{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T15:27:41Z","timestamp":1774884461760,"version":"3.50.1"},"reference-count":11,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,2,2]]},"abstract":"<jats:p>With the development of science and technology, the intelligent robot has become an important tool in our production and life. It not only improves people\u2019s living standards but also promotes economic development. At present, the related technology in the field of the intelligent robot has been developed rapidly, but at the same time, many technical problems have been exposed. The single path planning problem can be well solved, but the dynamic path planning of a robot is one of the current technical difficulties. At present, the genetic algorithm is the mainstream scheme, but its control accuracy is still lacking in practical application. To solve this problem, this paper proposes a dynamic path planning scheme for intelligent robots based on a fuzzy neural network. The research of this paper is mainly divided into four parts. The first part is to analyze the current situation of technology research in this field and put forward the idea of this paper by analyzing the shortcomings of existing technologies. The second part is the research of related basic theory, which deeply studies the core theory of intelligent robot and dynamic path planning, which provides a theoretical basis for the later model implementation. The third part is the design and implementation of dynamic path planning based on a fuzzy neural network. This paper gives the design principle and specific improvement method in detail. At the end of the paper, that is, the fourth part, through comparative analysis experiments, further proves the superiority of the fuzzy neural network algorithm. Compared with the traditional particle swarm optimization algorithm, it can significantly improve the control accuracy and robustness of the model.<\/jats:p>","DOI":"10.3233\/jifs-189344","type":"journal-article","created":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T15:23:27Z","timestamp":1601393007000},"page":"3055-3063","source":"Crossref","is-referenced-by-count":37,"title":["Dynamic robot path planning system using neural network"],"prefix":"10.1177","volume":"40","author":[{"given":"Gang","family":"Wang","sequence":"first","affiliation":[{"name":"Taizhou Institute of Science and Technology, Nanjing University of Science and Technology, Taizhou, Jiangsu, China"}]},{"given":"Jun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Taizhou Institute of Science and Technology, Nanjing University of Science and Technology, Taizhou, Jiangsu, China"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/JIFS-189344_ref3","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.1109\/JSYST.2018.2867285","article-title":"A Formal Model-Based Design Method for Robotic Systems, in","volume":"13","author":"Wang","year":"2019","journal-title":"IEEE Systems Journal"},{"issue":"10","key":"10.3233\/JIFS-189344_ref4","first-page":"1","article-title":"Optimal design of fractional-order PID controller for five-bar linkage robot using a new particle swarm optimization algorithm","volume":"20","author":"Aghababa","year":"2015","journal-title":"Soft Computing"},{"issue":"15","key":"10.3233\/JIFS-189344_ref5","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1002\/1099-1239(20001230)10:15<1249::AID-RNC514>3.0.CO;2-7","article-title":"Pid control design and \u221e loop shaping","volume":"10","author":"Panagopoulos","year":"2015","journal-title":"International Journal of Robust & Nonlinear Control"},{"issue":"3","key":"10.3233\/JIFS-189344_ref6","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1049\/iet-gtd.2014.0264","article-title":"Post-disturbance transient stability assessment of power systems by a self-adaptive intelligent system","volume":"9","author":"Zhang","year":"2015","journal-title":"Generation Transmission & Distribution Iet"},{"key":"10.3233\/JIFS-189344_ref7","doi-asserted-by":"crossref","unstructured":"Zhou S. and Tan B. , Electrocardiogram soft computing using hybrid deep learning CNN-ELM, Appl Soft Comput 86 (2020).","DOI":"10.1016\/j.asoc.2019.105778"},{"issue":"4","key":"10.3233\/JIFS-189344_ref8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40815-016-0166-0","article-title":"Hybrid intelligent algorithm for indoor path planning and trajectory-tracking control of the wheeled mobile robot","volume":"18","author":"Li","year":"2016","journal-title":"International Journal of Fuzzy Systems"},{"issue":"3","key":"10.3233\/JIFS-189344_ref9","doi-asserted-by":"crossref","first-page":"2321","DOI":"10.1007\/s13369-018-3497-3","article-title":"A novel and reduced CPU time modeling and simulation methodology for path planning based on resistive grids","volume":"44","author":"Hernandez-Mejia","year":"2019","journal-title":"Arabian Journal for ence and Engineering"},{"key":"10.3233\/JIFS-189344_ref10","doi-asserted-by":"crossref","unstructured":"Jain S. and Sinha A. , Social network sustainability for transport planning with complex interconnections, Sustain Comput Informatics Syst 24 (2019).","DOI":"10.1016\/j.suscom.2019.100351"},{"issue":"2","key":"10.3233\/JIFS-189344_ref11","doi-asserted-by":"crossref","first-page":"1319","DOI":"10.1109\/TVT.2019.2958197","article-title":"A novel hybrid neural network-based multi-robot path planning with motion coordination","volume":"69","author":"Pradhan","year":"2020","journal-title":"IEEE Transactions on Vehicular Technology"},{"issue":"001","key":"10.3233\/JIFS-189344_ref12","first-page":"32","article-title":"Application of intelligent monitoring system for integrated multi-physiological parameters and environment information for cape sentry based on domestic CPU","volume":"029","author":"Naijun","journal-title":"Armed Police Medicine"},{"issue":"2","key":"10.3233\/JIFS-189344_ref13","first-page":"585","article-title":"Evaluation of intelligent information system based on user cognitive features (case study: e-learning environment)","volume":"34","author":"Fatahi","year":"2019","journal-title":"Iranian Journal of Information Processing Management"}],"container-title":["Journal of Intelligent &amp; 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