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Drones
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Path Planning, Trajectory Tracking and Guidance for UAVs
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Path Planning, Trajectory Tracking and Guidance for UAVs

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

Deadline for manuscript submissions: closed (20 October 2024) | Viewed by 30896

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 Precision Instruments, Tsinghua University, Beijing 100190, China
Interests: automatic control; flight control; unmanned system
Special Issues, Collections and Topics in MDPI journals
Dr. Zheng Chen

E-Mail Website
Guest Editor
School of Aeronautics and Astronautics, Zhejiang University, Zhejiang 310058, China
Interests: guidance, navigation, and control; flight dy-namics 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; intel-ligent 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 ofUnmanned 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 successful completion 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 these 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.

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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Related Special Issue

Published Papers (15 papers)

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Research

22 pages, 18833 KiB  
Article
Cooperative Path Planning for Multi-UAVs with Time-Varying Communication and Energy Consumption Constraints
by Jia Guo, Minggang Gan and Kang Hu
Drones 2024, 8(11), 654; https://doi.org/10.3390/drones8110654 - 7 Nov 2024
Viewed by 851
Abstract
In the field of Unmanned Aerial Vehicle (UAV) path planning, designing efficient, safe, and feasible trajectories in complex, dynamic environments poses substantial challenges. Traditional optimization methods often struggle to address the multidimensional nature of these problems, particularly when considering constraints like obstacle avoidance, [...] Read more.
In the field of Unmanned Aerial Vehicle (UAV) path planning, designing efficient, safe, and feasible trajectories in complex, dynamic environments poses substantial challenges. Traditional optimization methods often struggle to address the multidimensional nature of these problems, particularly when considering constraints like obstacle avoidance, energy efficiency, and real-time responsiveness. In this paper, we propose a novel algorithm, Dimensional Learning Strategy and Spherical Motion-based Particle Swarm Optimization (DLS-SMPSO), specifically designed to handle the unique constraints and requirements of cooperative path planning for Multiple UAVs (Multi-UAVs). By encoding particle positions as motion paths in spherical coordinates, the algorithm offers a natural and effective approach to navigating multidimensional search spaces. The incorporation of a Dimensional Learning Strategy (DLS) enhances performance by minimizing particle oscillations and allowing each particle to learn valuable information from the global best solution on a dimension-by-dimension basis. Extensive simulations validate the effectiveness of the DLS-SMPSO algorithm, demonstrating its capability to consistently generate optimal paths. The proposed algorithm outperforms other metaheuristic optimization algorithms, achieving a feasibility ratio as high as 97%. The proposed solution is scalable, adaptable, and suitable for real-time implementation, making it an excellent choice for a broad range of cooperative multi-UAV applications. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs)
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24 pages, 10962 KiB  
Article
A Multi-Waypoint Motion Planning Framework for Quadrotor Drones in Cluttered Environments
by Delong Shi, Jinrong Shen, Mingsheng Gao and Xiaodong Yang
Drones 2024, 8(8), 414; https://doi.org/10.3390/drones8080414 - 22 Aug 2024
Viewed by 1143
Abstract
In practical missions, quadrotor drones frequently face the challenge of navigating through multiple predetermined waypoints in cluttered environments where the sequence of the waypoints is not specified. This study presents a comprehensive multi-waypoint motion planning framework for quadrotor drones, comprising multi-waypoint trajectory planning [...] Read more.
In practical missions, quadrotor drones frequently face the challenge of navigating through multiple predetermined waypoints in cluttered environments where the sequence of the waypoints is not specified. This study presents a comprehensive multi-waypoint motion planning framework for quadrotor drones, comprising multi-waypoint trajectory planning and waypoint sequencing. To generate a trajectory that follows a specified sequence of waypoints, we integrate uniform B-spline curves with a bidirectional A* search to produce a safe, kinodynamically feasible initial trajectory. Subsequently, we model the optimization problem as a quadratically constrained quadratic program (QCQP) to enhance the trackability of the trajectory. Throughout this process, a replanning strategy is designed to ensure the traversal of multiple waypoints. To accurately determine the shortest flight time waypoint sequence, the fast marching (FM) method is utilized to efficiently establish the cost matrix between waypoints, ensuring consistency with the constraints and objectives of the planning method. Ant colony optimization (ACO) is then employed to solve this variant of the traveling salesman problem (TSP), yielding the sequence with the lowest temporal cost. The framework’s performance was validated in various complex simulated environments, demonstrating its efficacy as a robust solution for autonomous quadrotor drone navigation. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs)
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32 pages, 17401 KiB  
Article
Unmanned Aerial Vehicle Obstacle Avoidance Based Custom Elliptic Domain
by Yong Liao, Yuxin Wu, Shichang Zhao and Dan Zhang
Drones 2024, 8(8), 397; https://doi.org/10.3390/drones8080397 - 15 Aug 2024
Viewed by 1244
Abstract
The velocity obstacles (VO) method is widely employed in real-time obstacle avoidance research for UAVs due to its succinct mathematical foundation and rapid, dynamic planning abilities. Traditionally, VO assumes a circle protection domain with a fixed radius, leading to issues such as excessive [...] Read more.
The velocity obstacles (VO) method is widely employed in real-time obstacle avoidance research for UAVs due to its succinct mathematical foundation and rapid, dynamic planning abilities. Traditionally, VO assumes a circle protection domain with a fixed radius, leading to issues such as excessive conservatism of obstacle avoidance areas, longer detour paths, and unnecessary avoidance angles. To overcome these challenges, this paper firstly reviews the fundamentals and pre-existing defects of the VO methodology. Next, we explore a scenario involving UAVs in head-on conflicts and introduce an elliptic velocity obstacle method tailored to the UAV’s current flight state. This method connects the protection domain size directly to the UAV’s flight state, transitioning from the conventional circle domain to a more efficient elliptic domain. Additionally, to manage the computational demands of Minkowski sums and velocity obstacle cones, an approximation algorithm for discretizing elliptic boundary points is introduced. A strategy to mitigate unilateral velocity oscillation had is developed. Comparative validation simulations in MATLAB R2022a confirm that, based on the experimental results for the first 10 s, the apex angle of the velocity obstacle cone for the elliptical domain is, on average, reduced by 0.1733 radians compared to the circular domain per unit simulation time interval, saving an airspace area of 13,292 square meters and reducing the detour distance by 14.92 m throughout the obstacle avoidance process, facilitating navigation in crowded situations and improving airspace utilization. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs)
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21 pages, 964 KiB  
Article
A Heuristic Routing Algorithm for Heterogeneous UAVs in Time-Constrained MEC Systems
by Long Chen, Guangrui Liu, Xia Zhu and Xin Li
Drones 2024, 8(8), 379; https://doi.org/10.3390/drones8080379 - 6 Aug 2024
Cited by 1 | Viewed by 897
Abstract
The rapid proliferation of Internet of Things (IoT) ground devices (GDs) has created an unprecedented demand for computing resources and real-time data-processing capabilities. Integrating unmanned aerial vehicles (UAVs) into Mobile Edge Computing (MEC) emerges as a promising solution to bring computation and storage [...] Read more.
The rapid proliferation of Internet of Things (IoT) ground devices (GDs) has created an unprecedented demand for computing resources and real-time data-processing capabilities. Integrating unmanned aerial vehicles (UAVs) into Mobile Edge Computing (MEC) emerges as a promising solution to bring computation and storage closer to the data sources. However, UAV heterogeneity and the time window constraints for task execution pose a significant challenge. This paper addresses the multiple heterogeneity UAV routing problem in MEC environments, modeling it as a multi-traveling salesman problem (MTSP) with soft time constraints. We propose a two-stage heuristic algorithm, heterogeneous multiple UAV routing (HMUR). The approach first identifies task areas (TAs) and optimal hovering positions for the UAVs and defines an effective fitness measurement to handle UAV heterogeneity. A novel scoring function further refines the path determination, prioritizing real-time task compliance to enhance Quality of Service (QoS). The simulation results demonstrate that our proposed HMUR method surpasses the existing baseline algorithms on multiple metrics, validating its effectiveness in optimizing resource scheduling in MEC environments. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs)
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25 pages, 8137 KiB  
Article
Research on Unmanned Aerial Vehicle (UAV) Visual Landing Guidance and Positioning Algorithms
by Xiaoxiong Liu, Wanhan Xue, Xinlong Xu, Minkun Zhao and Bin Qin
Drones 2024, 8(6), 257; https://doi.org/10.3390/drones8060257 - 12 Jun 2024
Cited by 2 | Viewed by 1485
Abstract
Considering the weak resistance to interference and generalization ability of traditional UAV visual landing navigation algorithms, this paper proposes a deep-learning-based approach for airport runway line detection and fusion of visual information with IMU for localization. Firstly, a coarse positioning algorithm based on [...] Read more.
Considering the weak resistance to interference and generalization ability of traditional UAV visual landing navigation algorithms, this paper proposes a deep-learning-based approach for airport runway line detection and fusion of visual information with IMU for localization. Firstly, a coarse positioning algorithm based on YOLOX is designed for airport runway localization. To meet the requirements of model accuracy and inference speed for the landing guidance system, regression loss functions, probability prediction loss functions, activation functions, and feature extraction networks are designed. Secondly, a deep-learning-based runway line detection algorithm including feature extraction, classification prediction and segmentation networks is designed. To create an effective detection network, we propose efficient loss function and network evaluation methods Finally, a visual/inertial navigation system is established based on constant deformation for visual localization. The relative positioning results are fused and optimized with Kalman filter algorithms. Simulation and flight experiments demonstrate that the proposed algorithm exhibits significant advantages in terms of localization accuracy, real-time performance, and generalization ability, and can provide accurate positioning information during UAV landing processes. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs)
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18 pages, 17410 KiB  
Article
HHPSO: A Heuristic Hybrid Particle Swarm Optimization Path Planner for Quadcopters
by Jiabin Lou, Rong Ding and Wenjun Wu
Drones 2024, 8(6), 221; https://doi.org/10.3390/drones8060221 - 28 May 2024
Viewed by 1238
Abstract
Path planning for quadcopters has been proven to be one kind of NP-hard problem with huge search space and tiny feasible solution range. Metaheuristic algorithms are widely used in such types of problems for their flexibility and effectiveness. Nevertheless, most of them cannot [...] Read more.
Path planning for quadcopters has been proven to be one kind of NP-hard problem with huge search space and tiny feasible solution range. Metaheuristic algorithms are widely used in such types of problems for their flexibility and effectiveness. Nevertheless, most of them cannot meet the needs in terms of efficiency and suffer from the limitations of premature convergence and local minima. This paper proposes a novel algorithm named Heuristic Hybrid Particle Swarm Optimization (HHPSO) to address the path planning problem. On the heuristic side, we use the control points of cubic b-splines as variables instead of waypoints and establish some heuristic rules during algorithm initialization to generate higher-quality particles. On the hybrid side, we introduce an iteration-varying penalty term to shrink the search range gradually, a Cauchy mutation operator to improve the exploration ability, and an injection operator to prevent population homogenization. Numerical simulations, physical model-based simulations, and a real-world experiment demonstrate the proposed algorithm’s superiority, effectiveness and robustness. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs)
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17 pages, 6884 KiB  
Article
Extended State Observer-Based Sliding-Mode Control for Aircraft in Tight Formation Considering Wake Vortices and Uncertainty
by Ruiping Zheng, Qi Zhu, Shan Huang, Zhihui Du, Jingping Shi and Yongxi Lyu
Drones 2024, 8(4), 165; https://doi.org/10.3390/drones8040165 - 21 Apr 2024
Cited by 3 | Viewed by 1204
Abstract
The tight formation of unmanned aerial vehicles (UAVs) provides numerous advantages in practical applications, increasing not only their range but also their efficiency during missions. However, the wingman aerodynamics are affected by the tail vortices generated by the leading aircraft in a tight [...] Read more.
The tight formation of unmanned aerial vehicles (UAVs) provides numerous advantages in practical applications, increasing not only their range but also their efficiency during missions. However, the wingman aerodynamics are affected by the tail vortices generated by the leading aircraft in a tight formation, resulting in unpredictable interference. In this study, a mathematical model of wake vortex was developed, and the aerodynamic characteristics of a tight formation were simulated using Xflow software. A robust control method for tight formations was constructed, in which the disturbance is first estimated with an extended state observer, and then a sliding mode controller (SMC) was designed, enabling the wingman to accurately track the position under conditions of wake vortex from the leading aircraft. The stability of the designed controller was confirmed. Finally, the controller was simulated and verified in mathematical simulation and semi-physical simulation platforms, and the experimental results showed that the controller has high tight formation accuracy and is robust. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs)
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22 pages, 725 KiB  
Article
Iterative Trajectory Planning and Resource Allocation for UAV-Assisted Emergency Communication with User Dynamics
by Zhilan Zhang, Yufeng Wang, Yizhe Luo, Hang Zhang, Xiaorong Zhang and Wenrui Ding
Drones 2024, 8(4), 149; https://doi.org/10.3390/drones8040149 - 11 Apr 2024
Viewed by 2219
Abstract
The demand for air-to-ground communication has surged in recent years, underscoring the significance of unmanned aerial vehicles (UAVs) in enhancing mobile communication, particularly in emergency scenarios due to their deployment efficiency and flexibility. In situations such as emergency cases, UAVs can function as [...] Read more.
The demand for air-to-ground communication has surged in recent years, underscoring the significance of unmanned aerial vehicles (UAVs) in enhancing mobile communication, particularly in emergency scenarios due to their deployment efficiency and flexibility. In situations such as emergency cases, UAVs can function as efficient temporary aerial base stations and enhance communication quality in instances where terrestrial base stations are incapacitated. Trajectory planning and resource allocation of UAVs continue to be vital techniques, while a relatively limited number of algorithms account for the dynamics of ground users. This paper focuses on emergency communication scenarios such as earthquakes, proposing an innovative path planning and resource allocation algorithm. The algorithm leverages a multi-stage subtask iteration approach, inspired by the block coordinate descent technique, to address the challenges presented in such critical environments. In this study, we establish an air-to-ground communication model, subsequently devising a strategy for user dynamics. This is followed by the introduction of a joint scheduling process for path and resource allocation, named ISATR (iterative scheduling algorithm of trajectory and resource). This process encompasses highly interdependent decision variables, such as location, bandwidth, and power resources. For mobile ground users, we employ the cellular automata (CA) method to forecast the evacuation trajectory. This algorithm successfully maintains data communication in the emergency-stricken area and enhances the communication quality through bandwidth division and power control which varies with time. The effectiveness of our algorithm is validated by evaluating the average throughput with different parameters in various simulation conditions and by using several heuristic methods as a contrast. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs)
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18 pages, 26777 KiB  
Article
Topological Map-Based Autonomous Exploration in Large-Scale Scenes for Unmanned Vehicles
by Ziyu Cao, Zhihui Du and Jianhua Yang
Drones 2024, 8(4), 124; https://doi.org/10.3390/drones8040124 - 27 Mar 2024
Cited by 2 | Viewed by 1986
Abstract
Robot autonomous exploration is a challenging and valuable research field that has attracted widespread research interest in recent years. However, existing methods often encounter problems such as incomplete exploration, repeated exploration paths, and low exploration efficiency when facing large-scale scenes. Considering that many [...] Read more.
Robot autonomous exploration is a challenging and valuable research field that has attracted widespread research interest in recent years. However, existing methods often encounter problems such as incomplete exploration, repeated exploration paths, and low exploration efficiency when facing large-scale scenes. Considering that many indoor and outdoor scenes usually have a prior topological map, such as road navigation maps, satellite road network maps, indoor computer-aided design (CAD) maps, etc., this paper incorporated this information into the autonomous exploration framework and proposed an innovative topological map-based autonomous exploration method for large-scale scenes. The key idea of the proposed method is to plan exploration paths with long-term benefits by tightly merging the information between robot-collected and prior topological maps. The exploration path follows a global exploration strategy but prioritizes exploring scenes outside the prior information, thereby preventing the robot from revisiting explored areas and avoiding the duplication of any effort. Furthermore, to improve the stability of exploration efficiency, the exploration path is further refined by assessing the cost and reward of each candidate viewpoint through a fast method. Simulation experimental results demonstrated that the proposed method outperforms state-of-the-art autonomous exploration methods in efficiency and stability and is more suitable for exploration in large-scale scenes. Real-world experimentation has also proven the effectiveness of our proposed method. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs)
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23 pages, 10860 KiB  
Article
Reducing Oscillations for Obstacle Avoidance in a Dense Environment Using Deep Reinforcement Learning and Time-Derivative of an Artificial Potential Field
by Zhilong Xi, Haoran Han, Jian Cheng and Maolong Lv
Drones 2024, 8(3), 85; https://doi.org/10.3390/drones8030085 - 1 Mar 2024
Cited by 2 | Viewed by 2243
Abstract
Obstacle avoidance plays a crucial role in ensuring the safe path planning of quadrotor unmanned aerial vehicles (QUAVs). In this study, we propose a hierarchical framework for obstacle avoidance, which combines the use of artificial potential field (APF) and deep reinforcement learning (DRL) [...] Read more.
Obstacle avoidance plays a crucial role in ensuring the safe path planning of quadrotor unmanned aerial vehicles (QUAVs). In this study, we propose a hierarchical framework for obstacle avoidance, which combines the use of artificial potential field (APF) and deep reinforcement learning (DRL) for training low-level motion controllers. Unlike traditional potential field methods, our approach modifies the state information received by the motion controllers using the outputs of the APF path planner. Specifically, the assumed target position is pushed away from obstacles, resulting in adjustments to the perceived position errors. Additionally, we address path oscillations by incorporating the target’s velocity information, which is calculated based on the time-derivative of the repulsive force. Experimental results have validated the effectiveness of our proposed framework in avoiding collisions with obstacles and reducing oscillations. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs)
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32 pages, 9220 KiB  
Article
A Multi-Regional Path-Planning Method for Rescue UAVs with Priority Constraints
by Lexu Du, Yankai Fan, Mingzhen Gui and Dangjun Zhao
Drones 2023, 7(12), 692; https://doi.org/10.3390/drones7120692 - 29 Nov 2023
Cited by 3 | Viewed by 2654
Abstract
This study focuses on the path-planning problem of rescue UAVs with regional detection priority. Initially, we propose a mixed-integer programming model that integrates coverage path planning (CPP) and the hierarchical traveling salesman problem (HTSP) to address multi-regional path planning under priority constraints. For [...] Read more.
This study focuses on the path-planning problem of rescue UAVs with regional detection priority. Initially, we propose a mixed-integer programming model that integrates coverage path planning (CPP) and the hierarchical traveling salesman problem (HTSP) to address multi-regional path planning under priority constraints. For intra-regional path planning, we present an enhanced method for acquiring reciprocating flight paths to ensure complete coverage of convex polygonal regions with shorter flight paths when a UAV is equipped with sensors featuring circular sampling ranges. An additional comparison was made for spiral flight paths, and second-order Bezier curves were employed to optimize both sets of paths. This optimization not only reduced the path length but also enhanced the ability to counteract inherent drone jitter. Additionally, we propose a variable neighborhood descent algorithm based on K-nearest neighbors to solve the inter-regional access order path-planning problem with priority. We establish parameters for measuring distance and evaluating the priority order of UAV flight paths. Simulation and experiment results demonstrate that the proposed algorithm can effectively assist UAVs in performing path-planning tasks with priority constraints, enabling faster information collection in important areas and facilitating quick exploration of three-dimensional characteristics in unknown disaster areas by rescue workers. This algorithm significantly enhances the safety of rescue workers and optimizes crucial rescue times in key areas. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs)
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18 pages, 3480 KiB  
Article
Multi-Constrained Geometric Guidance Law with a Data-Driven Method
by Xinghui Yan, Yuzhong Tang, Yulei Xu, Heng Shi and Jihong Zhu
Drones 2023, 7(10), 639; https://doi.org/10.3390/drones7100639 - 18 Oct 2023
Viewed by 2111
Abstract
A data-driven geometric guidance method is proposed for the multi-constrained guidance problem of variable-velocity unmanned aerial vehicles (UAVs). Firstly, a two-phase flight trajectory based on a log-aesthetic space curve (LASC) is designed. The impact angle is satisfied by a specified straight-line segment. The [...] Read more.
A data-driven geometric guidance method is proposed for the multi-constrained guidance problem of variable-velocity unmanned aerial vehicles (UAVs). Firstly, a two-phase flight trajectory based on a log-aesthetic space curve (LASC) is designed. The impact angle is satisfied by a specified straight-line segment. The impact time is controlled by adjusting the phase switching point. Secondly, a deep neural network is trained offline to establish the mapping relationship between the initial conditions and desired trajectory parameters. Based on this mapping network, the desired flight trajectory can be generated rapidly and precisely. Finally, the pure pursuit and line-of-sight (PLOS) algorithm is employed to generate guidance commands. The numerical simulation results validate the effectiveness and superiority of the proposed method in terms of impact time and angle control under time-varying velocity. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs)
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21 pages, 4141 KiB  
Article
A Robust Disturbance-Rejection Controller Using Model Predictive Control for Quadrotor UAV in Tracking Aggressive Trajectory
by Zhixiong Xu, Li Fan, Wei Qiu, Guangwei Wen and Yunhan He
Drones 2023, 7(9), 557; https://doi.org/10.3390/drones7090557 - 29 Aug 2023
Cited by 7 | Viewed by 1901
Abstract
A robust controller for the waypoint tracking of a quadrotor unmanned aerial vehicle (UAV) is proposed in this paper, in which position control and attitude control are effectively decoupled. Model predictive control (MPC) is employed in the position controller. The constraints of motors [...] Read more.
A robust controller for the waypoint tracking of a quadrotor unmanned aerial vehicle (UAV) is proposed in this paper, in which position control and attitude control are effectively decoupled. Model predictive control (MPC) is employed in the position controller. The constraints of motors are imposed on the state and input variables of the optimization equation. This design effectively mitigates the nonlinearity of the attitude loop and enhances the planning efficiency of the position controller. The attitude controller is designed using a nonlinear and robust control law based on SO(3) space, which enables continuous control on the SO(3) manifold. By extending the differential flatness of the quadrotor-UAV to the angular acceleration level, the mapping of the control reference from the position controller to the attitude controller is achieved. Simulations are carried out to demonstrate the capability of the proposed controller. In the simulations, multiple aggressive flight trajectories and severe external disturbances are designed. The results show that the controller is robust, with superior accuracy in tracking aggressive trajectories. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs)
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23 pages, 8611 KiB  
Article
Distributed Multi-Target Search and Surveillance Mission Planning for Unmanned Aerial Vehicles in Uncertain Environments
by Xiao Zhang, Wenjie Zhao, Changxuan Liu and Jun Li
Drones 2023, 7(6), 355; https://doi.org/10.3390/drones7060355 - 28 May 2023
Cited by 5 | Viewed by 2654
Abstract
In this paper, a distributed, autonomous, cooperative mission-planning (DACMP) approach was proposed to focus on the problem of the real-time cooperative searching and surveillance of multiple unmanned aerial vehicles (multi-UAVs) with threats in uncertain and highly dynamic environments. To deal with this problem, [...] Read more.
In this paper, a distributed, autonomous, cooperative mission-planning (DACMP) approach was proposed to focus on the problem of the real-time cooperative searching and surveillance of multiple unmanned aerial vehicles (multi-UAVs) with threats in uncertain and highly dynamic environments. To deal with this problem, a time-varying probabilistic grid graph was designed to represent the perception of a target based on its a priori dynamics. A heuristic search strategy based on pyramidal maps was also proposed. Using map information at different scales makes it easier to integrate local and global information, thereby improving the search capability of UAVs, which has not been previously considered. Moreover, we proposed an adaptive distributed task assignment method for cooperative search and surveillance tasks by considering the UAV motion environment as a potential field and modeling the effects of uncertain maps and targets on candidate solutions through potential field values. The results highlight the advantages of search task execution efficiency. In addition, simulations of different scenarios show that the proposed approach can provide a feasible solution for multiple UAVs in different situations and is flexible and stable in time-sensitive environments. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs)
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21 pages, 2032 KiB  
Article
Safe Reinforcement Learning for Transition Control of Ducted-Fan UAVs
by Yanbo Fu, Wenjie Zhao and Liu Liu
Drones 2023, 7(5), 332; https://doi.org/10.3390/drones7050332 - 22 May 2023
Cited by 2 | Viewed by 2354
Abstract
Ducted-fan tail-sitter unmanned aerial vehicles (UAVs) provide versatility and unique benefits, attracting significant attention in various applications. This study focuses on developing a safe reinforcement learning method for back-transition control between level flight mode and hover mode for ducted-fan tail-sitter UAVs. Our method [...] Read more.
Ducted-fan tail-sitter unmanned aerial vehicles (UAVs) provide versatility and unique benefits, attracting significant attention in various applications. This study focuses on developing a safe reinforcement learning method for back-transition control between level flight mode and hover mode for ducted-fan tail-sitter UAVs. Our method enables transition control with a minimal altitude change and transition time while adhering to the velocity constraint. We employ the Trust Region Policy Optimization, Proximal Policy Optimization with Lagrangian, and Constrained Policy Optimization (CPO) algorithms for controller training, showcasing the superiority of the CPO algorithm and the necessity of the velocity constraint. The transition trajectory achieved using the CPO algorithm closely resembles the optimal trajectory obtained via the well-known GPOPS-II software with the SNOPT solver. Meanwhile, the CPO algorithm also exhibits strong robustness under unknown perturbations of UAV model parameters and wind disturbance. Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs)
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