Fuzzy path planning of unmanned underwater vehicle based on adaptive fireworks algorithm optimization

Ma, Yiwei; Meng, Xiangyao; Mao, Zhaoyong; Yu, Sen-Nien; Qin, Junqi; Ding, Wenjun; Yang, Hongwei

doi:10.1109/icusai47366.2019.9124876

Cited by 2 publications

(2 citation statements)

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“…The number of inflection points on the path obtained by the combined method was minimized compared with that by the original A* algorithm and quick optimal path search was achieved with speed faster than that of the traditional A* algorithm. Ma F. et al [89] proposed a navigation path planning method for articulating underground LHD based on the improved A* algorithm, through introducing the collision treat cost into the evaluation function, in order to avoid the LHD from scraping the narrow roadway walls. According to the specific requirements of the path planning for unmanned underground LHD, Qi Yulong et al [90] proposed an improved A* algorithm modeled with extended nodes and introduced the collision threat cost into the evaluation function to avoid the scraper from collision onto the tunnel walls.…”

Section: Global Path Planningmentioning

confidence: 99%

Research Status and Development Trend of Underground Intelligent Load-Haul-Dump Vehicle—A Comprehensive Review

2022

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The underground intelligent load-haul-dump vehicle (LHD) is a product of the deep integration of traditional LHD with information network technology, automatic controlling and artificial intelligence technology. It gathers the functions of environmental perception, autonomous driving and fault diagnosis in one machine and exhibits higher safety and greater efficiency than traditional LHD. Hence, it is a particularly important piece of underground mining equipment for building green, safe and smart mines. Taking the studies about intelligent LHD collected by CNKI and WOS databases from 1980 to 2022 as a sample data source, employing Citespace visual analysis software for key feature extraction from the documents, statistical analysis was conducted to clarify the current research progress and the frontier topics of the intelligent LHD academia in the past 40 years, in relation to the future development trends. The development history and application status of underground intelligent LHD was expounded in this article, summarizing the research status at home and abroad from four aspects: ore heap perception and modeling technology, trajectory planning method of bucket shoveling, autonomous navigation technology, real-time monitoring and intelligent fault diagnosis technology. The demerits and merits of the technologies were reviewed as well, with future developing and researching trends of the underground intelligent LHD concluded.

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Section: Global Path Planningmentioning

confidence: 99%

Research Status and Development Trend of Underground Intelligent Load-Haul-Dump Vehicle—A Comprehensive Review

2022

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“…To effectively control identified hazards or lessen their effects, the PHA team must suggest additional safeguards. A passive or natural SMS of hazard control and the proposal of other SMSs of work and standard operating procedures are all case studies of safety precautions [11].…”

Section: Introductionmentioning

confidence: 99%

Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach

Nallathambi

Savaram²,

Sengan

et al. 2023

Sensors

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In the fireworks industry (FI), many accidents and explosions frequently happen due to human error (HE). Human factors (HFs) always play a dynamic role in the incidence of accidents in workplace environments. Preventing HE is a main challenge for safety and precautions in the FI. Clarifying the relationship between HFs can help in identifying the correlation between unsafe behaviors and influential factors in hazardous chemical warehouse accidents. This paper aims to investigate the impact of HFs that contribute to HE, which has caused FI disasters, explosions, and incidents in the past. This paper investigates why and how HEs contribute to the most severe accidents that occur while storing and using hazardous chemicals. The impact of fireworks and match industry disasters has motivated the planning of mitigation in this proposal. This analysis used machine learning (ML) and recommends an expert system (ES). There were many significant correlations between individual behaviors and the chance of HE to occur. This paper proposes an ML-based prediction model for fireworks and match work industries in Sivakasi, Tamil Nadu. For this study analysis, the questionnaire responses are reviewed for accuracy and coded from 500 participants from the fireworks and match industries in Tamil Nadu who were chosen to fill out a questionnaire. The Chief Inspectorate of Factories in Chennai and the Training Centre for Industrial Safety and Health in Sivakasi, Tamil Nadu, India, significantly contributed to the collection of accident datasets for the FI in Tamil Nadu, India. The data are analyzed and presented in the following categories based on this study’s objectives: the effect of physical, psychological, and organizational factors. The output implemented by comparing ML models, support vector machine (SVM), random forest (RF), and Naïve Bayes (NB) accuracy is 86.45%, 91.6%, and 92.1%, respectively. Extreme Gradient Boosting (XGBoost) has the optimal classification accuracy of 94.41% of ML models. This research aims to create a new ES to mitigate HE risks in the fireworks and match work industries. The proposed ES reduces HE risk and improves workplace safety in unsafe, uncertain workplaces. Proper safety management systems (SMS) can prevent deaths and injuries such as fires and explosions.

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Fuzzy path planning of unmanned underwater vehicle based on adaptive fireworks algorithm optimization

Cited by 2 publications

References 1 publication

Research Status and Development Trend of Underground Intelligent Load-Haul-Dump Vehicle—A Comprehensive Review

Research Status and Development Trend of Underground Intelligent Load-Haul-Dump Vehicle—A Comprehensive Review

Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach

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