{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T21:50:26Z","timestamp":1780091426537,"version":"3.54.0"},"reference-count":24,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,2,2]],"date-time":"2018-02-02T00:00:00Z","timestamp":1517529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A novel real-time reaction obstacle avoidance algorithm (RRA) is proposed for autonomous underwater vehicles (AUVs) that must adapt to unknown complex terrains, based on forward looking sonar (FLS). To accomplish this algorithm, obstacle avoidance rules are planned, and the RRA processes are split into five steps Introduction only lists 4 so AUVs can rapidly respond to various environment obstacles. The largest polar angle algorithm (LPAA) is designed to change detected obstacle\u2019s irregular outline into a convex polygon, which simplifies the obstacle avoidance process. A solution is designed to solve the trapping problem existing in U-shape obstacle avoidance by an outline memory algorithm. Finally, simulations in three unknown obstacle scenes are carried out to demonstrate the performance of this algorithm, where the obtained obstacle avoidance trajectories are safety, smooth and near-optimal.<\/jats:p>","DOI":"10.3390\/s18020438","type":"journal-article","created":{"date-parts":[[2018,2,2]],"date-time":"2018-02-02T12:00:10Z","timestamp":1517572810000},"page":"438","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["A Real-Time Reaction Obstacle Avoidance Algorithm for Autonomous Underwater Vehicles in Unknown Environments"],"prefix":"10.3390","volume":"18","author":[{"given":"Zheping","family":"Yan","sequence":"first","affiliation":[{"name":"Marine Assembly and Automatic Technology Institute, College of Automation, Harbin Engineering University, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiyun","family":"Li","sequence":"additional","affiliation":[{"name":"Marine Assembly and Automatic Technology Institute, College of Automation, Harbin Engineering University, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gengshi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Marine Assembly and Automatic Technology Institute, College of Automation, Harbin Engineering University, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Wu","sequence":"additional","affiliation":[{"name":"Marine Assembly and Automatic Technology Institute, College of Automation, Harbin Engineering University, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.oceaneng.2015.07.039","article-title":"Globally Finite-Time Stable Tracking Control of Underactuated UUVs","volume":"107","author":"Yan","year":"2015","journal-title":"Ocean Eng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xiang, X.B., Yu, C.Y., Niu, Z.M., and Zhang, Q. (2016). Subsea Cable Tracking by Autonomous Underwater Vehicle with Magnetic Sensing Guidance. Sensors, 16.","DOI":"10.3390\/s16081335"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.oceaneng.2014.09.001","article-title":"Shell Space Decomposition based Path Planning for AUVs Operating in a Variable Environment","volume":"91","author":"Zeng","year":"2014","journal-title":"Ocean Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1049\/iet-cta.2009.0265","article-title":"Position-Tracking Control of Underactuated Autonomous Underwater Vehicles in the Presence of Unknown Ocean Currents","volume":"4","author":"Bi","year":"2010","journal-title":"IET Control Theory Appl."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cai, W., Zhang, M.Y., and Zheng, Y.R. (2017). Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles Using 3D Dubins Curves. Sensors, 17.","DOI":"10.3390\/s17071607"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11370-013-0138-2","article-title":"Multipoint Potential Field Method for Path Planning of Autonomous Underwater Vehicles in 3D Space","volume":"6","author":"Saravanakumar","year":"2013","journal-title":"Intell. Serv. Robot."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.robot.2016.04.007","article-title":"A Novel Potential Field Method for Path Planning of Mobile Robots by Adapting Animal Motion Attributes","volume":"82","author":"Szayer","year":"2016","journal-title":"Robot. Auton. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2075","DOI":"10.1049\/iet-cta.2015.0071","article-title":"Receding Horizon Particle Swarm Optimisation-Based Formation Control with Collision Avoidance for Non-Holonomic Mobile Robots","volume":"9","author":"Lee","year":"2015","journal-title":"IET Control Theory Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.neucom.2015.11.007","article-title":"A PSO-Based Multi-Robot Cooperation Method for Target Searching in Unknown Environments","volume":"177","author":"Dadgar","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1016\/j.fss.2005.05.042","article-title":"An Obstacle-Avoidance Technique for Autonomous Underwater Vehicles Based on BK-Products of Fuzzy relation","volume":"157","author":"Bui","year":"2006","journal-title":"Fuzzy Sets Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"299","DOI":"10.3233\/IFS-2012-0554","article-title":"A Behavior-Based Approach for Collision Avoidance of Mobile Robots in Unknown and Dynamic Environments","volume":"24","author":"Nakhaeinia","year":"2013","journal-title":"Intell. Fuzzy Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.ins.2003.07.003","article-title":"An Intelligent Collision Avoidance System for AUVs Using Fuzzy Relational Products","volume":"158","author":"Lee","year":"2004","journal-title":"Inf. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neucom.2016.02.042","article-title":"Fully-Tuned Fuzzy Neural Network based Robust Adaptive Tracking Control of Unmanned Underwater Vehicle with thruster Dynamics","volume":"196","author":"Liu","year":"2016","journal-title":"Neurocomputering"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.robot.2015.04.007","article-title":"Navigation of Multiple Mobile Robots in a Highly Clutter Terrains Using Adaptive Neuro-Fuzzy Inference System","volume":"72","author":"Pothal","year":"2015","journal-title":"Robot. Auton. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2260","DOI":"10.1016\/j.engappai.2013.08.017","article-title":"The Bio-Inspired Model Based Hybrid Sliding-Mode Tracking Control for Unmanned Underwater Vehicles","volume":"26","author":"Zhu","year":"2013","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.robot.2016.03.011","article-title":"A Comparison of Optimization Techniques for AUV Path Planning in Environments with Ocean Currents","volume":"82","author":"Zeng","year":"2016","journal-title":"Robot. Auton. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.robot.2015.06.006","article-title":"An Innovative Decentralized Strategy for I-AUVs Cooperative Manipulation Tasks","volume":"72","author":"Conti","year":"2015","journal-title":"Robot. Auton. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1080\/08839514.2015.1004614","article-title":"Imperialist Competitive Algorithm for AUV Path Planning in a Variable Ocean","volume":"29","author":"Zeng","year":"2015","journal-title":"Appl. Artif. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1016\/j.neucom.2016.05.057","article-title":"A Hybrid Improved PSO-DV Algorithm for Multi-Robot Path Planning in a Clutter Environment","volume":"207","author":"Das","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.apor.2012.06.002","article-title":"3D Path Planning for Underwater Vehicles Using Five Evolutionary Optimization Algorithms Avoiding Static and Energetic Obstacles","volume":"38","author":"Aghababa","year":"2012","journal-title":"Appl. Ocean Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.oceaneng.2014.11.001","article-title":"Applying the Self-Tuning Fuzzy Control with the Image Detection Technique on the Obstacle-Avoidance for Autonomous Underwater Vehicles","volume":"93","author":"Fang","year":"2015","journal-title":"Ocean Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1049\/iet-cta.2014.0472","article-title":"On the Neuro-Adaptive Feedback Linearising Control of Underactuated Autonomous Underwater Vehicles in Three-Dimensional Space","volume":"9","author":"Shojaei","year":"2015","journal-title":"IET Control Theory Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1002\/rob.4620080307","article-title":"Adaptive Control of Nonlinear Systems: A Case Study of Underwater Robotic Systems","volume":"8","author":"Fossen","year":"1991","journal-title":"J. Robot. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Norgren, P., and Skjetne, R. (2015, January 24\u201327). Line-of-Sight iceberg Edge-Following Using an AUV Equipped With Multibeam Sonar. Proceedings of the 2015 16th IFAC on Technology, Culture and International Stability, Sozopos, Bulgaria.","DOI":"10.1016\/j.ifacol.2015.10.262"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/2\/438\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:53:37Z","timestamp":1760194417000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/2\/438"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,2,2]]},"references-count":24,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2018,2]]}},"alternative-id":["s18020438"],"URL":"https:\/\/doi.org\/10.3390\/s18020438","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,2,2]]}}}