{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T22:06:22Z","timestamp":1773871582216,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Interregional Innovation Projects Program 2023 of the Euroregion New Aquitaine\u2013Euskadi\u2013Navarra"},{"name":"Government of the Basque Country","award":["ELKARTEK KK-2025\/00012"],"award-info":[{"award-number":["ELKARTEK KK-2025\/00012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"<jats:p>Energy consumption optimisation has emerged as a critical need in Autonomous Mobile Robots (AMRs). Conventional A* implementations typically minimise path distance, neglecting energy-relevant factors such as directional changes and trajectory smoothness that significantly impact battery life and operational costs. This work proposes two energy-aware A* variants trained on empirical data from the KUKA KMP 1500 platform, where energy consumption is measured as battery SoC depletion: A*-RF, which integrates a Random Forest (RF) model directly into the cost function, and A*-MOD, which approximates the energy model through RF feature importance weights, achieving linear computational complexity O(nf). The RF model predicted energy consumption with an RMSE below 1.5% relative error, identifying travel distance and rotation angle as the dominant energy factors. Experimental validation across 42 path planning scenarios on a real industrial factory floor demonstrates that A*-MOD reduces energy consumption by up to 58.91% and improves operational autonomy by 2.21 times, with statistically significant improvements (p &lt; 0.01) across all evaluated metrics.<\/jats:p>","DOI":"10.3390\/robotics15030062","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T14:14:05Z","timestamp":1773843245000},"page":"62","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Energy-Efficient Path Planning for AMR Using Modified A* Algorithm with Machine Learning Integration"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3557-6831","authenticated-orcid":false,"given":"Mishell","family":"Cadena-Yanez","sequence":"first","affiliation":[{"name":"Navarra Artificial Intelligence Research Center\u2014NAIR Center, C. del Sadar, s\/n, 31006 Pamplona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7821-5661","authenticated-orcid":false,"given":"Danel","family":"Rico-Melgosa","sequence":"additional","affiliation":[{"name":"System Engineering and Automation Control Department, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV\/EHU), 01006 Vitoria-Gasteiz, Spain"}]},{"given":"Ekaitz","family":"Zulueta","sequence":"additional","affiliation":[{"name":"System Engineering and Automation Control Department, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV\/EHU), 01006 Vitoria-Gasteiz, Spain"}]},{"given":"Angela","family":"Bernardini","sequence":"additional","affiliation":[{"name":"Navarra Artificial Intelligence Research Center\u2014NAIR Center, C. del Sadar, s\/n, 31006 Pamplona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3418-5077","authenticated-orcid":false,"given":"Jorge","family":"Rodriguez-Guerra","sequence":"additional","affiliation":[{"name":"Research & Development Department, Aldakin, Nave 1G, 31800 Alsasua, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.ejor.2021.01.019","article-title":"Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda","volume":"294","author":"Fragapane","year":"2021","journal-title":"Eur. 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