Artificial intelligence, machine learning and deep learning in advanced robotics, a review

Cognitive Robotics, Journal Year: 2023, Volume and Issue: 3, P. 54 - 70

Published: Jan. 1, 2023

Artificial Intelligence (AI), Machine Learning (ML), and Deep (DL) have revolutionized the field of advanced robotics in recent years. AI, ML, DL are transforming robotics, making robots more intelligent, efficient, adaptable to complex tasks environments. Some applications include autonomous navigation, object recognition manipulation, natural language processing, predictive maintenance. These technologies also being used development collaborative (cobots) that can work alongside humans adapt changing environments tasks. The be transportation systems order provide safety, efficiency, convenience passengers companies . Also, playing a critical role advancement manufacturing assembly robots, enabling them efficiently, safely, intelligently. Furthermore, they wide range aviation management, helping airlines improve reduce costs, customer satisfaction. Moreover, help taxi better, safer services customers. research presents an overview current developments discusses various robot modification. Further works regarding suggested fill gaps between existing studies published papers. By reviewing systems, it is possible investigate modify performances enhance productivity robotic industries.

Language: Английский

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UAV Fault Detection Methods, State-of-the-Art

Drones, Journal Year: 2022, Volume and Issue: 6(11), P. 330 - 330

Published: Oct. 29, 2022

The continual expansion of the range applications for unmanned aerial vehicles (UAVs) is resulting in development more and sophisticated systems. greater complexity UAV, likelihood that a component will fail. Due to fact drones often operate close proximity humans, reliability flying robots, which directly affects level safety, becoming important. This review article presents recent research works on fault detection They include papers published between January 2016 August 2022. Web Science Google Scholar databases were used search articles. Terminology related was as keywords. articles analyzed, each paper briefly summarized most important details concerning described table.

Language: Английский

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Internet of drones security: Taxonomies, open issues, and future directions

Vehicular Communications, Journal Year: 2022, Volume and Issue: 39, P. 100552 - 100552

Published: Nov. 25, 2022

Language: Английский

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Secure communication in IOT-based UAV networks: A systematic survey

Internet of Things, Journal Year: 2023, Volume and Issue: 23, P. 100883 - 100883

Published: July 22, 2023

Language: Английский

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Artificial Intelligence in Aviation: New Professionals for New Technologies

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(21), P. 11660 - 11660

Published: Oct. 25, 2023

Major aviation organizations have highlighted the need to adopt artificial intelligence (AI) transform operations and improve efficiency safety. However, industry requires qualified graduates with relevant AI competencies meet this demand. This study analyzed engineering bachelor’s programs at European universities determine if they are preparing students for integration in by incorporating AI-related topics. The analysis focused on program descriptions syllabi using semantic annotation. results showed a limited focus machine learning competencies, more emphasis foundational digital skills. Reasons include newness of AI, its specialized nature, implementation challenges. As evolves, dedicated may emerge. But currently, curricula appear misaligned stated goals adoption. provides an analytical methodology competency framework help educators address gap. Producing equipped literacy collaboration skills will be key aviation’s intelligent future.

Language: Английский

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Navigating the Crescendo of Challenges in Harnessing Artificial Intelligence for Disaster Management

Advances in computational intelligence and robotics book series, Journal Year: 2024, Volume and Issue: unknown, P. 64 - 94

Published: Feb. 16, 2024

Global warming worsens natural disasters and humanitarian issues. Disaster prediction relies on satellites weather stations. AI may help catastrophe management. reduces disaster risk in many ways. Early warning systems, forecasts, recovery, reconstruction improve. could us predict, prepare, recover from calamities. These technologies provide climate change mitigation community protection hope. They propose a better future amid catastrophes. DRR is aggressively adopting AI, notably ML. This field encompasses severe event prediction, hazard mapping, real-time detection, situational awareness, decision assistance, more. Growing usage of management raises questions about its benefits. We face what issues? How can these difficulties be resolved opportunities maximised? What tell policymakers, stakeholders, the public to reduce disasters? The chapter introduces implementation issues, solutions make world more peaceful will addressed.

Language: Английский

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Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(5), P. 1844 - 1844

Published: Feb. 23, 2024

Land-area classification (LAC) research offers a promising avenue to address the intricacies of urban planning, agricultural zoning, and environmental monitoring, with specific focus on areas their complex land usage patterns. The potential LAC is significantly propelled by advancements in high-resolution satellite imagery machine learning strategies, particularly use convolutional neural networks (CNNs). Accurate paramount for informed development effective management. Traditional remote-sensing methods encounter limitations precisely classifying dynamic areas. Therefore, this study, we investigated application transfer Inception-v3 DenseNet121 architectures establish reliable system identifying classes. Leveraging these models provided distinct advantages, as it allows benefit from pre-trained features large datasets, enhancing model generalization performance compared starting scratch. Transfer also facilitates utilization limited labeled data fine-tuning, making valuable strategy optimizing accuracy tasks. Moreover, strategically employ fine-tuned versions networks, emphasizing transformative impact architectures. fine-tuning process enables leverage pre-existing knowledge extensive its adaptability LC classification. By aligning advanced techniques, our not only contributes evolution methodologies but underscores importance incorporating cutting-edge methodologies, such network architectures, continual enhancement systems. Through experiments conducted UC-Merced_LandUse dataset, demonstrate effectiveness approach, achieving remarkable results, including 92% accuracy, 93% recall, precision, F1-score. employing heatmap analysis further elucidates decision-making models, providing insights into mechanism. successful CNNs LAC, coupled analysis, opens avenues enhanced monitoring through more accurate automated land-area

Language: Английский

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Multi-UAV Autonomous Path Planning in Reconnaissance Missions Considering Incomplete Information: A Reinforcement Learning Method

Drones, Journal Year: 2022, Volume and Issue: 7(1), P. 10 - 10

Published: Dec. 23, 2022

Unmanned aerial vehicles (UAVs) are important in reconnaissance missions because of their flexibility and convenience. Vitally, UAVs capable autonomous navigation, which means they can be used to plan safe paths target positions dangerous surroundings. Traditional path-planning algorithms do not perform well when the environmental state is dynamic partially observable. It difficult for a UAV make correct decision with incomplete information. In this study, we proposed multi-UAV path planning algorithm based on multi-agent reinforcement learning entails adoption centralized training–decentralized execution architecture coordinate all UAVs. Additionally, introduced hidden recurrent neural network utilize historical observation To solve multi-objective optimization problem, We designed joint reward function guide learn optimal policies under multiple constraints. The results demonstrate that by using our method, were able problem information low efficiency caused partial observations sparse rewards learning, realized kdiff cooperative unknown environment.

Language: Английский

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Smart Flood Detection with AI and Blockchain Integration in Saudi Arabia Using Drones

Sensors, Journal Year: 2023, Volume and Issue: 23(11), P. 5148 - 5148

Published: May 28, 2023

Global warming and climate change are responsible for many disasters. Floods pose a serious risk require immediate management strategies optimal response times. Technology can respond in place of humans emergencies by providing information. As one these emerging artificial intelligence (AI) technologies, drones controlled their amended systems unmanned aerial vehicles (UAVs). In this study, we propose secure method flood detection Saudi Arabia using Flood Detection Secure System (FDSS) based on deep active learning (DeepAL) classification model federated to minimize communication costs maximize global accuracy. We use blockchain-based partially homomorphic encryption (PHE) privacy protection stochastic gradient descent (SGD) share solutions. InterPlanetary File (IPFS) addresses issues with limited block storage posed high gradients information transmitted blockchains. addition enhancing security, FDSS prevent malicious users from compromising or altering data. Utilizing images IoT data, train local models that detect monitor floods. A technique is used encrypt each locally trained achieve ciphertext-level aggregation filtering, which ensures the be verified while maintaining privacy. The proposed enabled us estimate flooded areas track rapid changes dam water levels gauge threat. methodology straightforward, easily adaptable, offers recommendations Arabian decision-makers administrators address growing danger flooding. This study concludes discussion its challenges managing floods remote regions blockchain technology.

Language: Английский

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Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems

Future Internet, Journal Year: 2024, Volume and Issue: 16(8), P. 276 - 276

Published: Aug. 2, 2024

The integration of artificial intelligence things (AIoT) is transforming aviation health monitoring systems by combining extensive data collection with advanced analytical capabilities. This study proposes a framework that enhances predictive accuracy, operational efficiency, and safety while optimizing maintenance strategies reducing costs. Utilizing three-tiered cloud architecture, the AIoT system enables real-time acquisition from sensors embedded in aircraft systems, followed machine learning algorithms to analyze interpret for proactive decision-making. research examines evolution traditional AIoT-enhanced monitoring, presenting comprehensive architecture integrated satellite communication 6G technology. mathematical models quantifying benefits increased diagnostic depth through AIoT, covering aspects such as cost savings, improvements are introduced this paper. findings emphasize strategic importance investing technologies balance cost, safety, efficiency operations, marking paradigm shift management aviation.

Language: Английский

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