{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T22:56:53Z","timestamp":1781996213079,"version":"3.54.5"},"reference-count":28,"publisher":"National Library of Serbia","issue":"2","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2024]]},"abstract":"<jats:p>In this paper, a real-time navigation control system based on lidar sensing is proposed for use in unknown environments. The proposed system comprises a behavioral controller for controlling an autonomous Ackerman robot for obstacle avoidance in the absence of global map information when moving toward a goal. The adopted obstacle avoidance method is selected by a wall-following fuzzy controller. The input parameter of this controller is the distance between the robot and the wall, which is determined by the lidar sensor, and the output parameter of the controller is the steering angle of the robot for it to reach the destination without collision. To prevent the robot from entering an endless loop, an endless loop escape mechanism is added to the proposed system. The simulation and experimental results of this study indicate that the proposed navigation control system can effectively assist an Ackerman robot to complete the navigation task successfully in unknown environments.<\/jats:p>","DOI":"10.2298\/csis220826008l","type":"journal-article","created":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T11:12:28Z","timestamp":1714475548000},"page":"473-490","source":"Crossref","is-referenced-by-count":1,"title":["Navigation control of an autonomous Ackerman robot in unknown environments by using a lidar-sensing-based fuzzy controller"],"prefix":"10.2298","volume":"21","author":[{"given":"Cheng-Jian","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jyun-Yu","family":"Jhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen-Chia","family":"Chuang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1078","reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Abdelazim, T., Malik, O.: An adaptive power system stabilizer using on-line self-learning fuzzy systems. 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