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Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning:...
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Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning: 2nd Edition

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

Deadline for manuscript submissions: 20 December 2024 | Viewed by 3929

Special Issue Editors

Dr. Yu Wu

E-Mail Website
Guest Editor
College of Aerospace Engineering, Chongqing University, No. 174, Shazheng Street, Shapingba District, Chongqing 400044, China
Interests: trajectory optimization; mission planning; scheduling; UAV formation control; autonomous system; meta-heuristic algorithms
Special Issues, Collections and Topics in MDPI journals
Dr. Liguo Sun

E-Mail Website
Guest Editor
School of Aeronautic Science and Engineering, Beihang University, No. 37, Xueyuan Road, Haidian District, Beijing 100191, China
Interests: fault-tolerant flight control; aerodynamic modelling and identification; adaptive nonlinear control; intelligent control; integrated flight/propulsion control; integrated pilot/autopilot control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit manuscripts to the MDPI Drones Special Issue, titled “Autonomous flight of drone: Control, trajectory optimization and mission planning”.

Drones have been widely applied, both in military and civil use in recent years. It is very important for the drones to realize a safe and efficient flight when performing various tasks. With the development of information science, many advanced theories, such as intelligent control, swarm and evolutionary computation, and deep reinforcement learning, are proposed to improve the degree of autonomy in many fields. When the drones meet the information science, their autonomous flight ability is expected to be enhanced from different levels, i.e., in terms of control, planning, and decision-making.

This Special Issue aims to present the advances in enhancing the autonomous level of drones during flight operation. To be specific, we focus on the latest developments in flight control, trajectory optimization, mission planning and decision-making for drones (the heterogeneous vehicle system which contains the drones is also interesting). We invite authors to submit original research articles and reviews for this Special Issue. Research areas may include (but not limited to) the following:

  • Pilot modeling and human–aircraft interaction;
  • Pilot/autopilot cooperative control;
  • Integrated flight/propulsion control;
  • Intelligent control application;
  • Flapping wing aircraft control;
  • UAV formation control;
  • UAV path planning and trajectory optimization;
  • Cooperative control for UAVs;
  • Task scheduling for UAV swarm;
  • Design and application of heterogeneous vehicle system.

Dr. Yu Wu
Dr. Liguo Sun
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

  • pilot control
  • intelligent control
  • UAV formation control
  • trajectory optimization
  • mission planning
  • autonomous system

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Related Special Issue

Published Papers (4 papers)

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Research

24 pages, 4837 KiB  
Article
Improved Grey Wolf Algorithm: A Method for UAV Path Planning
by Xingyu Zhou, Guoqing Shi and Jiandong Zhang
Drones 2024, 8(11), 675; https://doi.org/10.3390/drones8110675 - 14 Nov 2024
Viewed by 530
Abstract
The Grey Wolf Optimizer (GWO) algorithm is recognized for its simplicity and ease of implementation, and has become a preferred method for solving global optimization problems due to its adaptability and search capabilities. Despite these advantages, existing Unmanned Aerial Vehicle (UAV) path planning [...] Read more.
The Grey Wolf Optimizer (GWO) algorithm is recognized for its simplicity and ease of implementation, and has become a preferred method for solving global optimization problems due to its adaptability and search capabilities. Despite these advantages, existing Unmanned Aerial Vehicle (UAV) path planning algorithms are often hindered by slow convergence rates, susceptibility to local optima, and limited robustness. To surpass these limitations, we enhance the application of GWO in UAV path planning by improving its trajectory evaluation function, convergence factor, and position update method. We propose a collaborative UAV path planning model that includes constraint analysis and an evaluation function. Subsequently, an Enhanced Grey Wolf Optimizer model (NI–GWO) is introduced, which optimizes the convergence coefficient using a nonlinear function and integrates the Dynamic Window Approach (DWA) algorithm into the model based on the fitness of individual wolves, enabling it to perform dynamic obstacle avoidance tasks. In the final stage, a UAV path planning simulation platform is employed to evaluate and compare the effectiveness of the original and improved algorithms. Simulation results demonstrate that the proposed NI–GWO algorithm can effectively solve the path planning problem for UAVs in uncertain environments. Compared to Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), GWO, and MP–GWO algorithms, the NI–GWO algorithm can achieve the optimal fitness value and has significant advantages in terms of average path length, time, number of collisions, and obstacle avoidance capabilities. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning: 2nd Edition)
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25 pages, 5681 KiB  
Article
Multi-Batch Carrier-Based UAV Formation Rendezvous Method Based on Improved Sequential Convex Programming
by Zirui Zhang, Liguo Sun and Yanyang Wang
Drones 2024, 8(11), 615; https://doi.org/10.3390/drones8110615 - 26 Oct 2024
Viewed by 642
Abstract
The limitations of the existing catapults necessitate multiple batches of take-offs for carrier-based unmanned aerial vehicles (UAVs) to form a formation. Because of the differences in takeoff time and location of each batch of UAVs, ensuring the temporal and spatial consistency and rendezvous [...] Read more.
The limitations of the existing catapults necessitate multiple batches of take-offs for carrier-based unmanned aerial vehicles (UAVs) to form a formation. Because of the differences in takeoff time and location of each batch of UAVs, ensuring the temporal and spatial consistency and rendezvous efficiency of the formation becomes crucial. Concerning the challenges mentioned above, a multi-batch formation rendezvous method based on improved sequential convex programming (SCP) is proposed. A reverse solution approach based on the multi-batch rendezvous process is developed. On this basis, a non-convex optimization problem is formulated considering the following constraints: UAV dynamics, collision avoidance, obstacle avoidance, and formation consistency. An SCP method that makes use of the trust region strategy is introduced to solve the problem efficiently. Due to the spatiotemporal coupling characteristics of the rendezvous process, an inappropriate initial solution for SCP will inevitably reduce the rendezvous efficiency. Thus, an initial solution tolerance mechanism is introduced to improve the SCP. This mechanism follows the idea of simulated annealing, allowing the SCP to search for better reference solutions in a wider space. By utilizing the initial solution tolerance SCP (IST-SCP), the multi-batch formation rendezvous algorithm is developed correspondingly. Simulation results are obtained to verify the effectiveness and adaptability of the proposed method. IST-SCP reduces the rendezvous time from poor initial solutions without significantly increasing the computing time. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning: 2nd Edition)
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20 pages, 31597 KiB  
Article
A Pseudo-Exponential-Based Artificial Potential Field Method for UAV Cluster Control under Static and Dynamical Obstacles
by Jie Zhang, Fengyun Li, Jiacheng Li, Qian Chen and Hanlin Sheng
Drones 2024, 8(9), 506; https://doi.org/10.3390/drones8090506 - 19 Sep 2024
Viewed by 745
Abstract
This study presents a novel obstacle evasion method for unmanned aerial vehicle (UAV) clusters in the presence of static and dynamic obstacles. First, a discrete three-dimensional model of the UAV is provided. Second, the proposed improved artificial potential field (APF) is illustrated. In [...] Read more.
This study presents a novel obstacle evasion method for unmanned aerial vehicle (UAV) clusters in the presence of static and dynamic obstacles. First, a discrete three-dimensional model of the UAV is provided. Second, the proposed improved artificial potential field (APF) is illustrated. In designing the improved scheme, a pseudo-exponential function is fused into the potential field, thus avoiding local extreme points. Frictional resistance is introduced to optimize vibration and maintain stability after reaching the desired endpoints. Meanwhile, the relevant parameters are optimized, and appropriate state limits are defined, thus enhancing the control stability. Third, Lyapunov stability analysis proves that all signals in the closed-loop cluster system are ultimately bounded. Finally, the simulation results demonstrate that the UAV cluster can efficiently reconstruct, form, and maintain formations while avoiding static and dynamical obstacles along with maintaining a safe distance, solving the problem of the local extreme of traditional artificial potential field methods. The proposed scheme is also tested under large-scale multi-UAV scenarios. In conclusion, this study provides valuable insights for engineers working with UAV clusters navigating through formations. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning: 2nd Edition)
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30 pages, 3460 KiB  
Article
Drone Arc Routing Problems and Metaheuristic Solution Approach
by Islam Altin and Aydin Sipahioglu
Drones 2024, 8(8), 373; https://doi.org/10.3390/drones8080373 - 3 Aug 2024
Viewed by 1255
Abstract
The drone arc routing problem (DARP) is one of the arc routing problems (ARPs) that has been studied by researchers recently. Unlike traditional ARPs, drones can travel directly between any two points in the graph. Due to the flexibility of drones, it is [...] Read more.
The drone arc routing problem (DARP) is one of the arc routing problems (ARPs) that has been studied by researchers recently. Unlike traditional ARPs, drones can travel directly between any two points in the graph. Due to the flexibility of drones, it is possible to use edges not defined in the graphs when deadheading the edges. This advantage of drones makes this problem more challenging than any other ARP. With this study, the energy capacities of drones are considered in a DARP. Thus, a novel DARP called the drone arc routing problem with deadheading demand (DARP-DD) is addressed in this study. Drone capacities are used both when servicing the edges and when deadheading the edges in the DARP-DD. A special case of the DARP-DD, called the multiple service drone arc routing problem with deadheading demand (MS-DARP-DD), is also discussed, where some critical required edges may need to be served more than once. To solve these challenging problems, a simulated annealing algorithm is used, and the components of the algorithm are designed. Additionally, novel neighbor search operators are developed in this study. The computational results show that the proposed algorithm and its components are effective and useful in solving the DARP-DD and MS-DARP-DD. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning: 2nd Edition)
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