{"title":"Adaptive ANN-BFO Hybrid Method for Solving the Forward Kinematics Problem of a Hexa Parallel Robot","authors":"Ba-Phuc Huynh","doi":"10.1007/s12555-024-0296-3","DOIUrl":null,"url":null,"abstract":"<p>This paper introduces an adaptive hybrid approach to address the forward kinematics problem of a Hexa parallel robot (HPR), known for its challenge in obtaining a unique closed-form analytic solution. In the initial stage, we construct an artificial neural network (ANN) model to rapidly generate a preliminary result, effectively narrowing the search space. Subsequently, bacterial foraging optimization (BFO) is adapted to refine the result by focusing on exploration within the reduced search space. Adaptive functions adjust BFO parameters based on the error level in the preliminary result, enhancing algorithm performance. Software is developed to demonstrate the practical application of this method. Experimental results within the robot workspace indicate a significant reduction in calculation errors compared to using only the ANN model.</p>","PeriodicalId":54965,"journal":{"name":"International Journal of Control Automation and Systems","volume":"668 1","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Control Automation and Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s12555-024-0296-3","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
This paper introduces an adaptive hybrid approach to address the forward kinematics problem of a Hexa parallel robot (HPR), known for its challenge in obtaining a unique closed-form analytic solution. In the initial stage, we construct an artificial neural network (ANN) model to rapidly generate a preliminary result, effectively narrowing the search space. Subsequently, bacterial foraging optimization (BFO) is adapted to refine the result by focusing on exploration within the reduced search space. Adaptive functions adjust BFO parameters based on the error level in the preliminary result, enhancing algorithm performance. Software is developed to demonstrate the practical application of this method. Experimental results within the robot workspace indicate a significant reduction in calculation errors compared to using only the ANN model.
本文介绍了一种自适应混合方法，用于解决六轴并联机器人（HPR）的正向运动学问题。在初始阶段，我们构建了一个人工神经网络（ANN）模型，以快速生成初步结果，从而有效缩小搜索空间。随后，对细菌觅食优化（BFO）进行调整，通过在缩小的搜索空间内集中探索来完善结果。自适应功能根据初步结果的误差水平调整 BFO 参数，从而提高算法性能。我们开发了软件来演示这种方法的实际应用。机器人工作区内的实验结果表明，与仅使用 ANN 模型相比，计算误差显著减少。
期刊介绍：
International Journal of Control, Automation and Systems is a joint publication of the Institute of Control, Robotics and Systems (ICROS) and the Korean Institute of Electrical Engineers (KIEE).
The journal covers three closly-related research areas including control, automation, and systems.
The technical areas include
Control Theory
Control Applications
Robotics and Automation
Intelligent and Information Systems
The Journal addresses research areas focused on control, automation, and systems in electrical, mechanical, aerospace, chemical, and industrial engineering in order to create a strong synergy effect throughout the interdisciplinary research areas.
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