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UAV Trajectory Generation, Optimization and Cooperative Control

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

Deadline for manuscript submissions: 8 July 2024 | Viewed by 12866

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Special Issue Editors

Prof. Dr. Kaiyu Qin

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Guest Editor

School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China
Interests: self-organizing mobile internet communication network technology; aircraft measurement and control communication technology; navigation, guidance and control technology; aircraft cluster intelligent perception and control technology; microwave and communication measurement technology and instruments; high-speed signal real-time processing technology; microwave module and component technology; new energy automation technology

Prof. Dr. Haitao Nie

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Guest Editor

School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: aircraft overall design; intelligent UAV system overall design

Dr. Yikang Yang

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Guest Editor

School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: communication & signal processing

Prof. Dr. Xue Li

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Guest Editor

School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
Interests: aircraft tracking measurement and control and inter-satellite link networking precision measurement; space information and energy fusion network and wireless power transmission; satellite navigation signal processing and distributed networking collaborative navigation; giant broadband Internet constellation network operation control and security protection

Dr. Jinliang Shao

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Guest Editor

School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
Interests: multiagent system, robust control; matrix analysis with applications in control theory

Dr. Lei Shi

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Guest Editor

School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
Interests: multiagent collaborative control; opinion dynamics of social networks; distributed localization of sensor networks
Special Issues, Collections and Topics in MDPI journals

Dr. Mengji Shi

E-Mail Website
Guest Editor

School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: design and evaluation of cooperative control algorithm for agent system and its application in aircraft cooperation

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit manuscripts to the MDPI Drones Special Issue on “UAV Trajectory Generation, Optimization and Cooperative Control”.

In recent years, the research on UAVs has attracted widespread attention due to their broad applications in daily life and military operations, including for reconnaissance, surveillance, interference, relay communications, forest fire detection, and meteorological observation. However, complex and variable missions pose challenges for UAV technology, especially the computing power limitation of onboard computers. UAVs need to quickly generate and optimize a flyable trajectory to new mission points in emergencies. Additionally, because it is difficult for a single UAV to perform missions that can satisfy all demands, the collaboration of multi-UAV systems has become an important direction for UAV technology.

This Special Issue is inspired by the applications of UAVs in complex and variable missions.

Within this context, we invite manuscripts for this Special Issue on “UAV Trajectory Generation, Optimization and Cooperative Control”. Papers are solicited in areas directly related to topics including but not limited to those listed below:

Path planning and trajectory generation for UAVs;
Trajectory optimization for UAVs;
Collision avoidance for UAVs in complex environments;
Distributed cooperative guidance, control and optimization for UAVs;
Dynamic positioning/path following/trajectory tracking/target tracking problems of UAVs.

Prof. Dr. Kaiyu Qin
Prof. Dr. Haitao Nie
Dr. Yikang Yang
Prof. Dr. Xue Li
Dr. Jinliang Shao
Dr. Lei Shi
Dr. Mengji Shi
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

UAV trajectory generation
UAV trajectory optimization
collision avoidance
multi-UAV systems and cooperative control

Published Papers (10 papers)

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Research

19 pages, 3426 KiB

Open AccessFeature PaperArticle

Intelligent Scheduling Technology of Swarm Intelligence Algorithm for Drone Path Planning

by Zhipeng Meng, Dongze Li, Yong Zhang and Haoquan Yan

Drones 2024, 8(4), 120; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8040120 - 26 Mar 2024

Viewed by 696

Abstract

Different kinds of swarm intelligence algorithm obtain superior performances in solving complex optimization problems and have been widely used in path planning of drones. Due to their own characteristics, the optimization results may vary greatly in different dynamic environments. In this paper, a scheduling technology for swarm intelligence algorithms based on deep Q-learning is proposed to intelligently select algorithms to realize 3D path planning. It builds a unique path point database and two basic principles are proposed to guide model training. Path planning and network learning are separated by the proposed separation principle and the optimal selection principle ensures convergence of the model. Aiming at the problem of reward sparsity, the comprehensive cost of each path point in the whole track sequence is regarded as a dynamic reward. Through the investigation of dynamic environment conditions such as different distances and threats, the effectiveness of the proposed method is validated. Full article

(This article belongs to the Special Issue UAV Trajectory Generation, Optimization and Cooperative Control)

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25 pages, 9288 KiB

Open AccessArticle

Modeling, Guidance, and Robust Cooperative Control of Two Quadrotors Carrying a “Y”-Shaped-Cable-Suspended Payload

by Erquan Wang, Jinyang Sun, Yuanyuan Liang, Boyu Zhou, Fangfei Jiang and Yang Zhu

Drones 2024, 8(3), 103; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8030103 - 19 Mar 2024

Viewed by 951

Abstract

This paper investigates the problem of cooperative payload delivery by two quadrotors with a novel “Y”-shaped cable that improves payload carrying and dropping efficiency. Compared with the existing “V”-shaped suspension, the proposed suspension method adds another payload swing degree of freedom to the quadrotor–payload system, making the modeling and control of such a system more challenging. In the modeling, the payload swing motion is decomposed into a forward–backward process and a lateral process, and the swing motion is then transmitted to the dynamics of the two quadrotors by converting it into disturbance cable pulling forces. A novel guidance and control framework is proposed, where a guidance law is designed to not only achieve formation transformation but also generate a local reference for the quadrotor, which does not have access to the global reference, based on which a cooperative controller is developed by incorporating an uncertainty and disturbance estimator to actively compensate for payload swing disturbance to achieve the desired formation trajectory tracking performance. A singular perturbation theory-based analysis shows that the proposed parameter mapping method, which unifies the parameter tuning of different control channels, allows us to tune a single parameter,

ε

, to quantitatively enhance both the formation control performance and system robustness. Simulation results verify the effectiveness of the proposed approach in different scenarios. Full article

(This article belongs to the Special Issue UAV Trajectory Generation, Optimization and Cooperative Control)

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22 pages, 1053 KiB

Open AccessArticle

Multiple Unmanned Aerial Vehicle Autonomous Path Planning Algorithm Based on Whale-Inspired Deep Q-Network

by Wenshan Wang, Guoyin Zhang, Qingan Da, Dan Lu, Yingnan Zhao, Sizhao Li and Dapeng Lang

Drones 2023, 7(9), 572; https://0-doi-org.brum.beds.ac.uk/10.3390/drones7090572 - 08 Sep 2023

Cited by 1 | Viewed by 1313

Abstract

In emergency rescue missions, rescue teams can use UAVs and efficient path planning strategies to provide flexible rescue services for trapped people, which can improve rescue efficiency and reduce personnel risks. However, since the task environment of UAVs is usually complex, uncertain, and communication-limited, traditional path planning methods may not be able to meet practical needs. In this paper, we introduce a whale optimization algorithm into a deep Q-network and propose a path planning algorithm based on a whale-inspired deep Q-network, which enables UAVs to search for targets faster and safer in uncertain and complex environments. In particular, we first transform the UAV path planning problem into a Markov decision process. Then, we design a comprehensive reward function considering the three factors of path length, obstacle avoidance, and energy consumption. Next, we use the main framework of the deep Q-network to approximate the Q-value function by training a deep neural network. During the training phase, the whale optimization algorithm is introduced for path exploration to generate a richer action decision experience. Finally, experiments show that the proposed algorithm can enable the UAV to autonomously plan a collision-free feasible path in an uncertain environment. And compared with classic reinforcement learning algorithms, the proposed algorithm has a better performance in learning efficiency, path planning success rate, and path length. Full article

(This article belongs to the Special Issue UAV Trajectory Generation, Optimization and Cooperative Control)

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20 pages, 1459 KiB

Open AccessArticle

Multi-Group Tracking Control for MASs of UAV with a Novel Event-Triggered Scheme

by Can Zhao, Kaibo Shi, Yiqian Tang, Jianying Xiao and Nanrong He

Drones 2023, 7(7), 474; https://0-doi-org.brum.beds.ac.uk/10.3390/drones7070474 - 18 Jul 2023

Cited by 2 | Viewed by 901

Abstract

The flight control of UAVs can be implemented and theoretically analyzed using multi-agent systems (MASs), and tracking control is one of the important control technologies. This paper studies multi-group tracking control for multi-agent systems of UAV, in which the control scheme combines event-triggered technology and impulsive theory. The advantage of multi-group tracking control lies in its ability to realize multiple groups of tracking targets and make the UAV complete multiple groups of tasks. The tracking control makes use of a novel dynamic event-triggered control (DETC) proposed in this paper, in which it can better regulate and optimize the triggering frequency by adjusting the parameters. Furthermore, several forms of network interference that may affect the safety of UAV tracking control have also been resolved. Lastly, simulations are presented with numerical examples to showcase the efficacy of the proposed tracking control. Full article

(This article belongs to the Special Issue UAV Trajectory Generation, Optimization and Cooperative Control)

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15 pages, 1226 KiB

Open AccessArticle

Synchronized Tracking Control of Dynamic System of Unmanned Rear-Wheel Vehicles Based on Dynamic Analysis

by Can Zhao, Kaibo Shi, Yiqian Tang and Jianying Xiao

Drones 2023, 7(7), 417; https://0-doi-org.brum.beds.ac.uk/10.3390/drones7070417 - 23 Jun 2023

Viewed by 758

Abstract

From the classic automatic guided vehicle system, the system of the unmanned rear-wheel drive vehicle (URWDV) based on a dynamic analysis is studied. In the URWDV system, the relationship among the position information, velocity, and the heading angular velocity of the unmanned vehicle is established in the plane coordinate system and the coordinate system centered vehicle itself. The velocity and heading angular velocity values are obtained through a dynamic analysis and are used as control parameters. The synchronized tracking control of the unmanned vehicle is realized by the control scheme of the velocity and the heading angular velocity. Finally, the simulation examples show the effectiveness of the tracking control. Full article

(This article belongs to the Special Issue UAV Trajectory Generation, Optimization and Cooperative Control)

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26 pages, 3850 KiB

Open AccessArticle

Robust Flight-Path Angle Consensus Tracking Control for Non-Minimum Phase Unmanned Fixed-Wing Aircraft Formation in the Presence of Measurement Errors

by Yang Zhu and Kaiyu Qin

Drones 2023, 7(6), 350; https://0-doi-org.brum.beds.ac.uk/10.3390/drones7060350 - 27 May 2023

Viewed by 1019

Abstract

The robust flight-path angle consensus tracking control problem for multiple unmanned fixed-wing aircrafts is investigated in this paper, where the non-minimum phase properties and the presence of measurement errors are systematically addressed. A three-module control scheme is proposed for each aircraft: a Distributed Observer that obtains the available information from the reference system and the neighbor aircraft to provide the estimates of the reference states; a Casual Stable Inversion that calculates the bounded estimates of the desired input, desired external states, and most importantly, desired internal states to resolve the divergence issues caused by the non-minimum phase properties; and a Local Measurement Error Rejection Controller that includes a measurement error estimator (MEE) to actively compensate for the adverse effect of measurement errors to achieve robust consensus tracking control. Stability, convergence, and robustness of the proposed control are analyzed, showing that (1) the non-minimum phase issue can be systematically resolved by the designed Casual Stable Inversion to ensure aircraft internal stability and flight safety, and (2) the consensus tracking accuracy can be improved by tuning a single MEE parameter, which is favorable in practical applications to large-scale unmanned aircraft formations. Comparative simulation results with classic PID-based consensus control demonstrate the advantage of the proposed control in transient oscillations, steady-state tracking accuracy, and robustness against measurement errors. Full article

(This article belongs to the Special Issue UAV Trajectory Generation, Optimization and Cooperative Control)

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19 pages, 2689 KiB

Open AccessArticle

Tracking Multiple Unmanned Aerial Vehicles through Occlusion in Low-Altitude Airspace

by Sufyan Ali Memon, Hungsun Son, Wan-Gu Kim, Abdul Manan Khan, Mohsin Shahzad and Uzair Khan

Drones 2023, 7(4), 241; https://0-doi-org.brum.beds.ac.uk/10.3390/drones7040241 - 30 Mar 2023

Cited by 5 | Viewed by 1296

Abstract

In an intelligent multi-target tracking (MTT) system, the tracking filter cannot track multi-targets significantly through occlusion in a low-altitude airspace. The most challenging issues are the target deformation, target occlusion and targets being concealed by the presence of background clutter. Thus, the true tracks that follow the desired targets are often lost due to the occlusion of uncertain measurements detected by a sensor, such as a motion capture (mocap) sensor. In addition, sensor measurement noise, process noise and clutter measurements degrade the system performance. To avoid track loss, we use the Markov-chain-two (MC2) model that allows the propagation of target existence through the occlusion region. We utilized the MC2 model in linear multi-target tracking based on the integrated probabilistic data association (LMIPDA) and proposed a modified integrated algorithm referred to here as LMIPDA-MC2. We consider a three-dimensional surveillance for tracking occluded targets, such as unmanned aerial vehicles (UAVs) and other autonomous vehicles at low altitude in clutters. We compared the results of the proposed method with existing Markov-chain model based algorithms using Monte Carlo simulations and practical experiments. We also provide track retention and false-track discrimination (FTD) statistics to explain the significance of the LMIPDA-MC2 algorithm. Full article

(This article belongs to the Special Issue UAV Trajectory Generation, Optimization and Cooperative Control)

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15 pages, 2724 KiB

Open AccessArticle

A Penetration Method for UAV Based on Distributed Reinforcement Learning and Demonstrations

by Kexv Li, Yue Wang, Xing Zhuang, Hao Yin, Xinyu Liu and Hanyu Li

Drones 2023, 7(4), 232; https://0-doi-org.brum.beds.ac.uk/10.3390/drones7040232 - 27 Mar 2023

Cited by 1 | Viewed by 1302

Abstract

The penetration of unmanned aerial vehicles (UAVs) is an essential and important link in modern warfare. Enhancing UAV’s ability of autonomous penetration through machine learning has become a research hotspot. However, the current generation of autonomous penetration strategies for UAVs faces the problem of excessive sample demand. To reduce the sample demand, this paper proposes a combination policy learning (CPL) algorithm that combines distributed reinforcement learning and demonstrations. Innovatively, the action of the CPL algorithm is jointly determined by the initial policy obtained from demonstrations and the target policy in the asynchronous advantage actor-critic network, thus retaining the guiding role of demonstrations in the initial training. In a complex and unknown dynamic environment, 1000 training experiments and 500 test experiments were conducted for the CPL algorithm and related baseline algorithms. The results show that the CPL algorithm has the smallest sample demand, the highest convergence efficiency, and the highest success rate of penetration among all the algorithms, and has strong robustness in dynamic environments. Full article

(This article belongs to the Special Issue UAV Trajectory Generation, Optimization and Cooperative Control)

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25 pages, 4825 KiB

Open AccessArticle

Finite-Time Adaptive Consensus Tracking Control Based on Barrier Function and Cascaded High-Gain Observer

by Xinyu Zhang, Zheng H. Zhu, Fei Liao, Hui Gao, Weihao Li and Gun Li

Drones 2023, 7(3), 197; https://0-doi-org.brum.beds.ac.uk/10.3390/drones7030197 - 14 Mar 2023

Cited by 1 | Viewed by 1287

Abstract

This paper studies the consensus tracking control for a class of uncertain high-order nonlinear multi-agent systems under an undirected leader-following architecture. A novel distributed finite-time adaptive control framework is proposed based on the barrier function. The distributed cascaded high-gain observers are introduced to solve the problem of robust consensus tracking with unmeasured intermediate states in multi-agent systems based on the proposed control framework. The proposed control schemes guarantee the finite-time consensus of multi-agent systems, which is proven by the finite-time Lyapunov stability and singular perturbation theory. In conclusion, numerical simulations verify the proposed control protocols’ effectiveness, and their performance advantages are shown by comparing them with another existing method. Full article

(This article belongs to the Special Issue UAV Trajectory Generation, Optimization and Cooperative Control)

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28 pages, 6172 KiB

Open AccessArticle

HDP-TSRRT*: A Time–Space Cooperative Path Planning Algorithm for Multiple UAVs

by Yicong Guo, Xiaoxiong Liu, Wei Jiang and Weiguo Zhang

Drones 2023, 7(3), 170; https://0-doi-org.brum.beds.ac.uk/10.3390/drones7030170 - 28 Feb 2023

Cited by 2 | Viewed by 1648

Abstract

This paper proposes a fast cooperative path planning algorithm for multiple UAVs that satisfies the time–space cooperative constraints, namely, the RRT* algorithm based on heuristic decentralized prioritized planning (HDP-TSRRT*), which takes into account the simultaneous arrival time variables of each UAV as well as the avoidance of conflicts and threats. HDP-TSRRT* is a hierarchical decoupling algorithm. First, all UAV pre-paths are planned simultaneously at the synchronous decentralized planning level. Second, at the coordination path level, the heuristic decentralized prioritized planning algorithm (HDP) is proposed to quickly complete the coordination process of the path planning sequence. This strategy assigns reasonable and robust priority to all UAVs based on the performance evaluation function composed of the number of potential collisions and the violation of collaboration time of the pre-planned path. Third, the time–space cooperative constraints-based RRT* algorithm (TSRRT*) is proposed at the single-machine cooperative path planning level. Based on this, the algorithm uses multiple sampling and cost evaluation strategies to guide the expansion of new nodes, and then optimizes neighborhood nodes based on the time coordination cost function so as to improve the efficiency of coordination path planning. Simulation and comparison show that HDP-TSRRT* has certain advantages in algorithm performance. Full article

(This article belongs to the Special Issue UAV Trajectory Generation, Optimization and Cooperative Control)

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