Acoustic Localization for Autonomous Unmanned Systems

Kumar, Murali Manohar S; Yadav, Himanshu; Soman, Dawnee; Kumar, Ashish; N, Arun Kumar Reddy

doi:10.1109/iit50501.2020.9298972

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…An unmanned autonomous intelligent system (UAIS) [80][81][82][83][84][85][86][87][88] is defined as a mechatronic system capable of fulfilling universal tasks without or with limited human involvement. Typical UAIS include connected autonomous vehicles [89], swarm robot systems [90], unmanned aerial vehicles [91], unmanned ground vehicles, unmanned maritime vehicles [92], smart manufacturering systems [93][94][95][96], agricultural robots [97][98][99][100][101], service robots, and medical robots [102][103][104].…”

Section: Application Of Uav-aided Non-terrestrial Network In Uaismentioning

confidence: 99%

Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network

Wang,

Guo,

Gao

2024

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Non-terrestrial network (NTN) is a trending topic in the field of communication, as it shows promise for scenarios in which terrestrial infrastructure is unavailable. Unmanned autonomous intelligent systems (UAISs), as a physical form of artificial intelligence (AI), have gained significant attention from academia and industry. These systems have various applications in autonomous driving, logistics, area surveillance, and medical services. With the rapid evolution of information and communication technology (ICT), 5G and beyond-5G communication have enabled numerous intelligent applications through the comprehensive utilization of advanced NTN communication technology and artificial intelligence. To meet the demands of complex tasks in remote or communication-challenged areas, there is an urgent need for reliable, ultra-low latency communication networks to enable unmanned autonomous intelligent systems for applications such as localization, navigation, perception, decision-making, and motion planning. However, in remote areas, reliable communication coverage is not available, which poses a significant challenge for intelligent systems applications. The rapid development of non-terrestrial networks (NTNs) communication has shed new light on intelligent applications that require ubiquitous network connections in space, air, ground, and sea. However, challenges arise when using NTN technology in unmanned autonomous intelligent systems. Our research examines the advancements and obstacles in academic research and industry applications of NTN technology concerning UAIS, which is supported by unmanned aerial vehicles (UAV) and other low-altitude platforms. Nevertheless, edge computing and cloud computing are crucial for unmanned autonomous intelligent systems, which also necessitate distributed computation architectures for computationally intensive tasks and massive data offloading. This paper presents a comprehensive analysis of the opportunities and challenges of unmanned autonomous intelligent systems in UAV NTN, along with NTN-based unmanned autonomous intelligent systems and their applications. A field trial case study is presented to demonstrate the application of NTN in UAIS.

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Section: Application Of Uav-aided Non-terrestrial Network In Uaismentioning

confidence: 99%

Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network

Wang,

Guo,

Gao

2024

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“…There are many applications in which efficiently (accurately and promptly) solving the localization problem is crucial, such as navigation [21], underwater networks [22], surveillance [23,24], or power systems [25,26]. When considering the fourth industrial transformation and the fundamental advanced digital changes-known as Industry 4.0robust and precise localization can be seen as a key feature in pervasive systems in future industry and factory applications.…”

Section: Introductionmentioning

confidence: 99%

Swarm Optimization for Energy-Based Acoustic Source Localization: A Comprehensive Study

Fé

Correia

Tomic

et al. 2022

Sensors

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In the last decades, several swarm-based optimization algorithms have emerged in the scientific literature, followed by a massive increase in terms of their fields of application. Most of the studies and comparisons are restricted to high-level languages (such as MATLAB®) and testing methods on classical benchmark mathematical functions. Specifically, the employment of swarm-based methods for solving energy-based acoustic localization problems is still in its inception and has not yet been extensively studied. As such, the present work marks the first comprehensive study of swarm-based optimization algorithms applied to the energy-based acoustic localization problem. To this end, a total of 10 different algorithms were subjected to an extensive set of simulations with the following aims: (1) to compare the algorithms’ convergence performance and recognize novel, promising methods for solving the problem of interest; (2) to validate the importance (in convergence speed) of an intelligent swarm initialization for any swarm-based algorithm; (3) to analyze the methods’ time efficiency when implemented in low-level languages and when executed on embedded processors. The obtained results disclose the high potential of some of the considered swarm-based optimization algorithms for the problem under study, showing that these methods can accurately locate acoustic sources with low latency and bandwidth requirements, making them highly attractive for edge computing paradigms.

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Acoustic Localization for Autonomous Unmanned Systems

Cited by 2 publications

References 7 publications

Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network

Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network

Swarm Optimization for Energy-Based Acoustic Source Localization: A Comprehensive Study

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