Navigation, Control and Mission Planning Advances for Safe, Efficient and Autonomous Drones

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

Deadline for manuscript submissions: 18 May 2024 | Viewed by 14813

Special Issue Editors


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Guest Editor
School of Future Transport Engineering, Faculty of Engineering, Environment and Computing, Coventry University, Coventry CV1 5FB, UK
Interests: fault tolerance; control; motion estimation; system identification; aircraft; spacecraft
Special Issues, Collections and Topics in MDPI journals
Graduate School of Engineering, The University of Tokyo, Tokyo, Japan
Interests: artificial intelligence; IoT; fault tolerance; fault diagnosis; optimisation; autonomous maintenance; drones

E-Mail Website
Guest Editor
Department of Aerospace Science & Technology, National & Kapodistrian University of Athens, 157 72 Athens, Greece
Interests: attitude determination and control, UAV, control, satellite technology, spacecraft propulsion, autonomy
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 entitled “Navigation, Control and Mission Planning Advances for Safe, Efficient and Autonomous Drones”

Drones have considerably evolved in the last two decades, with an increased emphasis on safety, autonomy and performance to perform a wide range of missions. Air, ground, marine and even space vehicles are currently used for applications from land surveys to precision agriculture, disaster monitoring, forestry and other applications in society and in several industries.

Advances in navigation, control, as well as other enabling technologies from data handling to communication systems are necessary to safely meet the increased demand for autonomy. In a single drone, these challenges include the ability to maintain admissible or optimal flight performance using limited computational and power resources with the ability to handle faults, anomalies, as well as vehicle and mission constraints such as drone endurance and operational envelopes.

In UAV swarms and formations, the challenges extend to the need for mission-level architectures to coordinate the path planning and path following using centralised or decentralised navigation, control and communication systems, including ground station–vehicle communications.

The state-of-the-art methods used to address challenges in single and distributed drone systems are often based on advances in the navigation and control theory, increasingly based on machine learning, or a combination of those two approaches, such as artificial intelligence (AI)-enhanced navigation and control. Advances in new technologies such as the Internet of things and Detect and Avoid are also increasingly exploited to enhance navigation and control safety and performance.

This Special Issue will therefore bring together papers which describe recent research in the navigation, control and mission planning of drones, including ground, air, marine or space vehicles. Papers with theoretical, simulation and practical experimental results in this field are all encouraged. This includes review papers, tutorials, as well as original research papers.

Possible topics include, but are not limited to:

  • Advances in path planning and path following methods for drones;
  • Machine learning-based navigation and control or mission planning in drones;
  • Unmanned aerial vehicles (UAVs), autonomous underwater vehicles (AUVs), and spacecraft navigation and control;
  • Adaptive, optimal or robust control of drones;
  • Control under vehicle, operational and collision avoidance constraints;
  • Sensor fusion for drone navigation;
  • Hybrid and multimode navigation and control systems;
  • Linear and nonlinear motion estimation using filtering, observer-based and recursive methods;
  • Fault detection, isolation and recovery in drones;
  • Multi-vehicle networks and communication systems for coordinated drone navigation and control;
  • Coordinated navigation and control of formations and swarms of aerial, ground or space vehicles;
  • Distributed systems with different types of vehicles (eg. ground and air vehicles, air and space vehicles);
  • Advances in computer and data handling systems for increased navigation and control autonomy;
  • Dynamical modeling and/or control for emerging drone designs (hybrid UAV designs, eVToL);
  • Internet of thing applications in drone navigation and control;
  • System identification for drones.

Dr. Nadjim Horri
Dr. Samir Khan
Prof. Dr. Vaios Lappas
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

  • drone
  • path following
  • navigation
  • control
  • machine learning
  • autonomy
  • UAV
  • spacecraft

Published Papers (7 papers)

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Research

23 pages, 4285 KiB  
Article
Distributed Control for Multi-Robot Interactive Swarming Using Voronoi Partioning
by Alexandre Eudes, Sylvain Bertrand, Julien Marzat and Ioannis Sarras
Drones 2023, 7(10), 598; https://doi.org/10.3390/drones7100598 - 23 Sep 2023
Cited by 1 | Viewed by 1082
Abstract
The problem of safe navigation of a human-multi-robot system is addressed in this paper. More precisely, we propose a novel distributed algorithm to control a swarm of unmanned ground robots interacting with human operators in presence of obstacles. Contrary to many existing algorithms [...] Read more.
The problem of safe navigation of a human-multi-robot system is addressed in this paper. More precisely, we propose a novel distributed algorithm to control a swarm of unmanned ground robots interacting with human operators in presence of obstacles. Contrary to many existing algorithms that consider formation control, the proposed approach results in non-rigid motion for the swarm, which more easily enables interactions with human operators and navigation in cluttered environments. Each vehicle calculates distributively and dynamically its own safety zone in which it generates a reference point to be tracked. The algorithm relies on purely geometric reasoning through the use of Voronoi partitioning and collision cones, which allows to naturally account for inter-robot, human-robot and robot-obstacle interactions. Different interaction modes have been defined from this common basis to address the following practical problems: autonomous waypoint navigation, velocity-guided motion, and follow a localized operator. The effectiveness of the algorithm is illustrated by outdoor and indoor field experiments. Full article
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11 pages, 2787 KiB  
Article
Usability Comparison between 2D and 3D Control Methods for the Operation of Hovering Objects
by Daeseong Lee, Hajun Kim, Heesoo Yoon and Wonsup Lee
Drones 2023, 7(8), 520; https://doi.org/10.3390/drones7080520 - 08 Aug 2023
Viewed by 1002
Abstract
This paper experimentally analyzed the cognitive load of users based on different methods of operating hovering objects, such as drones. The traditional gamepad-type control method (2D) was compared with a control method that mapped the movement directions of the drone to the natural [...] Read more.
This paper experimentally analyzed the cognitive load of users based on different methods of operating hovering objects, such as drones. The traditional gamepad-type control method (2D) was compared with a control method that mapped the movement directions of the drone to the natural manipulation gestures of the user using a Leap Motion device (3D). Twenty participants operated the drone on an obstacle course using the two control methods. The drone’s trajectory was measured using motion-capture equipment with a reflective marker. The distance traveled by the drone, operation time, and trajectory smoothness were calculated and compared between the two control methods. The results showed that when the drone’s movements were mapped to the user’s natural directional gestures, the drone’s 3D movements were perceived as more natural and smoother. A more intuitive drone control method can reduce cognitive load and minimize operational errors, making it more user friendly and efficient. However, due to the users’ lack of familiarity with Leap Motion, it resulted in longer distance and time and lower subjective satisfaction; therefore, a more improved 3D control method over Leap Motion is needed to address the limitations. Full article
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18 pages, 3743 KiB  
Article
An Efficient Framework for Autonomous UAV Missions in Partially-Unknown GNSS-Denied Environments
by Michael Mugnai, Massimo Teppati Losé, Edwin Paúl Herrera-Alarcón, Gabriele Baris, Massimo Satler and Carlo Alberto Avizzano
Drones 2023, 7(7), 471; https://doi.org/10.3390/drones7070471 - 18 Jul 2023
Cited by 3 | Viewed by 2490
Abstract
Nowadays, multirotors are versatile systems that can be employed in several scenarios, where their increasing autonomy allows them to achieve complex missions without human intervention. This paper presents a framework for autonomous missions with low-cost Unmanned Aerial Vehicles (UAVs) in Global Navigation Satellite [...] Read more.
Nowadays, multirotors are versatile systems that can be employed in several scenarios, where their increasing autonomy allows them to achieve complex missions without human intervention. This paper presents a framework for autonomous missions with low-cost Unmanned Aerial Vehicles (UAVs) in Global Navigation Satellite System-denied (GNSS-denied) environments. This paper presents hardware choices and software modules for localization, perception, global planning, local re-planning for obstacle avoidance, and a state machine to dictate the overall mission sequence. The entire software stack has been designed exploiting the Robot Operating System (ROS) middleware and has been extensively validated in both simulation and real environment tests. The proposed solution can run both in simulation and in real-world scenarios without modification thanks to a small sim-to-real gap with PX4 software-in-the-loop functionality. The overall system has competed successfully in the Leonardo Drone Contest, an annual competition between Italian Universities with a focus on low-level, resilient, and fully autonomous tasks for vision-based UAVs, proving the robustness of the entire system design. Full article
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14 pages, 4223 KiB  
Article
Deep Reinforcement Learning for the Visual Servoing Control of UAVs with FOV Constraint
by Gui Fu, Hongyu Chu, Liwen Liu, Linyi Fang and Xinyu Zhu
Drones 2023, 7(6), 375; https://doi.org/10.3390/drones7060375 - 03 Jun 2023
Cited by 1 | Viewed by 1707
Abstract
Visual servoing is a control method that utilizes image feedback to control robot motion, and it has been widely applied in unmanned aerial vehicle (UAV) motion control. However, due to field-of-view (FOV) constraints, visual servoing still faces challenges, such as easy target loss [...] Read more.
Visual servoing is a control method that utilizes image feedback to control robot motion, and it has been widely applied in unmanned aerial vehicle (UAV) motion control. However, due to field-of-view (FOV) constraints, visual servoing still faces challenges, such as easy target loss and low control efficiency. To address these issues, visual servoing control for UAVs based on the deep reinforcement learning (DRL) method is proposed, which dynamically adjusts the servo gain in real time to avoid target loss and improve control efficiency. Firstly, a Markov model of visual servoing control for a UAV under field-of-view constraints is established, which consists ofquintuplet and considers the improvement of the control efficiency. Secondly, an improved deep Q-network (DQN) algorithm with a target network and experience replay is designed to solve the Markov model. In addition, two independent agents are designed to adjust the linear and angular velocity servo gains in order to enhance the control performance, respectively. In the simulation environment, the effectiveness of the proposed method was verified using a monocular camera. Full article
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19 pages, 3027 KiB  
Article
Path Planning of Autonomous Mobile Robots Based on an Improved Slime Mould Algorithm
by Ling Zheng, Yan Tian, Hu Wang, Chengzhi Hong and Bijun Li
Drones 2023, 7(4), 257; https://doi.org/10.3390/drones7040257 - 11 Apr 2023
Cited by 7 | Viewed by 1924
Abstract
Path planning is a crucial component of autonomous mobile robot (AMR) systems. The slime mould algorithm (SMA), as one of the most popular path-planning approaches, shows excellent performance in the AMR field. Despite its advantages, there is still room for SMA to improve [...] Read more.
Path planning is a crucial component of autonomous mobile robot (AMR) systems. The slime mould algorithm (SMA), as one of the most popular path-planning approaches, shows excellent performance in the AMR field. Despite its advantages, there is still room for SMA to improve due to the lack of a mechanism for jumping out of local optimization. This means that there is still room for improvement in the path planning of ARM based on this method. To find shorter and more stable paths, an improved SMA, called the Lévy flight-rotation SMA (LRSMA), is proposed. LRSMA utilizes variable neighborhood Lévy flight and an individual rotation perturbation and variation mechanism to enhance the local optimization ability and prevent falling into local optimization. Experiments in varying environments demonstrate that the proposed algorithm can generate the ideal collision-free path with the shortest length, higher accuracy, and robust stability. Full article
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32 pages, 10030 KiB  
Article
An Improved Probabilistic Roadmap Planning Method for Safe Indoor Flights of Unmanned Aerial Vehicles
by Qingeng Jin, Qingwu Hu, Pengcheng Zhao, Shaohua Wang and Mingyao Ai
Drones 2023, 7(2), 92; https://doi.org/10.3390/drones7020092 - 28 Jan 2023
Cited by 8 | Viewed by 2072
Abstract
Unmanned aerial vehicles (UAVs) have been widely used in industry and daily life, where safety is the primary consideration, resulting in their use in open outdoor environments, which are wider than complex indoor environments. However, the demand is growing for deploying UAVs indoors [...] Read more.
Unmanned aerial vehicles (UAVs) have been widely used in industry and daily life, where safety is the primary consideration, resulting in their use in open outdoor environments, which are wider than complex indoor environments. However, the demand is growing for deploying UAVs indoors for specific tasks such as inspection, supervision, transportation, and management. To broaden indoor applications while ensuring safety, the quadrotor is notable for its motion flexibility, particularly in the vertical direction. In this study, we developed an improved probabilistic roadmap (PRM) planning method for safe indoor flights based on the assumption of a quadrotor model UAV. First, to represent and model a 3D environment, we generated a reduced-dimensional map using a point cloud projection method. Second, to deploy UAV indoor missions and ensure safety, we improved the PRM planning method and obtained a collision-free flight path for the UAV. Lastly, to optimize the overall mission, we performed postprocessing optimization on the path, avoiding redundant flights. We conducted experiments to validate the effectiveness and efficiency of the proposed method on both desktop and onboard PC, in terms of path-finding success rate, planning time, and path length. The results showed that our method ensures safe indoor UAV flights while significantly improving computational efficiency. Full article
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27 pages, 5604 KiB  
Article
Finite-Time Neuro-Sliding-Mode Controller Design for Quadrotor UAVs Carrying Suspended Payload
by Özhan Bingöl and Hacı Mehmet Güzey
Drones 2022, 6(10), 311; https://doi.org/10.3390/drones6100311 - 21 Oct 2022
Cited by 10 | Viewed by 3074
Abstract
Due to the quadrotor’s underactuated nature, suspended payload dynamics, parametric uncertainties, and external disturbances, designing a controller for tracking the desired trajectories for a quadrotor that carries a suspended payload is a challenging task. Furthermore, one of the most significant disadvantages of designing [...] Read more.
Due to the quadrotor’s underactuated nature, suspended payload dynamics, parametric uncertainties, and external disturbances, designing a controller for tracking the desired trajectories for a quadrotor that carries a suspended payload is a challenging task. Furthermore, one of the most significant disadvantages of designing a controller for nonlinear systems is the infinite-time convergence to the desired trajectory. In this paper, a finite-time neuro-sliding mode controller (FTNSMC) for a quadrotor with a suspended payload that is subject to parametric uncertainties and external disturbances is designed. By constructing a finite-time sliding mode controller, the quadrotor can follow the reference trajectories in finite time. Furthermore, despite time-varying nonlinear dynamics, parametric uncertainties, and external disturbances, a neural network structure is added to the controller to effectively reduce chattering phenomena caused by high switching gains, and significantly reduce the size of the control signals. Following the completion of the controller design, the system’s stability is demonstrated using the Lyapunov stability criterion. Extensive numerical simulations with various scenarios are run to demonstrate the effectiveness of the proposed controller. Full article
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