Drones

Research

23 pages, 3816 KiB

Open AccessArticle

Integration of Deep Sequence Learning-Based Virtual GPS Model and EKF for AUV Navigation

by Peng-Fei Lv, Jun-Yi Lv, Zhi-Chao Hong and Li-Xin Xu

Drones 2024, 8(9), 441; https://doi.org/10.3390/drones8090441 - 29 Aug 2024

Viewed by 653

To address the issue of increasing navigation errors in low-cost autonomous underwater vehicles (AUVs) operating without assisted positioning underwater, this paper proposes a Virtual GPS Model (VGPSM) based on deep sequence learning. This model is integrated with an Extended Kalman Filter (EKF) to [...] Read more.

To address the issue of increasing navigation errors in low-cost autonomous underwater vehicles (AUVs) operating without assisted positioning underwater, this paper proposes a Virtual GPS Model (VGPSM) based on deep sequence learning. This model is integrated with an Extended Kalman Filter (EKF) to provide a high-precision navigation solution for AUVs. The VGPSM leverages the time-series characteristics of data from sensors such as the Attitude and Heading Reference System (AHRS) and the Doppler Velocity Log (DVL) while the AUV is on the surface. It learns the relationship between these sensor data and GPS data by utilizing a hybrid model of Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM), which are well-suited for processing and predicting time-series data. This approach constructs a virtual GPS model that generates virtual GPS displacements updated at the same frequency as the real GPS data. When the AUV navigates underwater, the virtual GPS displacements generated using the VGPSM in real-time are used as measurements to assist the EKF in state estimation, thereby enhancing the accuracy and robustness of underwater navigation. The effectiveness of the proposed method is validated through a series of experiments under various conditions. The experimental results demonstrate that the proposed method significantly reduces cumulative errors, with navigation accuracy improvements ranging from 29.2% to 69.56% compared to the standard EKF, indicating strong adaptability and robustness. Full article

(This article belongs to the Special Issue Navigation, Control and Mission Planning Advances for Safe, Efficient and Autonomous Drones)

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23 pages, 4285 KiB

Open AccessArticle

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 2 | Viewed by 1667

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

(This article belongs to the Special Issue Navigation, Control and Mission Planning Advances for Safe, Efficient and Autonomous Drones)

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11 pages, 2787 KiB

Open AccessArticle

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 - 8 Aug 2023

Cited by 1 | Viewed by 1482

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

(This article belongs to the Special Issue Navigation, Control and Mission Planning Advances for Safe, Efficient and Autonomous Drones)

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18 pages, 3743 KiB

Open AccessFeature PaperArticle

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 6 | Viewed by 3344

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

(This article belongs to the Special Issue Navigation, Control and Mission Planning Advances for Safe, Efficient and Autonomous Drones)

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14 pages, 4223 KiB

Open AccessArticle

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 - 3 Jun 2023

Cited by 5 | Viewed by 2579

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

(This article belongs to the Special Issue Navigation, Control and Mission Planning Advances for Safe, Efficient and Autonomous Drones)

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19 pages, 3027 KiB

Open AccessArticle

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 10 | Viewed by 2952

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

(This article belongs to the Special Issue Navigation, Control and Mission Planning Advances for Safe, Efficient and Autonomous Drones)

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32 pages, 10030 KiB

Open AccessEditor’s ChoiceArticle

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 18 | Viewed by 2831

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

(This article belongs to the Special Issue Navigation, Control and Mission Planning Advances for Safe, Efficient and Autonomous Drones)

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27 pages, 5604 KiB

Open AccessArticle

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 14 | Viewed by 3779

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

(This article belongs to the Special Issue Navigation, Control and Mission Planning Advances for Safe, Efficient and Autonomous Drones)

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