{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T10:47:52Z","timestamp":1775558872145,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T00:00:00Z","timestamp":1775520000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102130"],"award-info":[{"award-number":["62102130"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["62102130"],"award-info":[{"award-number":["62102130"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Central Government Guides Local Science and Technology Development Fund Project","award":["226Z0201G"],"award-info":[{"award-number":["226Z0201G"]}]},{"name":"Science Research Project of Hebei Education Department","award":["QN2024138"],"award-info":[{"award-number":["QN2024138"]}]},{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["F2020204003"],"award-info":[{"award-number":["F2020204003"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["F2024204001"],"award-info":[{"award-number":["F2024204001"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["F2025204012"],"award-info":[{"award-number":["F2025204012"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Path planning is a key technology in robot navigation and has long attracted significant attention. However, in scenarios with high-density or unstructured obstacle distributions, path planning methods based on swarm intelligence optimization still face issues of low computational efficiency and poor path quality, limiting their performance in real-time applications. To address these challenges, this paper defines path key points and proposes a path planning method based on the Key-Points Encoding Genetic Algorithm (KEGA). First, an encoding scheme is designed to map key-point sequences into binary encodings, guiding the population to explore efficiently. Then, a new path generation module is integrated using target point direction, local environment, and historical path information to generate high-quality key-point sequences, thereby improving path quality. Additionally, by evaluating key-point sequences as a proxy for full path evaluation, only one precise path construction is required per iteration, significantly reducing computational overhead. Experiments were conducted on four simulated maps with diverse obstacle distribution characteristics and eight real-world street maps to validate the method\u2019s robustness and generalizability. The results show that, compared to the existing state-of-the-art robot path planning methods, the proposed method achieves an average runtime savings of 75.40%, a path length reduction of 35.65% and a path smoothness improvement of 68%.<\/jats:p>","DOI":"10.3390\/a19040285","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T09:40:31Z","timestamp":1775554831000},"page":"285","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Robot Path Planning Method Based on a Key Point Encoding Genetic Algorithm"],"prefix":"10.3390","volume":"19","author":[{"given":"Chuanyu","family":"Yang","sequence":"first","affiliation":[{"name":"Key Laboratory of Agricultural Big Data of Hebei Province, Hebei Agricultural University, Baoding 071001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7041-8582","authenticated-orcid":false,"given":"Zhenxue","family":"He","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural Big Data of Hebei Province, Hebei Agricultural University, Baoding 071001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojun","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural Big Data of Hebei Province, Hebei Agricultural University, Baoding 071001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yijin","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural Big Data of Hebei Province, Hebei Agricultural University, Baoding 071001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural Big Data of Hebei Province, Hebei Agricultural University, Baoding 071001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1145\/3570723","article-title":"Path-Planning for Unmanned Aerial Vehicles with Environment Complexity Considerations: A Survey","volume":"55","author":"Jones","year":"2023","journal-title":"ACM Comput. 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