{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:19:30Z","timestamp":1760059170283,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:00:00Z","timestamp":1748304000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sichuan Provincial Science and Technology Plan Project","award":["2022NZZJ0036"],"award-info":[{"award-number":["2022NZZJ0036"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>To address the intelligent path planning challenges faced by mobile robots operating in complex road environments, this paper introduces the Hybrid Symmetric Bio-inspired Neural Network Algorithm (HSBNN). This algorithm integrates the improved bio-inspired neural network (BINN) with an improved genetic algorithm (IGA) and develops new models for environmental representation and path decision making, thereby significantly enhancing global optimization capabilities. The experimental results indicate that HSBNN outperforms traditional genetic algorithms, adaptive genetic algorithms, and ant colony algorithms in several key metrics, including global search performance, path length, and the number of turns, achieving reductions of up to 10 turns and 11.358% in path distance. Furthermore, HSBNN exhibits superior adaptability to varying environmental complexities and demonstrates enhanced operational efficiency.<\/jats:p>","DOI":"10.3390\/sym17060836","type":"journal-article","created":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T04:46:38Z","timestamp":1748493998000},"page":"836","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on Intelligent Path Planning of Mobile Robot Based on Hybrid Symmetric Bio-Inspired Neural Network Algorithm in Complex Road Environments"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8758-3672","authenticated-orcid":false,"given":"Siyu","family":"Chen","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Xihua University, Chengdu 610039, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6855-0060","authenticated-orcid":false,"given":"Tingping","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Xihua University, Chengdu 610039, China"},{"name":"School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6157-7159","authenticated-orcid":false,"given":"Junmin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Xihua University, Chengdu 610039, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6888-7993","authenticated-orcid":false,"given":"Simon X.","family":"Yang","sequence":"additional","affiliation":[{"name":"Advanced Robotics and Intelligent Systems Laboratory, School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Song, Y., Tian, W., Tian, Y., and Liu, X. 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