Survey of RPAS Autonomous Control Systems Using Artificial Intelligence

Aibin, Michał; Aldiab, Motasem; Bhavsar, Ruchi; Lodhra, Jasleen; Reyes, Mino; Rezaeian, Fifi; Saczuk, Eric; Taer, Mahsa; Taer, Maryam

doi:10.1109/access.2021.3136226

Cited by 12 publications

(9 citation statements)

References 75 publications

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“…It is crucial to note that the model under consideration, as represented by (1), encompasses a broad spectrum of autonomous systems. This inclusivity is achieved through the incorporation of both linear and nonlinear terms, rendering it applicable to diverse system dynamics.…”

Section: Design Of the Nonlinear Adaptive Observermentioning

confidence: 99%

“…An autonomous system, such as an autonomous drone, is a system that can achieve predefined objectives without or with a minimum degree of intervention from humans. 1 Furthermore, autonomous systems operate in a changing environment such as streets and hospitals, while robots operate in a more controlled environment, such as warehouses or factories. 2 Autonomous system technology exists thanks to the advancement of sensor and communication technology, and advanced control systems.…”

Section: Introductionmentioning

confidence: 99%

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Observer‐based fault diagnosis for autonomous systems

Hasan

2024

Intl J Robust & Nonlinear

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With the advancement in sensor and communication technology, autonomous systems have been incrementally reshaping the execution of tasks in commercial and military sectors. Since the systems are designed to complete tasks without or with minimal human intervention, fault diagnosis based on sensor data has been crucial to preventing accidents and fatalities. In this paper, fault diagnosis for autonomous systems is designed based on nonlinear adaptive observers, tested in numerical simulations, and implemented in a robotic platform. To this end, we utilize the persistence of excitation conditions on the parametric model of the faults. We derive sufficient conditions for the nonlinear adaptive observer in terms of linear matrix inequality to ensure the convergence of the estimates. Furthermore, we consider one‐sided Lipschitz conditions to obtain less conservative results. The main advantage of using the nonlinear adaptive observer is that the method converges quickly to the actual fault and requires minimum computational effort. However, solving the linear matrix inequality might not be trivial. Numerical simulations based on a single‐link flexible joint robot model and experimental tests in an autonomous quadcopter are performed to validate the effectiveness of the proposed method.

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Section: Design Of the Nonlinear Adaptive Observermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Observer‐based fault diagnosis for autonomous systems

Hasan

2024

Intl J Robust & Nonlinear

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“…In this regard, advanced ML-based information theoretic techniques were proposed to further improve the secrecy level of wireless transmissions. Since the implementation of Remotely Piloted Aircraft Systems (RPAS) with onboard intelligence that operate autonomously is highly desirable in real-world deployments, the role of Artificial Intelligence (AI) was explored in [15] without mentioning authentication issues concerning RPAs. Specifically, AI was considered as the key technology to avoid human intervention, reduce faults, and enhance the decision-making process in dynamic environments, thus ensuring accuracy, control system security, and safety.…”

Section: Previous Review Papersmentioning

confidence: 99%

A Review on Software-Based and Hardware-Based Authentication Mechanisms for the Internet of Drones

Michailidis

Vouyioukas

2022

Drones

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During the last few years, a wide variety of Internet of Drones (IoD) applications have emerged with numerous heterogeneous aerial and ground network elements interconnected and equipped with advanced sensors, computation resources, and communication units. The evolution of IoD networks presupposes the mitigation of several security and privacy threats. Thus, robust authentication protocols should be implemented in order to attain secure operation within the IoD. However, owing to the inherent features of the IoD and the limitations of Unmanned Aerial Vehicles (UAVs) in terms of energy, computational, and memory resources, designing efficient and lightweight authentication solutions is a non-trivial and complicated process. Recently, the development of authentication mechanisms for the IoD has received unprecedented attention. In this paper, up-to-date research studies on authentication mechanisms for IoD networks are presented. To this end, the adoption of conventional technologies and methods, such as the widely used hash functions, Public Key Infrastructure (PKI), and Elliptic-Curve Cryptography (ECC), is discussed along with emerging technologies, including Mobile Edge Computing (MEC), Machine Learning (ML), and Blockchain. Additionally, this paper provides a review of effective hardware-based solutions for the identification and authentication of network nodes within the IoD that are based on Trusted Platform Modules (TPMs), Hardware Security Modules (HSMs), and Physically Unclonable Functions (PUFs). Finally, future directions in these relevant research topics are given, stimulating further work.

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“…In addition, the uses of UAV swarms for modern applications are reported in [18]. Moreover, the survey paper re-lated to remotely piloted aircraft systems (RPAS) is presented by [19]. Thereby, the research gap of communication models using machine learning was identified.…”

Section: Introductionmentioning

confidence: 99%

A UAV-Swarm-Communication Model Using a Machine-Learning Approach for Search-and-Rescue Applications

Khalil

Rahman

Ullah

et al. 2022

Drones

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This paper presents a UAV-swarm-communication model using a machine-learning approach for search-and-rescue applications. Firstly, regarding the communication of UAVs, the receive signal strength (RSS) and power loss have been modeled using random forest regression, and the mathematical representation of the channel matrix has also been discussed. The second part consisted of swarm control modeling of UAVs; however, a dataset for five types of triangular swarm formations was generated, and K-means clustering was applied to predict the cluster. In order to obtain the correct swarm formation, the dendrogram of all types was investigated. Finally, the heat map and contour were plotted for all kinds of swarm clusters. Furthermore, it was observed that the RSS of proposed swarms had good agreement with swarm distances.

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Survey of RPAS Autonomous Control Systems Using Artificial Intelligence

Cited by 12 publications

References 75 publications

Observer‐based fault diagnosis for autonomous systems

Observer‐based fault diagnosis for autonomous systems

A Review on Software-Based and Hardware-Based Authentication Mechanisms for the Internet of Drones

A UAV-Swarm-Communication Model Using a Machine-Learning Approach for Search-and-Rescue Applications

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