{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T17:26:00Z","timestamp":1783445160425,"version":"3.54.6"},"reference-count":20,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Aiming at the problem that path planning in dynamic obstacle environments is difficult to balance global optimality and local real-time performance, a collision avoidance path planning method is proposed by integrating improved bidirectional fast expanding random tree (Bi RRT) and dynamic window method (DWA). This method is divided into two stages: dynamic obstacle space reconstruction and algorithm fusion. Firstly, in the spatial reconstruction stage, static and dynamic obstacle information is collected to construct obstacle reconstruction reference points, and configuration functions are used to reconstruct the obstacle information points, achieving accurate modeling of dynamic obstacle environments. Secondly, in the algorithm fusion stage, the improved Bi RRT algorithm introduces greedy expansion and bidirectional adaptive strategies, combined with the cost evaluation mechanism of the A* algorithm, to improve the efficiency and goal orientation of global path search; Improve the DWA algorithm to optimize the evaluation function, set up a threat space to screen obstacles and enhance target navigation capabilities, and improve the real-time and accuracy of local obstacle avoidance. The fusion of the two forms a path planning mechanism that combines global path guidance and local dynamic obstacle avoidance for collaborative optimization. The experimental results show that this method can effectively avoid local optima and collision problems in complex dynamic environments, and the reconstruction position and width are dynamically adjusted with the movement of obstacles. The shortest running time can reach 0.247\u202fs, significantly improving real-time performance and adaptability of path planning.<\/jats:p>","DOI":"10.1515\/comp-2025-0057","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T16:57:08Z","timestamp":1783443428000},"source":"Crossref","is-referenced-by-count":0,"title":["Fusion of\u00a0DWA algorithm and\u00a0improved Bi RRT obstacle avoidance path planning method in\u00a0dynamic obstacle environments"],"prefix":"10.1515","volume":"16","author":[{"given":"Yange","family":"Li","sequence":"first","affiliation":[{"name":"School of Data Science, Hebi Polytechnic , Hebi 458030 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2026,7,7]]},"reference":[{"key":"2026070716564085274_j_comp-2025-0057_ref_001","doi-asserted-by":"crossref","unstructured":"A. L. Jutinico, G. A. R. Rodriguez, and J. R. 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