Convolutional Neural Network-Based Robot Path Planning and Equipment Obstacle Intelligent Recognition System

Authors

  • Wusong Sun School of Automotive and Mechanical & Electrical Engineering, Lu’an Vocational Technical College, Lu’an 237158, Anhui, China
  • Hua Lin School of Mechanical and Automation Engineering, West AnHui University, Lu’an 237012, Anhui, China

Abstract

With the development of global economic integration, the number of environmental issues brought about by rapid developments cannot be underestimated. Global climate change is becoming increasingly severe, and governments at all levels around the world are using various means to reduce their own carbon emissions. In a business environment characterized by increasingly fierce competition, the discrepancies and problems existing in traditional logistics enterprises are becoming more acute. At present, information technology is changing rapidly and, consequently, various fields are developing rapidly. Market and customer expectations are also changing, imposing enormous pressure on transportation logistics. Therefore, in the field of logistics, the advantages of a lowcarbon supply chain (LCSC) energy-saving and emission reduction systembased on autonomous decisionmaking (ADM) intelligence for logistics enterprises stand out among traditional logistics systems. ADM can improve the operational efficiency of the logistics supply chain (LSC) while reducing the impact of logistics-related industries on the environment. However, at present, the research on the energy-saving and emission reduction systems of logistics enterprises is not adequate, making it essential to study these aspects of the logistics industry. Researchers are attempting to improve traditional LSC by upgrading their systems to address issues such as poor efficiency and high emissions. This
current study found that ADM can optimize the energy conservation and emission reduction system of LCSCs, and can effectively reduce the impact of the logistics industry on the environment. This study investigated this method of optimization using Petri net decision algorithm and examples. The results of statistical analysis showed that the impact of traditional LSC on the environment increased by 0.5% between 2015 and 2020, indicating an urgent need to reform traditional LSCs. This study investigated the advantages of ADM intelligence that can help to improve the energy conservation and emission reduction systems of LCSCs. The results indicate that with the utilization of ADM intelligence, the energy-saving and emission reduction system is more efficient and reduces emissions the emissions of each link in the supply chain.

Keywords: autonomous decision-making, low carbon, supply chain, reduce emissions

Cite As

W. Sun, H. Lin, "Convolutional Neural Network-Based Robot Path Planning and Equipment Obstacle
Intelligent Recognition System", Engineering Intelligent Systems, vol. 32 no. 3, pp. 213-223, 2024.





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Published

2024-05-01

Issue

Vol. 32 No. 3 (2024): International Journal of Engineering Intelligent Systems

Section

Article

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