{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T16:07:40Z","timestamp":1758816460583,"version":"3.41.2"},"reference-count":29,"publisher":"Cambridge University Press (CUP)","issue":"5","license":[{"start":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T00:00:00Z","timestamp":1747353600000},"content-version":"unspecified","delay-in-days":15,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotica"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The selection of random sampling points is crucial for the path quality generated by probabilistic roadmap (PRM) algorithm. Increasing the number of sampling points can enhance path quality. However, it may also lead to extended convergence time and reduced computational efficiency. Therefore, an improved probabilistic roadmap algorithm (TL-PRM) is proposed based on topological discrimination and lazy collision. TL-PRM algorithm first generates a circular grid area among start and goal points. Then, it constructs topological nodes. Subsequently, elliptical sampling areas are created between each pair of adjacent topological nodes. Random sampling points are generated within these areas. These sampling points are interconnected using a layer connection strategy. An initial path is generated using a delayed collision strategy. The path is then adjusted by modifying the nodes on the convex outer edges to avoid obstacles. Finally, a reconnection strategy is employed to optimize the path. This reduces the number of path waypoints. In dynamic environments, TL-PRM algorithm employs pose adjustment strategies for semi-static and dynamic obstacles. It can use either the same or opposite pose adjustments to avoid dynamic obstacles. Experimental results indicate that TL-PRM algorithm reduces the average number of generated sampling points by 70.9% and average computation time by 62.1% compared with PRM* and PRM-Astar algorithms. In winding and narrow passage maps, TL-PRM algorithm significantly decreases the number of sampling points and shortens convergence time. In dynamic environments, the algorithm can adjust its pose orientation in real time. This allows it to safely reach the goal point. TL-PRM algorithm provides an effective solution for reducing the generation of sampling points in PRM algorithm.<\/jats:p>","DOI":"10.1017\/s0263574725000529","type":"journal-article","created":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T07:48:57Z","timestamp":1747381737000},"page":"1885-1909","source":"Crossref","is-referenced-by-count":1,"title":["Topological discrimination and lazy collision-Based multistage optimization of probabilistic roadmap algorithm for path planning"],"prefix":"10.1017","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4391-6401","authenticated-orcid":false,"given":"Wenbin","family":"Gong","sequence":"first","affiliation":[]},{"given":"Hua","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Su","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Gu","sequence":"additional","affiliation":[]},{"given":"HongHao","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yutao","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Tangju","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Hongbing","family":"Li","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2025,5,16]]},"reference":[{"key":"S0263574725000529_ref19","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.3390\/mi14061181","article-title":"Microrobot path planning based on the multi-module DWA method in crossing dense obstacle scenario","volume":"14","author":"Dequan","year":"2023","journal-title":"Micromachines-BASEL"},{"key":"S0263574725000529_ref22","first-page":"1","article-title":"Path Planning Algorithm improvement based on PRM technology","volume":"1","author":"Zou","year":"2019","journal-title":"Combined Machine Tools Autom. 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