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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

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 58264

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 the information science, many advanced theories, such as intelligent control, swarm and evolutionary computation, and machine 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 execution, planning, and decision-making.

This Special Issue aims to present the advances in enhancing the autonomous level of drones during the flight operation. To be specific, we focus on the latest developments in flight control, trajectory optimization, and mission planning for drones (the heterogeneous vehicle system which contains the drones are also interested). 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 (19 papers)

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Research

20 pages, 4180 KiB  
Article
Clustering-Based Multi-Region Coverage-Path Planning of Heterogeneous UAVs
by Peng Xiao, Ni Li, Feng Xie, Haihong Ni, Min Zhang and Ban Wang
Drones 2023, 7(11), 664; https://doi.org/10.3390/drones7110664 - 7 Nov 2023
Cited by 2 | Viewed by 2222
Abstract
Unmanned aerial vehicles (UAVs) multi-area coverage-path planning has a broad range of applications in agricultural mapping and military reconnaissance. Compared to homogeneous UAVs, heterogeneous UAVs have higher application value due to their superior flexibility and efficiency. Nevertheless, variations in performance parameters among heterogeneous [...] Read more.
Unmanned aerial vehicles (UAVs) multi-area coverage-path planning has a broad range of applications in agricultural mapping and military reconnaissance. Compared to homogeneous UAVs, heterogeneous UAVs have higher application value due to their superior flexibility and efficiency. Nevertheless, variations in performance parameters among heterogeneous UAVs can significantly amplify computational complexity, posing challenges to solving the multi-region coverage path-planning problem. Consequently, this study studies a clustering-based method to tackle the multi-region coverage path-planning problem of heterogeneous UAVs. First, the constraints necessary during the planning process are analyzed, and a planning formula based on an integer linear programming model is established. Subsequently, this problem is decomposed into regional allocation and visiting order optimization subproblems. This study proposes a novel clustering algorithm that utilizes centroid iteration and spatiotemporal similarity to allocate regions and adopts the nearest-to-end policy to optimize the visiting order. Additionally, a distance-based bilateral shortest-selection strategy is proposed to generate region-scanning trajectories, which serve as trajectory references for real flight. Simulation results in this study prove the effective performance of the proposed clustering algorithm and region-scanning strategy. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning)
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23 pages, 8368 KiB  
Article
Analysis of UTM Tracking Performance for Conformance Monitoring via Hybrid SITL Monte Carlo Methods
by Wei Dai, Zhi Hao Quek, Bizhao Pang and Mir Feroskhan
Drones 2023, 7(10), 597; https://doi.org/10.3390/drones7100597 - 22 Sep 2023
Viewed by 1698
Abstract
Conformance monitoring supports UTM safety by observing if unmanned aircraft (UA) are adhering to declared operational intent. As a supporting system, robust cooperative tracking is critical. Nevertheless, tracking systems for UAS traffic management (UTM) are in an early stage and under-standardized, and existing [...] Read more.
Conformance monitoring supports UTM safety by observing if unmanned aircraft (UA) are adhering to declared operational intent. As a supporting system, robust cooperative tracking is critical. Nevertheless, tracking systems for UAS traffic management (UTM) are in an early stage and under-standardized, and existing literature hardly addresses the problem. To bridge this gap, this study aims to probabilistically evaluate the impact of the change in tracking performances on the effectiveness of conformance monitoring. We propose a Monte Carlo simulation-based method. To ensure a realistic simulation environment, we use a hybrid software-in-the-loop (SITL) scheme. The major uncertainties contributing to the stochastic evaluation are measured separately and are integrated into the final Monte Carlo simulation. Latency tests were conducted to assess the performance of different communication technologies for cooperative tracking. Flight technical error generation via SITL simulations and navigational system error generation based on flight experiments were employed to model UA trajectory uncertainty. Based on these tests, further Monte Carlo simulations were used to study the overall impacts of various tracking key performance indicators in UTM conformance monitoring. Results suggest that the extrapolation of UA position enables quicker non-conformance detection, but introduces greater variability in detection delay, and exacerbates the incidence of nuisance alerts and missed detections, particularly when latencies are high and velocity errors are severe. Recommendations for UA position update rates of ≥1 Hz remain consistent with previous studies, as investments in increasing the update rate do not lead to corresponding improvements in conformance monitoring performance according to simulation results. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning)
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22 pages, 1385 KiB  
Article
RL-Based Detection, Tracking, and Classification of Malicious UAV Swarms through Airborne Cognitive Multibeam Multifunction Phased Array Radar
by Wahab Khawaja, Qasim Yaqoob and Ismail Guvenc
Drones 2023, 7(7), 470; https://doi.org/10.3390/drones7070470 - 16 Jul 2023
Cited by 3 | Viewed by 2211
Abstract
Detecting, tracking, and classifying unmanned aerial vehicles (UAVs) in a swarm presents significant challenges due to their small and diverse radar cross-sections, multiple flight altitudes, velocities, and close trajectories. To overcome these challenges, adjustments of the radar parameters and/or position of the radar [...] Read more.
Detecting, tracking, and classifying unmanned aerial vehicles (UAVs) in a swarm presents significant challenges due to their small and diverse radar cross-sections, multiple flight altitudes, velocities, and close trajectories. To overcome these challenges, adjustments of the radar parameters and/or position of the radar (for airborne platforms) are often required during runtime. The runtime adjustments help to overcome the anomalies in the detection, tracking, and classification of UAVs. The runtime adjustments are performed either manually or through fixed algorithms, each of which can have its limitations for complex and dynamic scenarios. In this work, we propose the use of multi-agent reinforcement learning (RL) to carry out the runtime adjustment of the radar parameters and position of the radar platform. The radar used in our work is a multibeam multifunction phased array radar (MMPAR) placed onboard UAVs. The simulations show that the cognitive adjustment of the MMPAR parameters and position of the airborne platform using RL helps to overcome anomalies in the detection, tracking, and classification of UAVs in a swarm. A comparison with other artificial intelligence (AI) algorithms shows that RL performs better due to the runtime learning of the environment through rewards. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning)
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21 pages, 7298 KiB  
Article
An Augmented Sliding Mode Control for Fixed-Wing UAVs with External Disturbances and Model Uncertainties
by Yu Pan, Ni Li, Wanyong Zou, Ban Wang, Kaibo Wang, Xiaojun Tang, Shuhui Bu and Ling Qin
Drones 2023, 7(7), 440; https://doi.org/10.3390/drones7070440 - 2 Jul 2023
Cited by 1 | Viewed by 1939
Abstract
Model uncertainties and external disturbances present significant challenges for controlling fixed-wing unmanned aerial vehicles (UAVs). An adaptive smooth second-order time-varying nonsingular fast terminal sliding mode control method is proposed in this paper for attitude and airspeed control of fixed-wing UAVs with model uncertainties [...] Read more.
Model uncertainties and external disturbances present significant challenges for controlling fixed-wing unmanned aerial vehicles (UAVs). An adaptive smooth second-order time-varying nonsingular fast terminal sliding mode control method is proposed in this paper for attitude and airspeed control of fixed-wing UAVs with model uncertainties and external disturbances. This control method does not require information about the bounds of disturbances and can avoid overestimation of the control gains. A radial basis function neural network observer is designed to mitigate the influence caused by sudden disturbances. The convergence of the attitude and airspeed controllers is proven by using the Lyapunov stability theory. Simulation results demonstrate the effectiveness of the proposed method for controlling a six-degrees-of-freedom fixed-wing UAV. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning)
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20 pages, 1329 KiB  
Article
Drone Optimization in Factory: Exploring the Minimal Level Vehicle Routing Problem for Efficient Material Distribution
by Ivan Derpich and Carlos Rey
Drones 2023, 7(7), 435; https://doi.org/10.3390/drones7070435 - 30 Jun 2023
Cited by 2 | Viewed by 2321
Abstract
The efficient movement of raw materials within organizations is fundamental to maintaining the seamless progression of production processes. However, these logistical operations can inadvertently compromise overall company efficiency, primarily due to the substantial time invested in transporting materials. This paper introduces an innovative [...] Read more.
The efficient movement of raw materials within organizations is fundamental to maintaining the seamless progression of production processes. However, these logistical operations can inadvertently compromise overall company efficiency, primarily due to the substantial time invested in transporting materials. This paper introduces an innovative mathematical model specifically designed to optimize the transport of raw materials via drones across multiple workstations. This model employs a novel modification of the traditional multi-level Vehicle Routing Problem by incorporating an additional index and accounting for the drone’s energy consumption. We employ a widely-recognized solver for practical resolution and compare it with a heuristic algorithm. The resultant strategies offer promising prospects for the organization studied, introducing robust solutions for elevating the efficiency of raw material transportation. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning)
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18 pages, 14520 KiB  
Article
Potential-Field-RRT: A Path-Planning Algorithm for UAVs Based on Potential-Field-Oriented Greedy Strategy to Extend Random Tree
by Tai Huang, Kuangang Fan, Wen Sun, Weichao Li and Haoqi Guo
Drones 2023, 7(5), 331; https://doi.org/10.3390/drones7050331 - 21 May 2023
Cited by 5 | Viewed by 3344
Abstract
This paper proposes a random tree algorithm based on a potential field oriented greedy strategy for the path planning of unmanned aerial vehicles (UAVs). Potential-field-RRT (PF-RRT) discards the defect of traditional artificial potential field (APF) algorithms that are prone to fall into local [...] Read more.
This paper proposes a random tree algorithm based on a potential field oriented greedy strategy for the path planning of unmanned aerial vehicles (UAVs). Potential-field-RRT (PF-RRT) discards the defect of traditional artificial potential field (APF) algorithms that are prone to fall into local errors, and introduces potential fields as an aid to the expansion process of random trees. It reasonably triggers a greedy strategy based on the principle of field strength descending gradient optimization, accelerating the process of random tree expansion to a better region and reducing path search time. Compared with other optimization algorithms that improve the sampling method to reduce the search time of the random tree, PF-RRT takes full advantage of the potential field without limiting the arbitrariness of random tree expansion. Secondly, the path construction process is based on the principle of triangle inequality for the root node of the new node to improve the quality of the path in one iteration. Simulation experiments of the algorithm comparison show that the algorithm has the advantages of fast acquisition of high-quality initial path solutions and fast optimal convergence in the path search process. Compared with the original algorithm, obtaining the initial solution using PF-RRT can reduce the time loss by 20% to 70% and improve the path quality by about 25%. In addition, the feasibility of PF-RRT for UAV path planning is demonstrated by actual flight test experiments at the end of the experiment. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning)
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19 pages, 6766 KiB  
Article
Robust Planning System for Fast Autonomous Flight in Complex Unknown Environment Using Sparse Directed Frontier Points
by Yinghao Zhao, Li Yan, Jicheng Dai, Xiao Hu, Pengcheng Wei and Hong Xie
Drones 2023, 7(3), 219; https://doi.org/10.3390/drones7030219 - 21 Mar 2023
Cited by 4 | Viewed by 2246
Abstract
Path planning is one of the key parts of unmanned aerial vehicle (UAV) fast autonomous flight in cluttered environments. However, it remains a challenge to efficiently generate a high-quality trajectory for flight tasks with a high success rate. In this paper, a robust [...] Read more.
Path planning is one of the key parts of unmanned aerial vehicle (UAV) fast autonomous flight in cluttered environments. However, it remains a challenge to efficiently generate a high-quality trajectory for flight tasks with a high success rate. In this paper, a robust planning framework is proposed, which can stably support autonomous flight tasks in complex unknown environments with limited onboard computing resources. Firstly, we propose the directed frontier point information structure (DFP), which can roughly capture the frontier information of the explored environment. The planning direction of a local planner can be evaluated and rectified efficiently based on the DFP to avoid falling into traps with limited cost. Secondly, an adaptive fusion replanning method is designed to generate a high-quality trajectory efficiently by incorporating two optimization methods with different characteristics, which can both take advantage of different optimization methods while avoiding disadvantages as much as possible, but also adjust the focus of the optimization according to the actual situation to improve the success rate of the planning method. Finally, sufficient comparison and evaluation experiments in simulation environments are presented. Experimental results show the proposed method has better performance, especially in terms of adaptability and robustness, compared to typical and state-of-the-art methods in unknown complex scenarios. Moreover, the proposed system is integrated into a fully autonomous quadrotor, and the effectiveness of the proposed method is further evaluated by using the quadrotor in real-world environments. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning)
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19 pages, 13839 KiB  
Article
Fault-Tolerant Control for Carrier-Based UAV Based on Sliding Mode Method
by Zhuoer Yao, Zi Kan, Chong Zhen, Haoyuan Shao and Daochun Li
Drones 2023, 7(3), 194; https://doi.org/10.3390/drones7030194 - 13 Mar 2023
Cited by 12 | Viewed by 2192
Abstract
To enable a carrier-based unmanned aerial vehicle (UAV) to track the desired glide trajectory and safely land on the deck with the presence of system faults, this paper proposes a neural network-based adaptive sliding mode fault-tolerant control (NASFTC) method. Firstly, the dynamic model [...] Read more.
To enable a carrier-based unmanned aerial vehicle (UAV) to track the desired glide trajectory and safely land on the deck with the presence of system faults, this paper proposes a neural network-based adaptive sliding mode fault-tolerant control (NASFTC) method. Firstly, the dynamic model of the carrier -based UAV, the actuator fault model, the additional unknown fault model, and the control framework of the automatic carrier landing system (ACLS) were developed. Subsequently, controllers for both longitudinal and lateral channels were designed by using the NASFTC method. The controller consists of three parts: the adaptive laws for compensating the actuator faults, the RBF neural network for compensating the additional unknown faults, and the sliding mode method for ensuring overall trajectory tracking. Then, the Lyapunov function theorem was applied to carry out the stability analysis. Finally, comparative simulations under three different scenarios were conducted. The comparative results show the effectiveness of the proposed NASFTC method, which has fault-tolerant ability and can successfully control the aircraft to execute carrier landing task regardless of the actuator partial loss fault and the additional unknown fault. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning)
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32 pages, 21875 KiB  
Article
Collision-Free 4D Dynamic Path Planning for Multiple UAVs Based on Dynamic Priority RRT* and Artificial Potential Field
by Yicong Guo, Xiaoxiong Liu, Wei Jiang and Weiguo Zhang
Drones 2023, 7(3), 180; https://doi.org/10.3390/drones7030180 - 6 Mar 2023
Cited by 8 | Viewed by 3167
Abstract
In this paper, a four-dimensional (4D) dynamic cooperative path planning algorithm for multiple unmanned aerial vehicles (UAVs) is proposed, in which the cooperative time variables of UAVs, as well as conflict and threat avoidance, are considered. The algorithm proposed in this paper uses [...] Read more.
In this paper, a four-dimensional (4D) dynamic cooperative path planning algorithm for multiple unmanned aerial vehicles (UAVs) is proposed, in which the cooperative time variables of UAVs, as well as conflict and threat avoidance, are considered. The algorithm proposed in this paper uses a hierarchical framework that is divided into a 4D cooperative planning layer and a local threat avoidance planning layer. In the cooperative planning layer, the proposed algorithm, named dynamic priority rapidly exploring random trees (DPRRT*), would be used for the 4D cooperative path planning of all UAVs involved in a given task. We first designed a heuristic prioritization strategy in the DPRRT* algorithm to rank all UAVs to improve the efficiency of cooperative planning. Then, the improved RRT* algorithm with the 4D coordination cost function was used to plan the 4D coordination path for each UAV. Whenever the environment changes dynamically (i.e., sudden static or moving threats), the proposed heuristic artificial potential field algorithm (HAPF) in the local threat avoidance planning layer is used to plan the local collision avoidance path. After completing local obstacle avoidance planning, the DPRRT* of the 4D cooperative planning layer is again called upon for path replanning to finally realize 4D cooperative path planning for all UAVs. The simulation and comparison experiments prove the feasibility, efficiency, and robustness of the proposed algorithm. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning)
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24 pages, 3224 KiB  
Article
Quadrotor Path Planning and Polynomial Trajectory Generation Using Quadratic Programming for Indoor Environments
by Muhammad Awais Arshad, Jamal Ahmed and Hyochoong Bang
Drones 2023, 7(2), 122; https://doi.org/10.3390/drones7020122 - 9 Feb 2023
Cited by 4 | Viewed by 5355
Abstract
This study considers the problem of generating optimal, kino-dynamic-feasible, and obstacle-free trajectories for a quadrotor through indoor environments. We explore methods to overcome the challenges faced by quadrotors for indoor settings due to their higher-order vehicle dynamics, relatively limited free spaces through the [...] Read more.
This study considers the problem of generating optimal, kino-dynamic-feasible, and obstacle-free trajectories for a quadrotor through indoor environments. We explore methods to overcome the challenges faced by quadrotors for indoor settings due to their higher-order vehicle dynamics, relatively limited free spaces through the environment, and challenging optimization constraints. In this research, we propose a complete pipeline for path planning, trajectory generation, and optimization for quadrotor navigation through indoor environments. We formulate the trajectory generation problem as a Quadratic Program (QP) with Obstacle-Free Corridor (OFC) constraints. The OFC is a collection of convex overlapping polyhedra that model tunnel-like free connecting space from current configuration to goal configuration. Linear inequality constraints provided by the polyhedra of OFCs are used in the QP for real-time optimization performance. We demonstrate the feasibility of our approach, its performance, and its completeness by simulating multiple environments of differing sizes and varying obstacle densities using MATLAB Optimization Toolbox. We found that our approach has higher chances of convergence of optimization solver as compared to current approaches for challenging scenarios. We show that our proposed pipeline can plan complete paths and optimize trajectories in a few hundred milliseconds and within approximately ten iterations of the optimization solver for everyday indoor settings. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning)
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18 pages, 10028 KiB  
Article
Fast Marching Techniques for Teaming UAV’s Applications in Complex Terrain
by Santiago Garrido, Javier Muñoz, Blanca López, Fernando Quevedo, Concepción A. Monje and Luis Moreno
Drones 2023, 7(2), 84; https://doi.org/10.3390/drones7020084 - 25 Jan 2023
Cited by 2 | Viewed by 2356
Abstract
In this paper, we present a study on coverage missions carried out by UAV formations in 3D environments. These missions are designed to be applied in tracking and search and rescue missions, especially in the case of accidents. In this manner, the presented [...] Read more.
In this paper, we present a study on coverage missions carried out by UAV formations in 3D environments. These missions are designed to be applied in tracking and search and rescue missions, especially in the case of accidents. In this manner, the presented method focuses on the path planning stage, the objective of which is to compute a convenient trajectory to completely cover a certain area in a determined environment. The methodology followed uses a Gaussian mixture to approximate a probability of containment distribution along with the Fast Marching Square (FM2) as path planner. The Gaussians permit to define a zigzag trajectory that optimizes the path. Next, a first 2D geometric path perpendicular to the Voronoi diagram of the Gaussian distribution is calculated, obtained by skeletonization. To this path, the height above the ground is added plus the desired flight height to make it 3D. Finally, the FM2 method for formations is applied to make the path smooth and safe enough to be followed by UAVs. The simulation experiments show that the proposed method achieves good results for the zigzag path in terms of smoothness, safety and distance to cover the desired area through the formation of UAVs. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning)
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18 pages, 2023 KiB  
Article
Dual Observer Based Adaptive Controller for Hybrid Drones
by Nihal Dalwadi, Dipankar Deb and Stepan Ozana
Drones 2023, 7(1), 48; https://doi.org/10.3390/drones7010048 - 11 Jan 2023
Cited by 6 | Viewed by 2312
Abstract
A biplane quadrotor (hybrid vehicle) benefits from rotary-wing and fixed-wing structures. We design a dual observer-based autonomous trajectory tracking controller for the biplane quadrotor. Extended state observer (ESO) is designed for the state estimation, and based on this estimation, a Backstepping controller (BSC), [...] Read more.
A biplane quadrotor (hybrid vehicle) benefits from rotary-wing and fixed-wing structures. We design a dual observer-based autonomous trajectory tracking controller for the biplane quadrotor. Extended state observer (ESO) is designed for the state estimation, and based on this estimation, a Backstepping controller (BSC), Integral Terminal Sliding Mode Controller (ITSMC), and Hybrid Controller (HC) that is a combination of ITSMC + BSC are designed for the trajectory tracking. Further, a Nonlinear disturbance observer (DO) is designed and combined with ESO based controller to estimate external disturbances. In this simulation study, These ESO-based controllers with and without DO are applied for trajectory tracking, and results are evaluated. An ESO-based Adaptive Backstepping Controller (ABSC) and Adaptive Hybrid controller (AHC) with DO are designed, and performance is evaluated to handle the mass change during the flight despite wind gusts. Simulation results reveal the effectiveness of ESO-based HC with DO compared to ESO-based BSC and ITSMC with DO. Furthermore, an ESO-based AHC with DO is more efficient than an ESO-based ABSC with DO. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning)
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18 pages, 3352 KiB  
Article
Predefined Location Formation: Keeping Control for UAV Clusters Based on Monte Carlo Strategy
by Shuzhen Li, Yuzhe Li, Junlin Zhu and Bin Liu
Drones 2023, 7(1), 29; https://doi.org/10.3390/drones7010029 - 31 Dec 2022
Cited by 5 | Viewed by 2073
Abstract
UAV formation keeping is an important research element due to its cooperative formation control. This study proposes a passive positioning model for UAVs based on the Monte Carlo strategy and provides a trajectory programming decision scheme based on the predicted calculation of deviated [...] Read more.
UAV formation keeping is an important research element due to its cooperative formation control. This study proposes a passive positioning model for UAVs based on the Monte Carlo strategy and provides a trajectory programming decision scheme based on the predicted calculation of deviated UAV predefined endpoint locations, effectively improving the efficiency of UAVs performing formation-keeping tasks during flight. Then, the simulation after sampling by Gaussian distribution is used to obtain the trajectory planning under simultaneous control of multiple cluster formations, and the feasibility, accuracy and stability of the proposed model are verified. This study provides useful guidance for UAV formation control applications. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning)
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26 pages, 5630 KiB  
Article
UAV4PE: An Open-Source Framework to Plan UAV Autonomous Missions for Planetary Exploration
by Julian Galvez-Serna, Fernando Vanegas, Shahzad Brar, Juan Sandino, David Flannery and Felipe Gonzalez
Drones 2022, 6(12), 391; https://doi.org/10.3390/drones6120391 - 2 Dec 2022
Cited by 7 | Viewed by 4042
Abstract
Autonomous Unmanned Aerial Vehicles (UAV) for planetary exploration missions require increased onboard mission-planning and decision-making capabilities to access full operational potential in remote environments (e.g., Antarctica, Mars or Titan). However, the uncertainty introduced by the environment and the limitation of available sensors has [...] Read more.
Autonomous Unmanned Aerial Vehicles (UAV) for planetary exploration missions require increased onboard mission-planning and decision-making capabilities to access full operational potential in remote environments (e.g., Antarctica, Mars or Titan). However, the uncertainty introduced by the environment and the limitation of available sensors has presented challenges for planning such missions. Partially Observable Markov Decision Processes (POMDPs) are commonly used to enable decision-making and mission-planning processes that account for environmental, perceptional (extrinsic) and actuation (intrinsics) uncertainty. Here, we propose the UAV4PE framework, a testing framework for autonomous UAV missions using POMDP formulations. This framework integrates modular components for simulation, emulation, UAV guidance, navigation and mission planning. State-of-the-art tools such as python, C++, ROS, PX4 and JuliaPOMDP are employed by the framework, and we used python data-science libraries for the analysis of the experimental results. The source code and the experiment data are included in the UAV4PE framework. The POMDP formulation proposed here was able to plan and command a UAV-based planetary exploration mission in simulation, emulation and real-world experiments. The experiments evaluated key indicators such as the mission success rate, the surface area explored and the number of commands (actions) executed. We also discuss future work aimed at improving the UAV4PE framework, and the autonomous UAV mission planning formulation for planetary exploration. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning)
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17 pages, 2052 KiB  
Article
Joint Efficient UAV Trajectory and Velocity Optimization for IoT Data Collection Using a New Projection Algorithm
by Kuangyu Zheng, Zimo Ma, Mingyue Zhao, Zhuyang Zhou, Ziheng Zhang and Yifeng Li
Drones 2022, 6(12), 376; https://doi.org/10.3390/drones6120376 - 24 Nov 2022
Viewed by 2187
Abstract
Unmanned aerial vehicle (UAV)-assisted networking and communications are increasingly used in different applications, especially in the data collection of distributed Internet of Things (IoT) systems; its advantages include great flexibility and scalability. However, due to the UAV’s very limited battery capacity, the UAV [...] Read more.
Unmanned aerial vehicle (UAV)-assisted networking and communications are increasingly used in different applications, especially in the data collection of distributed Internet of Things (IoT) systems; its advantages include great flexibility and scalability. However, due to the UAV’s very limited battery capacity, the UAV energy efficiency has become a bottleneck for longer working time and larger area coverage. Therefore, it is critical to optimize the path and speed of the UAV with less energy consumption, while guaranteeing data collection under the workload and time requirements. In this paper, as a key finding, by analyzing the speed–power and the speed–energy relationships of UAVs, we found that there should be different speed selection strategies under different scenarios (i.e., fixed time or fixed distance), which can lead to much-improved energy efficiency. Moreover, we propose CirCo, a novel algorithm that jointly optimizes UAV trajectory and velocity for minimized energy consumption. CirCo is based on an original projection method, turning a 3D problem (GN locations and transmission ranges on the 2D plane, plus the minimum transmission time requirements on the temporal dimensions) into a 2D problem, which could help to directly find the feasible UAV crossing window, which greatly reduces the optimization complexity. Moreover, CirCo can classify the projected conditions to calculate the optimal path and speed schedule under each category, so that the energy consumption of each situation can be fine-regulated. The experiments demonstrate that CirCo can save as much as 54.3% of energy consumption and 62.9% of flight time over existing approaches. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning)
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17 pages, 2452 KiB  
Article
Sliding Mode Disturbance Observer-Based Adaptive Dynamic Inversion Fault-Tolerant Control for Fixed-Wing UAV
by Zhe Dong, Kai Liu and Shipeng Wang
Drones 2022, 6(10), 295; https://doi.org/10.3390/drones6100295 - 10 Oct 2022
Cited by 9 | Viewed by 2752
Abstract
Unmanned aerial vehicles (UAVs) have been widely applied over the past decades, especially in the military field. Due to the unpredictability of the flight environment and failures, higher requirements are placed on the design of the control system of the fixed-wing UAV. In [...] Read more.
Unmanned aerial vehicles (UAVs) have been widely applied over the past decades, especially in the military field. Due to the unpredictability of the flight environment and failures, higher requirements are placed on the design of the control system of the fixed-wing UAV. In this study, a sliding mode disturbance observer-based (SMDO) adaptive dynamic inversion fault-tolerant controller was designed, which includes an outer-loop sliding mode observer-based disturbance suppression dynamic inversion controller and an inner-loop real-time aerodynamic identification-based adaptive fault-tolerant dynamic inversion controller. The sliding mode disturbance observer in the outer-loop controller was designed based on the second-order super-twisting algorithm to alleviate chattering. The aerodynamic identification in the inner-loop controller adopts the recursive least squares algorithm to update the aerodynamic model of the UAV online, thereby realizing the fault-tolerant control for the control surface damage. The effectiveness of the proposed SMDO enhanced adaptive fault-tolerant control method was validated by mathematical simulation. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning)
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24 pages, 1044 KiB  
Article
Active Disturbance Rejection Control for the Robust Flight of a Passively Tilted Hexarotor
by Santos Miguel Orozco Soto, Jonathan Cacace, Fabio Ruggiero and Vincenzo Lippiello
Drones 2022, 6(9), 258; https://doi.org/10.3390/drones6090258 - 17 Sep 2022
Cited by 8 | Viewed by 3076
Abstract
This paper presents a robust control strategy for controlling the flight of an unmanned aerial vehicle (UAV) with a passively (fixed) tilted hexarotor. The proposed controller is based on a robust extended-state observer to estimate and reject internal dynamics and external disturbances at [...] Read more.
This paper presents a robust control strategy for controlling the flight of an unmanned aerial vehicle (UAV) with a passively (fixed) tilted hexarotor. The proposed controller is based on a robust extended-state observer to estimate and reject internal dynamics and external disturbances at runtime. Both the stability and convergence of the observer are proved using Lyapunov-based perturbation theory and an ultimate bound approach. Such a controller is implemented within a highly realistic simulation environment that includes physics motors, showing an almost identical behavior to that of a real UAV. The controller was tested for flying under normal conditions and in the presence of different types of disturbances, showing successful results. Furthermore, the proposed control system was compared with another robust control approach, and it presented a better performance regarding the attenuation of the error signals. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning)
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19 pages, 3855 KiB  
Article
Adaptive Neural-Network-Based Nonsingular Fast Terminal Sliding Mode Control for a Quadrotor with Dynamic Uncertainty
by Shurui Huang and Yueneng Yang
Drones 2022, 6(8), 206; https://doi.org/10.3390/drones6080206 - 12 Aug 2022
Cited by 13 | Viewed by 2615
Abstract
This paper proposes an adaptive neural-network-based nonsingular fast terminal sliding mode (NN-NFTSMC) approach to address the trajectory tracking control problem of a quadrotor in the presence of model uncertainties and external disturbances. First, the dynamic model of the quadrotor with uncertainty is derived. [...] Read more.
This paper proposes an adaptive neural-network-based nonsingular fast terminal sliding mode (NN-NFTSMC) approach to address the trajectory tracking control problem of a quadrotor in the presence of model uncertainties and external disturbances. First, the dynamic model of the quadrotor with uncertainty is derived. Then, a control scheme using nonsingular fast terminal sliding mode control (NFTSMC) is proposed to guarantee the finite-time convergence of the quadrotor to its desired trajectory. NFTSMC is firstly formulated for the case that the upper bound of the lumped uncertainty is known in advance. Under this framework, a disturbance observer by using the hyperbolic tangent nonlinear tracking differentiator (TANH-NTD) is designed to estimate the external interference, and a neural network (NN) approximator is used to develop an online estimate of the model uncertainty. Subsequently, adaptive algorithms are designed to compensate the approximation error and update the NN weight matrix. An NN-NFTSMC algorithm is formulated to provide the system with robustness to the model uncertainty and external disturbance. Moreover, Lyapunov-based approach is employed to prove the global stability of the closed-loop system and the finite-time convergence of the trajectory tracking errors. The results of a comparative simulation study with other recent methods illustrate the proposed control method reduces the chattering effectively and has remarkable performance. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning)
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26 pages, 9549 KiB  
Article
Modeling Fuzzy and Adaptive Human Behavior for Aircraft with Dynamic-Pitch-Control Envelope Cue
by Shuting Xu, Wenqian Tan, Yu Wu and Liguo Sun
Drones 2022, 6(5), 121; https://doi.org/10.3390/drones6050121 - 9 May 2022
Cited by 1 | Viewed by 2589
Abstract
As one of the key issues in aviation safety, loss-of-control in the form of adverse aircraft-pilot couplings is attracting attention increasingly. Dynamic-pitch-control envelope shows to be a promising means to evaluate the loss-of-control related to pilot-induced oscillations. To mitigate this issue, this paper [...] Read more.
As one of the key issues in aviation safety, loss-of-control in the form of adverse aircraft-pilot couplings is attracting attention increasingly. Dynamic-pitch-control envelope shows to be a promising means to evaluate the loss-of-control related to pilot-induced oscillations. To mitigate this issue, this paper develops a human pilot model with the dynamic-pitch-control envelope cue. A key feature of the model is the capability to afford the characteristics of the pilot’s behavior through analyzing the cue of envelope boundaries in different areas. The fuzziness and adaption of the human are introduced into the model to describe the behavior of the human pilot. Fuzzy control logic is designed to reflect the fuzziness of the human’s response to the envelope cue. Time-varying parameters are adjusted to embody the adaptive characteristics of the human pilot to different regional envelope cues. Furthermore, three metrics methods, including error metric, envelope boundaries metric, and scalogram-based pilot-induced oscillation (PIO) metric, are proposed to design the dynamic-pitch-control envelope cues. The assessment results obtained by pilot–aircraft system simulation are compared with the pilot-in-the-loop flight experiment in-ground simulator to validate the effectiveness of the model. Simulation and experimental results show that the proposed human pilot model and envelope cue method can be applied to mitigate the loss-of-control events caused by the pilot–aircraft system oscillations. Full article
(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning)
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