Pub Date : 2024-01-19DOI: 10.1142/s0219876224500051
S. Duan, Lin-Xin Zhang, Xu Han, Yu-Le Li, Fang Wang, G. R. Liu
Abstract: A safe and smooth operating path is a prerequisite for mobile robots to accomplish tasks. Although the existing path optimization methods improve the smoothness of the planned path by introducing Bézier curve to locally optimize the path with regard to turning points, most of these methods manually select the position of control points and subjectively analyze the feasibility of the optimized path. It is argued unfavourably that it exhibits strong subjectivity and cumbersome selection process. To this gap, an adaptive path-smoothening optimization method is proposed in this study, which combines neural network, genetic algorithm, and Bézier curve to effectively resolve the problems of strong subjectivity, cumbersome steps, and thus low efficiency in the selection process of control points. To start with, the data set corresponding to the position of the control point and the path offset are constructed. Based on the actual working conditions, the value space of control point position is derived. Latin hypercube sampling is used to sample the control point position of the second-order Bézier curve, which is input into the Bézier curve solution model to calculate the corresponding path offset. The data set corresponding to the position of control point and path offset are thus acquired. Based on the data set, the neural network algorithm is used to train it, and the prediction model of path offset is constructed. Subsequently, with reference to the prediction model of path offset, a performance evaluation function is formulated by comprehending multiple influential factors of mobile robot motion safety and path smoothness. The genetic algorithm is then introduced to detect the optimal control points in different environments. The proposed method is verified by experiments in different operating environments. The study results show that the currently proposed adaptive path-smoothening optimization method exhibits remarkably superior applicability and effectiveness compared to the currently prevailing methods. It demonstrates advantages of fast path planning, reduced path turning points, and desirable path smoothness. In addition, it can also ensure the safety of mobile robot along the planned path as availed by a pre-set criterion.
{"title":"Novel Adaptive Path-Smoothening Optimization Method For Mobile Robots","authors":"S. Duan, Lin-Xin Zhang, Xu Han, Yu-Le Li, Fang Wang, G. R. Liu","doi":"10.1142/s0219876224500051","DOIUrl":"https://doi.org/10.1142/s0219876224500051","url":null,"abstract":" Abstract: A safe and smooth operating path is a prerequisite for mobile robots to accomplish tasks. Although the existing path optimization methods improve the smoothness of the planned path by introducing Bézier curve to locally optimize the path with regard to turning points, most of these methods manually select the position of control points and subjectively analyze the feasibility of the optimized path. It is argued unfavourably that it exhibits strong subjectivity and cumbersome selection process. To this gap, an adaptive path-smoothening optimization method is proposed in this study, which combines neural network, genetic algorithm, and Bézier curve to effectively resolve the problems of strong subjectivity, cumbersome steps, and thus low efficiency in the selection process of control points. To start with, the data set corresponding to the position of the control point and the path offset are constructed. Based on the actual working conditions, the value space of control point position is derived. Latin hypercube sampling is used to sample the control point position of the second-order Bézier curve, which is input into the Bézier curve solution model to calculate the corresponding path offset. The data set corresponding to the position of control point and path offset are thus acquired. Based on the data set, the neural network algorithm is used to train it, and the prediction model of path offset is constructed. Subsequently, with reference to the prediction model of path offset, a performance evaluation function is formulated by comprehending multiple influential factors of mobile robot motion safety and path smoothness. The genetic algorithm is then introduced to detect the optimal control points in different environments. The proposed method is verified by experiments in different operating environments. The study results show that the currently proposed adaptive path-smoothening optimization method exhibits remarkably superior applicability and effectiveness compared to the currently prevailing methods. It demonstrates advantages of fast path planning, reduced path turning points, and desirable path smoothness. In addition, it can also ensure the safety of mobile robot along the planned path as availed by a pre-set criterion.","PeriodicalId":54968,"journal":{"name":"International Journal of Computational Methods","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139525334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-12DOI: 10.1142/s0219876224500038
Koki Tazoe, Tomonori Yamada, G. Yagawa
{"title":"Accelerating SPH-fatigue computation by using single precision program on GPU","authors":"Koki Tazoe, Tomonori Yamada, G. Yagawa","doi":"10.1142/s0219876224500038","DOIUrl":"https://doi.org/10.1142/s0219876224500038","url":null,"abstract":"","PeriodicalId":54968,"journal":{"name":"International Journal of Computational Methods","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139623989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-05DOI: 10.1142/s0219876224500014
Zhoufeng Wang, Yao Cheng
{"title":"An adaptive PML finite volume algorithm for the scattering by periodic gratings","authors":"Zhoufeng Wang, Yao Cheng","doi":"10.1142/s0219876224500014","DOIUrl":"https://doi.org/10.1142/s0219876224500014","url":null,"abstract":"","PeriodicalId":54968,"journal":{"name":"International Journal of Computational Methods","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139449322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-24DOI: 10.1142/s0219876223500408
Mehmet Pakdemirli
{"title":"New numerical iteration schemes based on perturbation iteration algorithms","authors":"Mehmet Pakdemirli","doi":"10.1142/s0219876223500408","DOIUrl":"https://doi.org/10.1142/s0219876223500408","url":null,"abstract":"","PeriodicalId":54968,"journal":{"name":"International Journal of Computational Methods","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139238633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Identification of vivo Material Parameters of Arterial Wall based on Improved Niching Genetic Algorithm and Neural Networks","authors":"Luming Zhao, Jianbing Sang, Lifang Sun, Fengtao Li, Huaxin Xiang","doi":"10.1142/s0219876223500391","DOIUrl":"https://doi.org/10.1142/s0219876223500391","url":null,"abstract":"","PeriodicalId":54968,"journal":{"name":"International Journal of Computational Methods","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139263665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-10DOI: 10.1142/s0219876223500329
Samala Rathan, Deepit Shah, T. Hemanth Kumar, K. Sandeep Charan
In this paper, our objective is primarily to use adaptive inverse-quadratic (IQ) and inverse-multi-quadratic (IMQ) radial basis function (RBF) interpolation techniques to develop third and fourth-order methods such as Adams–Bashforth (AB) and Adams–Moulton (AM) methods. By utilizing a free parameter involved in the RBF, the local convergence of the numerical solution is enhanced by making the local truncation error vanish. Consistency and stability analysis is presented along with some numerical results to back up our assertions. The accuracy and rate of convergence of each proposed technique are equal to or better than the original AB and AM methods by eliminating the local truncation error thus in that sense, the proposed adaptive methods are optimal. We conclude that both IQ and IMQ-RBF methods yield an improved order of convergence than classical methods, while the superiority of one method depends on the method and the problem considered.
本文的主要目的是利用自适应反二次（IQ）和反多二次（IMQ）径向基函数（RBF）插值技术来开发三阶和四阶方法，如亚当斯-巴什福斯（AB）和亚当斯-穆尔顿（AM）方法。通过利用 RBF 中的自由参数，使局部截断误差消失，从而增强了数值解的局部收敛性。我们提出了一致性和稳定性分析以及一些数值结果来支持我们的论断。通过消除局部截断误差，所提出的每种技术的精度和收敛速度都等于或优于原始的 AB 和 AM 方法，因此从这个意义上说，所提出的自适应方法是最优的。我们的结论是，与传统方法相比，IQ 和 IMQ-RBF 方法都能产生更好的收敛阶次，而一种方法的优劣取决于所考虑的方法和问题。
{"title":"Adaptive IQ and IMQ-RBFs for Solving Initial Value Problems: Adams–Bashforth and Adams–Moulton Methods","authors":"Samala Rathan, Deepit Shah, T. Hemanth Kumar, K. Sandeep Charan","doi":"10.1142/s0219876223500329","DOIUrl":"https://doi.org/10.1142/s0219876223500329","url":null,"abstract":"<p>In this paper, our objective is primarily to use adaptive inverse-quadratic (IQ) and inverse-multi-quadratic (IMQ) radial basis function (RBF) interpolation techniques to develop third and fourth-order methods such as Adams–Bashforth (AB) and Adams–Moulton (AM) methods. By utilizing a free parameter involved in the RBF, the local convergence of the numerical solution is enhanced by making the local truncation error vanish. Consistency and stability analysis is presented along with some numerical results to back up our assertions. The accuracy and rate of convergence of each proposed technique are equal to or better than the original AB and AM methods by eliminating the local truncation error thus in that sense, the proposed adaptive methods are optimal. We conclude that both IQ and IMQ-RBF methods yield an improved order of convergence than classical methods, while the superiority of one method depends on the method and the problem considered.</p>","PeriodicalId":54968,"journal":{"name":"International Journal of Computational Methods","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140045445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-10DOI: 10.1142/s0219876223420021
Jian Chen, Dominik Krengel, Hans-Georg Matuttis
{"title":"Toward Development of a Plate Discrete Element Method: Geometry and Kinematics","authors":"Jian Chen, Dominik Krengel, Hans-Georg Matuttis","doi":"10.1142/s0219876223420021","DOIUrl":"https://doi.org/10.1142/s0219876223420021","url":null,"abstract":"","PeriodicalId":54968,"journal":{"name":"International Journal of Computational Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135186141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-07DOI: 10.1142/s021987622342001x
Y. Jane Liu, John Peddieson, Stephen Idem
{"title":"Geometrically Non-Linear Analysis of Beam-Reinforced Thin Plates Using the Methodology of Groebner Bases","authors":"Y. Jane Liu, John Peddieson, Stephen Idem","doi":"10.1142/s021987622342001x","DOIUrl":"https://doi.org/10.1142/s021987622342001x","url":null,"abstract":"","PeriodicalId":54968,"journal":{"name":"International Journal of Computational Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135541074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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