of UAV path planning has made significant advancements over the past two decades, particularly since 2018. These studies encompass various disciplinary areas, underscoring the increasing necessity for the integration of multidisciplinary approaches to UAV path planning in recent years. The aim of this study is to provide researchers with a comprehensive reference and new research perspectives while offering technical guidelines for professionals working in related applications. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 2nd Edition)
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