{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T16:04:58Z","timestamp":1779725098818,"version":"3.53.1"},"reference-count":35,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T00:00:00Z","timestamp":1778457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"<jats:p>Path planning in cluttered environments constitutes a critical challenge for mobile robotics. Although optimal solutions can be obtained by classical methods such as A*, they have the disadvantage of being computationally expensive in complex environments. In this paper, we propose a novel deep learning-based framework for 2D trajectory prediction in grid environments. The framework employs attention mechanisms specifically designed for path planning tasks. In particular, we design an Attention U-Net architecture that employs attention gates for effective path area focusing and residual connections for efficient feature selection. To validate our method, the Attention U-Net architecture is trained on 5000 randomly sampled 40 \u00d7 40 environments and tested on a separate test set of 200 environments. The experimental results show that the Attention U-Net architecture significantly outperforms the A* algorithm. It expands 62% fewer nodes (207.6 vs. 543.11) and achieves near-optimal path lengths (99.8% of optimal) and planning speed (0.78 ms vs. 1.19 ms). Furthermore, the Attention U-Net architecture achieves a 100% success rate for A* path planning with the attention heuristic, demonstrating the effectiveness of the attention heuristic for path planning.<\/jats:p>","DOI":"10.3390\/robotics15050096","type":"journal-article","created":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T15:05:08Z","timestamp":1779721508000},"page":"96","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Attention-Guided Path Planning: Learning Efficient Heuristics for Mobile Robot Navigation via Deep Neural Networks"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2672-3587","authenticated-orcid":false,"given":"Abderrahim","family":"Waga","sequence":"first","affiliation":[{"name":"School of Digital Engineering and Artificial Intelligence, Euromed University, Fez 30000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Said","family":"Benhlima","sequence":"additional","affiliation":[{"name":"Computer Science, Faculty of Sciences, Moulay Ismail University, Zitoun, Meknes 50000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5440-8913","authenticated-orcid":false,"given":"Ali","family":"Bekri","sequence":"additional","affiliation":[{"name":"Computer Science, Faculty of Sciences, Moulay Ismail University, Zitoun, Meknes 50000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3095-2578","authenticated-orcid":false,"given":"Fatima Zahrae","family":"Saber","sequence":"additional","affiliation":[{"name":"Computer Science, Faculty of Sciences, Ibn Tofail University, Kenitra 14000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jawad","family":"Abdouni","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National School of Applied Sciences of Kenitra, Ibn Tofail University, Kenitra 14000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5733-3119","authenticated-orcid":false,"given":"Toufik","family":"Mzili","sequence":"additional","affiliation":[{"name":"Computer Science, Faculty of Sciences, Chouaib Doukkali University, Eljadida 24000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4906-2571","authenticated-orcid":false,"given":"Ahmed","family":"Regragui","sequence":"additional","affiliation":[{"name":"Computer Science, Faculty of Sciences, Moulay Ismail University, Zitoun, Meknes 50000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tang, Y., Zakaria, M.A., and Younas, M. 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