{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T11:28:36Z","timestamp":1770290916645,"version":"3.49.0"},"reference-count":34,"publisher":"Emerald","issue":"6","license":[{"start":{"date-parts":[[2019,10,16]],"date-time":"2019-10-16T00:00:00Z","timestamp":1571184000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IR"],"published-print":{"date-parts":[[2019,10,16]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>This paper aims to propose a novel active SLAM framework to realize avoid obstacles and finish the autonomous navigation in indoor environment.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>The improved fuzzy optimized Q-Learning (FOQL) algorithm is used to solve the avoidance obstacles problem of the robot in the environment. To reduce the motion deviation of the robot, fractional controller is designed. The localization of the robot is based on FastSLAM algorithm.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>Simulation results of avoiding obstacles using traditional Q-learning algorithm, optimized Q-learning algorithm and FOQL algorithm are compared. The simulation results show that the improved FOQL algorithm has a faster learning speed than other two algorithms. To verify the simulation result, the FOQL algorithm is implemented on a NAO robot and the experimental results demonstrate that the improved fuzzy optimized Q-Learning obstacle avoidance algorithm is feasible and effective.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>The improved fuzzy optimized Q-Learning (FOQL) algorithm is used to solve the avoidance obstacles problem of the robot in the environment. To reduce the motion deviation of the robot, fractional controller is designed. To verify the simulation result, the FOQL algorithm is implemented on a NAO robot and the experimental results demonstrate that the improved fuzzy optimized Q-Learning obstacle avoidance algorithm is feasible and effective.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ir-01-2019-0002","type":"journal-article","created":{"date-parts":[[2019,10,18]],"date-time":"2019-10-18T08:11:59Z","timestamp":1571386319000},"page":"801-811","source":"Crossref","is-referenced-by-count":7,"title":["NAO robot obstacle avoidance based on fuzzy Q-learning"],"prefix":"10.1108","volume":"47","author":[{"given":"Shuhuan","family":"Wen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueheng","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hak Keung","family":"Lam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuchun","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Fang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"issue":"3","key":"key2020100914205018800_ref001","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1109\/70.88137","article-title":"The vector field histogram-fast obstacle avoidance for mobile robots","volume":"7","year":"1991","journal-title":"IEEE Transactions on Robotics and Automation"},{"issue":"5","key":"key2020100914205018800_ref002","doi-asserted-by":"crossref","first-page":"2101","DOI":"10.1109\/TITS.2014.2308977","article-title":"Autonomous visual navigation and laser-based moving obstacle avoidance","volume":"15","year":"2014","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"key2020100914205018800_ref003","first-page":"1568","article-title":"Real-time dynamic fuzzy Q-learning and control of mobile robots","volume-title":"5th Asian Control Conference (IEEE Cat. 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