{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:23:20Z","timestamp":1778603000409,"version":"3.51.4"},"reference-count":31,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,16]],"date-time":"2024-06-16T00:00:00Z","timestamp":1718496000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangxi Key Research and Development Plan Project","award":["No. AB24010274"],"award-info":[{"award-number":["No. AB24010274"]}]},{"name":"Guangxi Key Research and Development Plan Project","award":["No. AD24010061"],"award-info":[{"award-number":["No. AD24010061"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Sensors"],"abstract":"<jats:p>In the domain of mobile robot navigation, conventional path-planning algorithms typically rely on predefined rules and prior map information, which exhibit significant limitations when confronting unknown, intricate environments. With the rapid evolution of artificial intelligence technology, deep reinforcement learning (DRL) algorithms have demonstrated considerable effectiveness across various application scenarios. In this investigation, we introduce a self-exploration and navigation approach based on a deep reinforcement learning framework, aimed at resolving the navigation challenges of mobile robots in unfamiliar environments. Firstly, we fuse data from the robot\u2019s onboard lidar sensors and camera and integrate odometer readings with target coordinates to establish the instantaneous state of the decision environment. Subsequently, a deep neural network processes these composite inputs to generate motion control strategies, which are then integrated into the local planning component of the robot\u2019s navigation stack. Finally, we employ an innovative heuristic function capable of synthesizing map information and global objectives to select the optimal local navigation points, thereby guiding the robot progressively toward its global target point. In practical experiments, our methodology demonstrates superior performance compared to similar navigation methods in complex, unknown environments devoid of predefined map information.<\/jats:p>","DOI":"10.3390\/s24123895","type":"journal-article","created":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T06:29:43Z","timestamp":1718605783000},"page":"3895","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Autonomous Navigation by Mobile Robot with Sensor Fusion Based on Deep Reinforcement Learning"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0838-0389","authenticated-orcid":false,"given":"Yang","family":"Ou","sequence":"first","affiliation":[{"name":"School of Computer and Electronic Information, Guangxi University, Nanning 530004, China"},{"name":"The Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning 530004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4237-9330","authenticated-orcid":false,"given":"Yiyi","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Computer and Electronic Information, Guangxi University, Nanning 530004, China"},{"name":"The Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning 530004, China"},{"name":"School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youming","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer and Electronic Information, Guangxi University, Nanning 530004, China"},{"name":"The Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning 530004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tuanfa","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Computer and Electronic Information, Guangxi University, Nanning 530004, China"},{"name":"The Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning 530004, China"},{"name":"School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Singandhupe, A., and La, H.M. 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