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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: 31 May 2025 | Viewed by 13105

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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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (11 papers)

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Research

Jump to: Review

27 pages, 1845 KiB  
Article
Offshore Wind Farm Delivery with Autonomous Drones: A Holistic View of System Architecture and Onboard Capabilities
by Simon Schopferer, Philipp Schitz, Mark Spiller, Alexander Donkels, Pranav Nagarajan, Fabian Krause, Sebastian Schirmer, Christoph Torens, Johann C. Dauer, Sebastian Cain and Vincenz Schneider
Drones 2025, 9(4), 295; https://doi.org/10.3390/drones9040295 - 10 Apr 2025
Viewed by 344
Abstract
Maintenance of offshore wind farms requires the transportation of tools and spare parts in close coordination with the deployment of technicians and the cost-intensive shutdown of the wind turbines. In addition to ships and helicopters, drones are envisioned to support the offshore transportation [...] Read more.
Maintenance of offshore wind farms requires the transportation of tools and spare parts in close coordination with the deployment of technicians and the cost-intensive shutdown of the wind turbines. In addition to ships and helicopters, drones are envisioned to support the offshore transportation system in the future. For cost-efficient and scalable offshore drone operations, autonomy is key to minimize the required infrastructure and personnel. In this work, we present a system architecture that integrates the key onboard capabilities for autonomous offshore drone operations: onboard mission and contingency management, en-route trajectory planning, robust flight control, safe landing, communication management, and runtime monitoring. We also present technical solutions for each of these capabilities and discuss their integration and interaction within the autonomy architecture. Furthermore, remaining challenges and the feasibility of autonomous drone operations for offshore wind farm cargo delivery are addressed, contributing to the realization of this vision in the near future. The work presented here summarizes the results of autonomous cargo drone operations within the UDW research project, a joint project between the German Aerospace Center (DLR) and the energy supplier EnBW. 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, 3481 KiB  
Article
A Hierarchical Control Algorithm for a Pursuit–Evasion Game Based on Fuzzy Actor–Critic Learning and Model Predictive Control
by Penglin Hu, Chunhui Zhao and Quan Pan
Drones 2025, 9(3), 184; https://doi.org/10.3390/drones9030184 - 1 Mar 2025
Viewed by 591
Abstract
In this paper, we adopt the fuzzy actor–critic learning (FACL) and model predictive control (MPC) algorithms to solve the pursuit–evasion game (PEG) of quadrotors. FACL is used for perception, decision-making, and predicting the trajectories of agents, while MPC is utilized to address the [...] Read more.
In this paper, we adopt the fuzzy actor–critic learning (FACL) and model predictive control (MPC) algorithms to solve the pursuit–evasion game (PEG) of quadrotors. FACL is used for perception, decision-making, and predicting the trajectories of agents, while MPC is utilized to address the flight control and target optimization of quadrotors. Specifically, based on the information of the opponent, the agent obtains its own game strategy by using the FACL algorithm. Based on the reference input from the FACL algorithm, the MPC algorithm is used to develop altitude, translation, and attitude controllers for the quadrotor. In the proposed hierarchical framework, the FACL algorithm provides real-time reference inputs for the MPC controller, enhancing the robustness of quadrotor control. The simulation and experimental results show that the proposed hierarchical control algorithm effectively realizes the PEG of quadrotors. 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, 11390 KiB  
Article
A Multi-Objective Black-Winged Kite Algorithm for Multi-UAV Cooperative Path Planning
by Xiukang Liu, Fufu Wang, Yu Liu and Long Li
Drones 2025, 9(2), 118; https://doi.org/10.3390/drones9020118 - 5 Feb 2025
Viewed by 949
Abstract
In UAV path-planning research, it is often difficult to achieve optimal performance for conflicting objectives. Therefore, the more promising approach is to find a balanced solution that mitigates the effects of subjective weighting, utilizing a multi-objective optimization algorithm to address the complex planning [...] Read more.
In UAV path-planning research, it is often difficult to achieve optimal performance for conflicting objectives. Therefore, the more promising approach is to find a balanced solution that mitigates the effects of subjective weighting, utilizing a multi-objective optimization algorithm to address the complex planning issues that involve multiple machines. Here, we introduce an advanced mathematical model for cooperative path planning among multiple UAVs in urban logistics scenarios, employing the non-dominated sorting black-winged kite algorithm (NSBKA) to address this multi-objective optimization challenge. To evaluate the efficacy of NSBKA, it was benchmarked against other algorithms using the Zitzler, Deb, and Thiele (ZDT) test problems, Deb, Thiele, Laumanns, and Zitzler (DTLZ) test problems, and test functions from the conference on evolutionary computation 2009 (CEC2009) for three types of multi-objective problems. Comparative analyses and statistical results indicate that the proposed algorithm outperforms on all 22 test functions. To verify the capability of NSBKA in addressing the multi-UAV cooperative problem model, the algorithm is applied to solve the problem. Simulation experiments for three UAVs and five UAVs show that the proposed algorithm can obtain a more reasonable collaborative path solution set for UAVs. Moreover, path planning based on NSBKA is generally superior to other algorithms in terms of energy saving, safety, and computing efficiency during planning. This affirms the effectiveness of the meta-heuristic algorithm in dealing with multiple objective multi-UAV cooperation problems and further enhances the robustness and competitiveness of NSBKA. 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, 6000 KiB  
Article
Assignment Technology Based on Improved Great Wall Construction Algorithm
by Xianjun Zeng, Yao Wei, Yang Yu, Hanjie Hu, Qixiang Tang and Ning Hu
Drones 2025, 9(2), 113; https://doi.org/10.3390/drones9020113 - 4 Feb 2025
Viewed by 527
Abstract
The problem of allocating multiple UAV tasks is a complex combinatorial optimization challenge, involving various constraints. This paper presents an autonomous multi-UAV cooperative task allocation method based on an improved Great Wall Construction Algorithm. A model integrating battlefield environmental factors, 3D terrain data, [...] Read more.
The problem of allocating multiple UAV tasks is a complex combinatorial optimization challenge, involving various constraints. This paper presents an autonomous multi-UAV cooperative task allocation method based on an improved Great Wall Construction Algorithm. A model integrating battlefield environmental factors, 3D terrain data, and threat assessments is developed for optimized task allocation and trajectory planning. The algorithm is enhanced using a good point set initialization strategy, Gaussian distribution estimation, and a Cauchy reorganization variant. The simulation results show that replacing straight-line distances with actual flight distances leads to more rational mission sequences, improving combat effectiveness under realistic terrain and threat conditions. The enhanced algorithm demonstrates superior accuracy and faster convergence. 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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24 pages, 2980 KiB  
Article
Super-Twisting Algorithm Backstepping Adaptive Terminal Sliding-Mode Tracking Control of Quadrotor Drones Subjected to Faults and Disturbances
by Ye Zhang, Yihao Fu, Zhiguo Han and Jingyu Wang
Drones 2025, 9(2), 82; https://doi.org/10.3390/drones9020082 - 22 Jan 2025
Cited by 1 | Viewed by 742
Abstract
The rapid advancement of quadrotor systems has introduced significant challenges across multiple disciplines. Among these, fault tolerance and trajectory tracking in complex environments have long been recognized as critical challenges in quadrotor control research. To address issues such as rotor performance degradation and [...] Read more.
The rapid advancement of quadrotor systems has introduced significant challenges across multiple disciplines. Among these, fault tolerance and trajectory tracking in complex environments have long been recognized as critical challenges in quadrotor control research. To address issues such as rotor performance degradation and external disturbances, a novel position-attitude control system was developed, aimed to achieve precise position and attitude tracking. Initially, a dynamic model of the quadrotor was formulated, serving as the foundation for the controller design. Super-twisting algorithm terminal sliding-mode control (STATSMC) was then employed within the position loop to suppress chattering by the super-twisting algorithm. Subsequently, a new super-twisting algorithm beckstepping adaptive terminal sliding-mode control (STABATSMC) was proposed to mitigate the controller output and merge enable adherence to the desired Euler angles in case of failure. This approach enables the quadrotor to accurately follow position commands and achieve the desired attitude angles. The introduction of terminal sliding-mode control enhances convergence speed and tracking precision, while the super-twisting algorithm mitigates chattering and smoothens the control output. Finally, a series of simulation experiments were conducted within the Simulink environment to validate the proposed control system. The experimental results are compared with the state-of-art terminal sliding-mode control method, demonstrating the superior performance and effectiveness of the proposed method. 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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24 pages, 27332 KiB  
Article
A Global Coverage Path Planning Method for Multi-UAV Maritime Surveillance in Complex Obstacle Environments
by Yiyuan Li, Weiyi Chen, Bing Fu, Zhonghong Wu and Lingjun Hao
Drones 2024, 8(12), 764; https://doi.org/10.3390/drones8120764 - 17 Dec 2024
Viewed by 1001
Abstract
The study of unmanned aerial vehicle (UAV) coverage path planning is of great significance for ensuring maritime situational awareness and monitoring. In response to the problem of maritime multi-region coverage surveillance in complex obstacle environments, this paper proposes a global path planning method [...] Read more.
The study of unmanned aerial vehicle (UAV) coverage path planning is of great significance for ensuring maritime situational awareness and monitoring. In response to the problem of maritime multi-region coverage surveillance in complex obstacle environments, this paper proposes a global path planning method capable of simultaneously addressing the multiple traveling salesman problem, coverage path planning problem, and obstacle avoidance problem. Firstly, a multiple traveling salesmen problem–coverage path planning (MTSP-CPP) model with the objective of minimizing the maximum task completion time is constructed. Secondly, a method for calculating obstacle-avoidance path costs based on the Voronoi diagram is proposed, laying the foundation for obtaining the optimal access order. Thirdly, an improved discrete grey wolf optimizer (IDGWO) algorithm integrated with variable neighborhood search (VNS) operations is proposed to perform task assignment for multiple UAVs and achieve workload balancing. Finally, based on dynamic programming, the coverage path points of the area are solved precisely to generate the globally coverage path. Through simulation experiments with scenarios of varying scales, the effectiveness and superiority of the proposed method are validated. The experimental results demonstrate that this method can effectively solve MTSP-CPP in complex obstacle environments. 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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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
Cited by 5 | Viewed by 1758
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 1056
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 1205
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
Cited by 2 | Viewed by 1970
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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Review

Jump to: Research

34 pages, 1950 KiB  
Review
Dealing with Multiple Optimization Objectives for UAV Path Planning in Hostile Environments: A Literature Review
by Thomas Quadt, Roy Lindelauf, Mark Voskuijl, Herman Monsuur and Boris Čule
Drones 2024, 8(12), 769; https://doi.org/10.3390/drones8120769 - 19 Dec 2024
Cited by 1 | Viewed by 1564
Abstract
As Unmanned Aerial Vehicles (UAVs) are becoming crucial in modern warfare, research on autonomous path planning is becoming increasingly important. The conflicting nature of the optimization objectives characterizes path planning as a multi-objective optimization problem. Current research has predominantly focused on developing new [...] Read more.
As Unmanned Aerial Vehicles (UAVs) are becoming crucial in modern warfare, research on autonomous path planning is becoming increasingly important. The conflicting nature of the optimization objectives characterizes path planning as a multi-objective optimization problem. Current research has predominantly focused on developing new optimization algorithms. Although being able to find the mathematical optimum is important, one also needs to ensure this optimum aligns with the decision-maker’s (DM’s) most preferred solution (MPS). In particular, to align these, one needs to handle the DM’s preferences on the relative importance of each optimization objective. This paper provides a comprehensive overview of all preference handling techniques employed in the military UAV path planning literature over the last two decades. It shows that most of the literature handles preferences by the overly simplistic method of scalarization via weighted sum. Additionally, the current literature neglects to evaluate the performance (e.g., cognitive validity and modeling accuracy) of the chosen preference handling technique. To aid future researchers handle preferences, we discuss each employed preference handling technique, their implications, advantages, and disadvantages in detail. Finally, we identify several directions for future research, mainly related to aligning the mathematical optimum to the MPS. 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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