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Research on Industrial Robot Path Planning Optimization Algorithm Based on IoT and Deep Reinforcement Learning.

Bibliographic Details
Title: Research on Industrial Robot Path Planning Optimization Algorithm Based on IoT and Deep Reinforcement Learning.
Authors: Du, Honglin1 (AUTHOR) honglindu@outlook.com
Source: Journal of Advanced Manufacturing Systems. Jul2026, p1-21. 21p.
Subject Terms: *Industrial robots, *Industry 4.0, Robotic path planning, Optimization algorithms, Reinforcement learning, Obstacle avoidance (Robotics), Internet of things, Time-varying systems
Abstract: With the rapid development of Industry 4.0, industrial robots are increasingly widely used in intelligent manufacturing. Path planning is one of its core tasks, directly affecting production efficiency and safety. Traditional path planning methods are often complex to consider in real-time and optimally when facing a complex dynamic environment. For this purpose, this study introduces an optimization algorithm for industrial robot path planning, using the Internet of Things (IoT) and Deep Reinforcement Learning (DRL) technologies. The algorithm collects environmental data in real-time through IoT technology and combines DRL models to make dynamic decisions to achieve efficient and adaptive path planning. The simulation outcomes show that in the simulated industrial environment, compared with the conventional A* algorithm and ant colony algorithm, this algorithm has an average 35% reduction in path planning time, a 22% improvement in path length optimization, and a success rate over 95% for obstacle avoidance in dynamic obstacle situations. The algorithm can rapidly adapt to environmental changes via real-time data feedback from IoT devices, and the volatility of the planning time is reduced by 40%. This research offers a novel approach for industrial robot path planning in intricate settings, with significant practical worth and potential for widespread adoption. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Advanced Manufacturing Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Business Source Index
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Header DbId: bsx
DbLabel: Business Source Index
An: 195577292
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PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1411.61413574219
IllustrationInfo
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  Label: Title
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  Data: Research on Industrial Robot Path Planning Optimization Algorithm Based on IoT and Deep Reinforcement Learning.
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  Data: <searchLink fieldCode="AR" term="%22Du%2C+Honglin%22">Du, Honglin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> honglindu@outlook.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Advanced+Manufacturing+Systems%22">Journal of Advanced Manufacturing Systems</searchLink>. Jul2026, p1-21. 21p.
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  Data: *<searchLink fieldCode="DE" term="%22Industrial+robots%22">Industrial robots</searchLink><br />*<searchLink fieldCode="DE" term="%22Industry+4%2E0%22">Industry 4.0</searchLink><br /><searchLink fieldCode="DE" term="%22Robotic+path+planning%22">Robotic path planning</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Obstacle+avoidance+%28Robotics%29%22">Obstacle avoidance (Robotics)</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+of+things%22">Internet of things</searchLink><br /><searchLink fieldCode="DE" term="%22Time-varying+systems%22">Time-varying systems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: With the rapid development of Industry 4.0, industrial robots are increasingly widely used in intelligent manufacturing. Path planning is one of its core tasks, directly affecting production efficiency and safety. Traditional path planning methods are often complex to consider in real-time and optimally when facing a complex dynamic environment. For this purpose, this study introduces an optimization algorithm for industrial robot path planning, using the Internet of Things (IoT) and Deep Reinforcement Learning (DRL) technologies. The algorithm collects environmental data in real-time through IoT technology and combines DRL models to make dynamic decisions to achieve efficient and adaptive path planning. The simulation outcomes show that in the simulated industrial environment, compared with the conventional A* algorithm and ant colony algorithm, this algorithm has an average 35% reduction in path planning time, a 22% improvement in path length optimization, and a success rate over 95% for obstacle avoidance in dynamic obstacle situations. The algorithm can rapidly adapt to environmental changes via real-time data feedback from IoT devices, and the volatility of the planning time is reduced by 40%. This research offers a novel approach for industrial robot path planning in intricate settings, with significant practical worth and potential for widespread adoption. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Advanced Manufacturing Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1142/s0219686728500175
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 21
        StartPage: 1
    Subjects:
      – SubjectFull: Industrial robots
        Type: general
      – SubjectFull: Industry 4.0
        Type: general
      – SubjectFull: Robotic path planning
        Type: general
      – SubjectFull: Optimization algorithms
        Type: general
      – SubjectFull: Reinforcement learning
        Type: general
      – SubjectFull: Obstacle avoidance (Robotics)
        Type: general
      – SubjectFull: Internet of things
        Type: general
      – SubjectFull: Time-varying systems
        Type: general
    Titles:
      – TitleFull: Research on Industrial Robot Path Planning Optimization Algorithm Based on IoT and Deep Reinforcement Learning.
        Type: main
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          Name:
            NameFull: Du, Honglin
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          Dates:
            – D: 24
              M: 07
              Text: Jul2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 02196867
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            – TitleFull: Journal of Advanced Manufacturing Systems
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