{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T13:43:19Z","timestamp":1756993399518},"reference-count":23,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2007,3,13]],"date-time":"2007-03-13T00:00:00Z","timestamp":1173744000000},"content-version":"vor","delay-in-days":7407,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Robotic Syst."],"published-print":{"date-parts":[[1986,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Navigation planning is one of the most vital aspects of an autonomous mobile robot. Robot navigation for completely known terrain has been solved in many cases. Comparatively less research dealing with robot navigation in unexplored obstacle terrain has been reported in the literature. In recent times this problem has been addressed by adding learning capability to a robot. The robot explores terrain using sensors as it navigates, and builds a terrain model in an incremental manner. In this article we present concurrent algorithms for robot navigation in unexplored terrain. The performance of the concurrent algorithms is analyzed in terms of planning time, travel time, scanning time, and update time. The analysis reveals the need for an efficient data structure to store an obstacle terrain model in order to reduce traversal time, and also to incorporate learning. A modified adjacency list is proposed as a data structure for storing a spatial graph that represents an obstacle terrain. The time complexities of the algorithms that access, maintain, and update the spatial graph are estimated, and the effectiveness of the implementation is illustrated.<\/jats:p>","DOI":"10.1002\/rob.4620030404","type":"journal-article","created":{"date-parts":[[2007,7,6]],"date-time":"2007-07-06T04:21:00Z","timestamp":1183695660000},"page":"389-407","source":"Crossref","is-referenced-by-count":28,"title":["Robot navigation in an unexplored terrain"],"prefix":"10.1002","volume":"3","author":[{"given":"Nageswara S. V.","family":"Rao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S. S.","family":"Iyengar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C. C.","family":"Jorgensen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C. R.","family":"Weisbin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2007,3,13]]},"reference":[{"key":"e_1_2_1_2_2","doi-asserted-by":"crossref","unstructured":"N. J.Nilsson \u201cMobile Automation: An Application of Artificial Intelligence Techniques \u201dProc. 1st Int. Joint Conf. Artificial Intelligence May1969 pp.509\u2013520.","DOI":"10.21236\/ADA459660"},{"key":"e_1_2_1_3_2","unstructured":"A. M.Thompson \u201cThe Navigation System of the JPL Robot \u201dProc. 5th Int. Joint Conf. Artificial Intelligence Cambridge MA August 22\u201325 1977 pp.749\u2013757."},{"key":"e_1_2_1_4_2","unstructured":"G.Giralt R.Sobek andR.Chatila \u201cA Multilevel Planning and Navigation System for a Mobile Robot \u201dProc. 6th Int. Joint Conf. Artificial Intelligence Tokyo August 20\u201323 1979 pp.335\u2013338."},{"key":"e_1_2_1_5_2","unstructured":"H. P.Moravec \u201cThe CMU Rover \u201dProc. Nat. 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A.Stephenov \u201cEffect of Uncertainty on Continuous Path Planning for an Autonomous Vehicle \u201dProc. 23rd Conf. on Decision and Control Las Vegas Nevada December1984.","DOI":"10.1109\/CDC.1984.272354"},{"key":"e_1_2_1_16_2","doi-asserted-by":"publisher","DOI":"10.1017\/S0263574700008559"},{"key":"e_1_2_1_17_2","unstructured":"S. S.Iyengar C. C.Jorgensen S. V. N.Rao andC. R.Weisbin \u201cLearned Navigation Paths for a Robot in Unexplored Terrain \u201dProc. 2nd Conf. Artificial Intelligence Applications Miami Beach FL December 11\u201313 1985 pp.148\u2013155."},{"key":"e_1_2_1_18_2","unstructured":"R.Chatila \u201cPath Planning and Environment Learning in a Mobile Robot System \u201d inProc. European Conf. Artificial Intelligence Torsey France 1982."},{"key":"e_1_2_1_19_2","unstructured":"J.Laumond \u201cModel Structuring and Concept Recognition: Two Aspects of Learning for a Mobile Robot \u201dProc. 8th Conf. 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