Intelligence of Autonomous Vehicles: A Concise Revisit

Naz, Neelma; Ehsan, Muhammad Khurram; Amirzada, Muhammad Rizwan; Ali, Md. Yeakub; Qureshi, Muhammad Aasim

doi:10.1155/2022/2690164

Cited by 20 publications

(10 citation statements)

References 61 publications

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“…The algorithm proposed in this research has worst-case time bound of OðV + EÞ, which is linear in time and hence is better than the one presented in [29] for routing in intelligent networks [38][39][40][41][42].…”

Section: Discussionmentioning

confidence: 95%

A Robust Graph Theoretic Solution of Routing in Intelligent Networks

Qureshi

Hassan

Ehsan

et al. 2022

Wireless Communications and Mobile Computing

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Implementation of robust routing is very critical in network communication. Connecting devices like routers maintain databases for the whole network topology in the routing table. Each router needs to keep these tables updated with the best possible routes so that an efficient communication can always take place in nondelay tolerant intelligent networks that include military and tactical systems, vehicular communication networks, underwater acoustic networks, and intelligent sensor networks. The fast construction of shortest-path tree (SPT) is important to devise an efficient routing in a nondelay tolerant networks. That is why a simple and efficient algorithm is the need of the time. A robust routing solution SPT with O V + E time complexity is proposed to supersede the existing landmark.

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Section: Discussionmentioning

confidence: 95%

A Robust Graph Theoretic Solution of Routing in Intelligent Networks

Qureshi

Hassan

Ehsan

et al. 2022

Wireless Communications and Mobile Computing

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“…Artificial intelligence (AI) plays a significant role in perception, path planning, and control techniques in various complex environments. Yet, there is much scope to improve the performance of AGV [ 105 ]. However, implementing vehicular algorithms requires high computing to meet this demand.…”

Section: Discussionmentioning

confidence: 99%

Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review

Islam¹,

Nabi²,

Ball³

2022

Sensors

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When it comes to some essential abilities of autonomous ground vehicles (AGV), detection is one of them. In order to safely navigate through any known or unknown environment, AGV must be able to detect important elements on the path. Detection is applicable both on-road and off-road, but they are much different in each environment. The key elements of any environment that AGV must identify are the drivable pathway and whether there are any obstacles around it. Many works have been published focusing on different detection components in various ways. In this paper, a survey of the most recent advancements in AGV detection methods that are intended specifically for the off-road environment has been presented. For this, we divided the literature into three major groups: drivable ground and positive and negative obstacles. Each detection portion has been further divided into multiple categories based on the technology used, for example, single sensor-based, multiple sensor-based, and how the data has been analyzed. Furthermore, it has added critical findings in detection technology, challenges associated with detection and off-road environment, and possible future directions. Authors believe this work will help the reader in finding literature who are doing similar works.

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“…A navigation approach using an Artificial Intelligence (AI) algorithm is developed to solve problems more quickly, accurate and reliable in optimization [100]. This approach has proven to be effective and is now widely used in autonomous navigation [9], [101], [102]. Bitsch and Schweitzer [103] investigated that, in order to provide a conceptual framework for addressing a list of requirements that compares several machine learning algorithms in order to determine the most effective AI algorithm for intelligent AGV control.…”

Section: Artificial Intelligence Approachmentioning

confidence: 99%

A Survey on Navigation Approaches for Automated Guided Vehicle Robots in Dynamic Surrounding

2023

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Automated Guided Vehicle (AGV) have received a lot of attention in recent years in terms of both hardware and software research. Nowadays, the AGV offer more adaptable and effective industrial and transportation system solutions. An AGV navigation techniques is essential to its operation. The decision to use AGV navigation is not simple, though to be suite to the application and sufficient. This paper survey the navigation approaches applied in AGV in the past five years of published academic research. In doing so, this work responds to three related questions: (1) is the AGV classical navigation techniques still relevant to the current application area; (2) is the heuristic navigation techniques itself able to optimize AGV movement in term of guide and strategy; (3) is the use of Artificial Intelligence (AI) in AGV navigation techniques able to increase system performance. As a result, numerous techniques for the AGV navigation have been developed globally. On the other hand, the most popular navigation approaches are provided below for more research.

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Intelligence of Autonomous Vehicles: A Concise Revisit

Cited by 20 publications

References 61 publications

A Robust Graph Theoretic Solution of Routing in Intelligent Networks

A Robust Graph Theoretic Solution of Routing in Intelligent Networks

Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review

A Survey on Navigation Approaches for Automated Guided Vehicle Robots in Dynamic Surrounding

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