Swarms of Unmanned Aerial Vehicles — A Survey

Tahir, Anam; Böling, Jari M.; Haghbayan, Mohammad-Hashem; Toivonen, Hannu; Plosila, Juha

doi:10.1016/j.jii.2019.100106

Cited by 195 publications

(99 citation statements)

References 7 publications

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“…While aircraft UAV is capable of reaching altitudes above 12 km [293], small UAVs still have limited altitude. Limitations to available weight capacity reduce the availability of sensors that can assess specific coastal and marine components, especially for a small UAV [294]. Operational safety for UAV missions remains challenging.…”

Section: Technology-based Challengementioning

confidence: 99%

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

et al. 2020

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Coastal and marine management require the evaluation of multiple environmental threats and issues. However, there are gaps in the necessary data and poor access or dissemination of existing data in many countries around the world. This research identifies how remote sensing can contribute to filling these gaps so that environmental agencies, such as the United Nations Environmental Programme, European Environmental Agency, and International Union for Conservation of Nature, can better implement environmental directives in a cost-effective manner. Remote sensing (RS) techniques generally allow for uniform data collection, with common acquisition and reporting methods, across large areas. Furthermore, these datasets are sometimes open-source, mainly when governments finance satellite missions. Some of these data can be used in holistic, coastal and marine environmental management frameworks, such as the DAPSI(W)R(M) framework (Drivers–Activities–Pressures–State changes–Impacts (on Welfare)–Responses (as Measures), an updated version of Drivers–Pressures–State–Impact–Responses. The framework is a useful and holistic problem-structuring framework that can be used to assess the causes, consequences, and responses to change in the marine environment. Six broad classifications of remote data collection technologies are reviewed for their potential contribution to integrated marine management, including Satellite-based Remote Sensing, Aerial Remote Sensing, Unmanned Aerial Vehicles, Unmanned Surface Vehicles, Unmanned Underwater Vehicles, and Static Sensors. A significant outcome of this study is practical inputs into each component of the DAPSI(W)R(M) framework. The RS applications are not expected to be all-inclusive; rather, they provide insight into the current use of the framework as a foundation for developing further holistic resource technologies for management strategies in the future. A significant outcome of this research will deliver practical insights for integrated coastal and marine management and demonstrate the usefulness of RS to support the implementation of environmental goals, descriptors, targets, and policies, such as the Water Framework Directive, Marine Strategy Framework Directive, Ocean Health Index, and United Nations Sustainable Development Goals. Additionally, the opportunities and challenges of these technologies are discussed.

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Section: Technology-based Challengementioning

confidence: 99%

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

et al. 2020

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“…To overcome this problem, several quadrotor control methods were investigated such as fuzzy, intelligent control, dynamic surface control, backstepping control, etc. [16,17]. In [18], the authors presented a novel approximate dynamic programming deep learning algorithm for enabling a quadrotor systems to perform a trajectory tracking in the presence of adversarial inputs.…”

Section: Introductionmentioning

confidence: 99%

Robust Backstepping Trajectory Tracking Control of a Quadrotor with Input Saturation via Extended State Observer

Xuan-Mung

Hong

2019

Applied Sciences

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Quadrotor unmanned aerial vehicles have become increasingly popular in several applications, and the improvement of their control performance has been documented in several studies. Nevertheless, the design of a high-performance tracking controller for aerial vehicles that reliably functions in the simultaneous presence of model uncertainties, external disturbances, and control input saturation still remains a challenge. In this paper, we present a robust backstepping trajectory tracking control of a quadrotor with input saturation. The controller design accounts for both parameterized uncertainties and external disturbances, whereas a new auxiliary system is proposed to cope with control input saturation. Taking into account that only the position and attitude of the quadrotor are measurable, we devise an extended state observer to supply the estimations of unmeasured states, model uncertainties, and external disturbances. We strictly prove the stability of the closed-loop system by using the Lyapunov theory and demonstrate the effectiveness of the proposed algorithm through numerical simulations.

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“…A decoupling attitude parameterization was presented in [19]. It allows the design of an independent and straightforward position heading tracking control using the backstepping control technique.Some works try to overcome uncertainties (e.g., sensor noise, parametric uncertainties and external disturbance) by designing adaptive controllers [22,23]. In another recent work [24] a flight controller based on a Neural Network model has been presented for stabilization and trajectory control.…”

mentioning

confidence: 99%

“…This combination shows a significant improvement of the performance in position control as well as the compensation of large external forces.Generally, many studies where a review of control techniques can be found. Such studies are addressed in [22,23,31]. Most of them are pointing out their advantages and disadvantages, but the experiments with the same UAV model and various control techniques are missing in the literature.…”

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confidence: 99%

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Control Methods Comparison for the Real Quadrotor on an Innovative Test Stand

et al. 2020

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This article is a continuation of our previously published work that presented a comparison of nine attitude quaternion-based controllers of the quadrotor in simulation environment. In this article, the best three controllers were implemented into the real quadrotor. Namely proportional derivative (PD), linear quadratic regulator (LQR) and backstepping quaternion-based control techniques were evaluated. As a suitable test stand was not available on the basis of literature analysis, the article also outlines the requirements and the development of a new innovative test stand. In order to provide a comprehensive overview, the hardware and software that was used is also presented in the article. The main contribution of this article is a performance comparison of the controllers, which was based on absolute quaternion (positioning) error and energy consumption.The quadrotor is coupled and underactuated system (i.e., six degrees of freedom are controlled only by four actuators). The translational motion of the quadrotor depends on the attitude of the quadrotor as can be seen from the equations in [9]. By this means, the quadrotor is a cascade system [10]. Therefore, a cascade structure of a controller is usually applied to control position and attitude of the quadrotor. Various controllers based on classic or modern control theory were already designed and verified. The non-linear or linear model of a quadrotor is used depending on the chosen control method [11].The linear model of a quadrotor is achieved by linearization of the non-linear model around a hovering point. Controllers using the linearized model generally perform well around this hovering point. When the quadrotor goes away from the linearized point, the performance may worsen. Furthermore, the input saturation can cause control failure when large rapid maneuvers are required [3,5,12,13]. The main advantages of linear controllers are simplicity and ease of implementation to a real platform. The disadvantage of this approach is the use of the linearized model of the quadrotor during the process of designing a controller. Commonly used linear controllers are a linear quadratic regulator (LQR) and a proportional derivative (PD) controller [14][15][16].Another group of controllers consists of a wide variety of non-linear controllers. The serious disadvantage of these controllers is the complexity that prevents the wide adoption of non-linear controllers in real applications. Among non-linear methods, the backstepping control technique based on the Lyapunov function is widely adopted due to its systematic design and an intuitive approach [17]. The proposed control law is based on the compensation of non-linear forces or torques depending on whether an attitude or position controller is being designed. Applying Lyapunov stability analysis proves that the closed-loop system is asymptotically stable. This approach was used to stabilize the quadrotor in [3,5,12,[18][19][20][21].A backstepping-based inverse optimal attitude controller (BIOAC) was derived in [3] ta...

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Swarms of Unmanned Aerial Vehicles — A Survey

Cited by 195 publications

References 7 publications

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

Robust Backstepping Trajectory Tracking Control of a Quadrotor with Input Saturation via Extended State Observer

Control Methods Comparison for the Real Quadrotor on an Innovative Test Stand

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