{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T21:43:00Z","timestamp":1782510180098,"version":"3.54.5"},"publisher-location":"Cham","reference-count":77,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032009852","type":"print"},{"value":"9783032009869","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-00986-9_28","type":"book-chapter","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T23:24:29Z","timestamp":1759274669000},"page":"433-453","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["NAUTICAL: Navigation Aid Using U-Net and\u00a0$$Theta^*$$ with\u00a0Integrated Collision Avoidance and\u00a0Landmarking"],"prefix":"10.1007","author":[{"given":"Yashwardhan","family":"Deshmukh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8763-0536","authenticated-orcid":false,"given":"Martin J.-D.","family":"Otis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8503-6093","authenticated-orcid":false,"given":"Salick","family":"Diagne","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,1]]},"reference":[{"key":"28_CR1","doi-asserted-by":"publisher","unstructured":"Al\u00a0Maawali, W., Mesbah, M., Al\u00a0Maashri, A., Saleem, A.: Design of intelligent thruster decision-making system for USVs. Ocean Eng. 285 (2023). https:\/\/doi.org\/10.1016\/j.oceaneng.2023.115431","DOI":"10.1016\/j.oceaneng.2023.115431"},{"key":"28_CR2","doi-asserted-by":"publisher","unstructured":"Changxin, Z., et al.: UAV electric patrol path planning based on improved ant colony optimization-a* algorithm. In: IEEE International Conference on Electrical Engineering, Big Data and Algorithms, pp. 1374 \u2013 1380 (2022). https:\/\/doi.org\/10.1109\/EEBDA53927.2022.9744949","DOI":"10.1109\/EEBDA53927.2022.9744949"},{"key":"28_CR3","doi-asserted-by":"publisher","unstructured":"Chen, H., Liang, Y., Meng, X.: A UAV path planning method for building surface information acquisition utilizing opposition-based learning artificial bee colony algorithm. Remote Sens. 15(17) (2023). https:\/\/doi.org\/10.3390\/rs15174312","DOI":"10.3390\/rs15174312"},{"key":"28_CR4","doi-asserted-by":"publisher","first-page":"126439","DOI":"10.1109\/ACCESS.2019.2936689","volume":"7","author":"Z Chen","year":"2019","unstructured":"Chen, Z., Zhang, Y., Zhang, Y., Nie, Y., Tang, J., Zhu, S.: A hybrid path planning algorithm for unmanned surface vehicles in complex environment with dynamic obstacles. IEEE Access 7, 126439\u2013126449 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2936689","journal-title":"IEEE Access"},{"key":"28_CR5","doi-asserted-by":"publisher","unstructured":"Chun, D.H., Roh, M.I., Lee, H.W., Ha, J., Yu, D.: Deep reinforcement learning-based collision avoidance for an autonomous ship. Ocean Eng. 234 (2021). https:\/\/doi.org\/10.1016\/j.oceaneng.2021.109216","DOI":"10.1016\/j.oceaneng.2021.109216"},{"key":"28_CR6","doi-asserted-by":"publisher","first-page":"59486","DOI":"10.1109\/ACCESS.2021.3073704","volume":"9","author":"Z Cui","year":"2021","unstructured":"Cui, Z., Wang, Y.: UAV path planning based on multi-layer reinforcement learning technique. IEEE Access 9, 59486\u201359497 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3073704","journal-title":"IEEE Access"},{"key":"28_CR7","doi-asserted-by":"publisher","unstructured":"Dai, J., Chen, D., Yang, S.: Path planning method of unmanned ship based on PPO and DWA. In: SPIE - The International Society for Optical Engineering. vol. 12793 (2023). https:\/\/doi.org\/10.1117\/12.3006648","DOI":"10.1117\/12.3006648"},{"key":"28_CR8","doi-asserted-by":"publisher","unstructured":"Daniel, K., Nash, A., Koenig, S., Felner, A.: Theta*: any-angle path planning on grids. J. Artif. Intell. Res. 39, 533\u2013579 (2010). https:\/\/doi.org\/10.1613\/jair.2994","DOI":"10.1613\/jair.2994"},{"key":"28_CR9","doi-asserted-by":"publisher","unstructured":"Deshmukh, Y., Otis, M.: Source code for inland navigation and vessel optimal path planning. [Source code] OSF (2024). https:\/\/doi.org\/10.17605\/OSF.IO\/24ATK","DOI":"10.17605\/OSF.IO\/24ATK"},{"key":"28_CR10","doi-asserted-by":"publisher","unstructured":"Erke, S., Bin, D., Yiming, N., Qi, Z., Liang, X., Dawei, Z.: An improved a-star based path planning algorithm for autonomous land vehicles. Int. J. Adv. Robotic Syst. 17(5) (2020). https:\/\/doi.org\/10.1177\/1729881420962263","DOI":"10.1177\/1729881420962263"},{"key":"28_CR11","doi-asserted-by":"publisher","unstructured":"Fan, Y., Sun, Z., Wang, G.: A novel intelligent collision avoidance algorithm based on deep reinforcement learning approach for USV. Ocean Eng. 287 (2023). https:\/\/doi.org\/10.1016\/j.oceaneng.2023.115649","DOI":"10.1016\/j.oceaneng.2023.115649"},{"key":"28_CR12","doi-asserted-by":"publisher","unstructured":"Fusic, S.J., Sitharthan, R., Masthan, S.S., Hariharan, K.: Autonomous vehicle path planning for smart logistics mobile applications based on modified heuristic algorithm. Measur. Sci. Technol. 34(3) (2023). https:\/\/doi.org\/10.1088\/1361-6501\/aca708","DOI":"10.1088\/1361-6501\/aca708"},{"key":"28_CR13","doi-asserted-by":"publisher","unstructured":"Gao, D., Zhou, P., Shi, W., Wang, T., Wang, Y.: A dynamic obstacle avoidance method for unmanned surface vehicle under the international regulations for preventing collisions at sea. J. Marine Sci. Eng. 10(7) (2022). https:\/\/doi.org\/10.3390\/jmse10070901","DOI":"10.3390\/jmse10070901"},{"issue":"3","key":"28_CR14","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1109\/MITS.2022.3229109","volume":"15","author":"W Guan","year":"2023","unstructured":"Guan, W., Wang, K.: Autonomous collision avoidance of unmanned surface vehicles based on improved a-star and dynamic window approach algorithms. IEEE Intell. Transp. Syst. Mag. 15(3), 36\u201350 (2023). https:\/\/doi.org\/10.1109\/MITS.2022.3229109","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"issue":"4","key":"28_CR15","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1139\/er-2019-0033","volume":"28","author":"WD Halliday","year":"2020","unstructured":"Halliday, W.D., Pine, M.K., Insley, S.J.: Underwater noise and arctic marine mammals: review and policy recommendations. Environ. Rev. 28(4), 438\u2013448 (2020)","journal-title":"Environ. Rev."},{"issue":"4","key":"28_CR16","doi-asserted-by":"publisher","first-page":"337","DOI":"10.14430\/arctic69294","volume":"72","author":"WD Halliday","year":"2019","unstructured":"Halliday, W.D., et al.: Beluga vocalizations decrease in response to vessel traffic in the Mackenzie river estuary. Arctic 72(4), 337\u2013346 (2019)","journal-title":"Arctic"},{"key":"28_CR17","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"28_CR18","unstructured":"Hirai, Y., Tsuchiya, T., Shimizu, E.: Estimation of sound exposure level based on relative movement of ship and whale. In: International Congress on Sound and Vibration (2017)"},{"issue":"1\u20133","key":"28_CR19","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/0004-3702(81)90024-2","volume":"17","author":"BK Horn","year":"1981","unstructured":"Horn, B.K., Schunck, B.G.: Determining optical flow. Artif. Intell. 17(1\u20133), 185\u2013203 (1981). https:\/\/doi.org\/10.1016\/0004-3702(81)90024-2","journal-title":"Artif. Intell."},{"key":"28_CR20","doi-asserted-by":"publisher","unstructured":"Hu, B., Wan, Y., Lei, Y.: Collision avoidance of USV by model predictive control-aided deep reinforcement learning. In: IEEE International Conference on Industrial Technology (2022). https:\/\/doi.org\/10.1109\/ICIT48603.2022.10002769","DOI":"10.1109\/ICIT48603.2022.10002769"},{"key":"28_CR21","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Weinberger, K.Q.: Densely connected convolutional networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261\u20132269 (2016)","DOI":"10.1109\/CVPR.2017.243"},{"issue":"6","key":"28_CR22","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.5194\/os-14-1373-2018","volume":"14","author":"JP Jalkanen","year":"2018","unstructured":"Jalkanen, J.P., et al.: Modelling of ships as a source of underwater noise. Ocean Sci. 14(6), 1373\u20131383 (2018)","journal-title":"Ocean Sci."},{"issue":"8","key":"28_CR23","doi-asserted-by":"publisher","first-page":"7445","DOI":"10.1007\/s13369-021-05445-6","volume":"46","author":"X Ji","year":"2021","unstructured":"Ji, X., Feng, S., Han, Q., Yin, H., Yu, S.: Improvement and fusion of A* algorithm and dynamic window approach considering complex environmental information. Arab. J. Sci. Eng. 46(8), 7445\u20137459 (2021). https:\/\/doi.org\/10.1007\/s13369-021-05445-6","journal-title":"Arab. J. Sci. Eng."},{"key":"28_CR24","doi-asserted-by":"publisher","DOI":"10.1177\/00202940231195937","author":"D Jiang","year":"2023","unstructured":"Jiang, D., Yuan, M., Xiong, J., Xiao, J., Duan, Y.: Obstacle avoidance USV in multi-static obstacle environments based on a deep reinforcement learning approach. Measur. Control (United Kingdom) (2023). https:\/\/doi.org\/10.1177\/00202940231195937","journal-title":"Measur. Control (United Kingdom)"},{"key":"28_CR25","doi-asserted-by":"publisher","unstructured":"Kim, H.G., Yun, S.J., Choi, Y.H., Ryu, J.K., Suh, J.H.: Collision avoidance algorithm based on colregs for unmanned surface vehicle. J. Mar. Sci. Eng. 9(8) (2021). https:\/\/doi.org\/10.3390\/jmse9080863","DOI":"10.3390\/jmse9080863"},{"key":"28_CR26","doi-asserted-by":"publisher","unstructured":"Li, L., Sheng, W.: Collision avoidance dynamic window approach in multi-agent system. In: Chinese Automation Congress, pp. 2307\u20132311 (2020). https:\/\/doi.org\/10.1109\/CAC51589.2020.9327673","DOI":"10.1109\/CAC51589.2020.9327673"},{"key":"28_CR27","doi-asserted-by":"publisher","unstructured":"Li, L., Wu, D., Huang, Y., Yuan, Z.M.: A path planning strategy unified with a colregs collision avoidance function based on deep reinforcement learning and artificial potential field. Appl. Ocean Res. 113 (2021). https:\/\/doi.org\/10.1016\/j.apor.2021.102759","DOI":"10.1016\/j.apor.2021.102759"},{"key":"28_CR28","doi-asserted-by":"publisher","unstructured":"Li, Y., Zuo, Y., Shan, Q., Li, T.: Path planning of ship collision avoidance for minimized energy consumption. In: Chinese Control and Decision Conference, p. 177\u2013182 (2021). https:\/\/doi.org\/10.1109\/CCDC52312.2021.9601496","DOI":"10.1109\/CCDC52312.2021.9601496"},{"key":"28_CR29","doi-asserted-by":"publisher","unstructured":"Li, Y., Zhang, H.: Collision avoidance decision method for unmanned surface vehicle based on an improved velocity obstacle algorithm. J. Marine Sci. Eng. 10(8) (2022). https:\/\/doi.org\/10.3390\/jmse10081047","DOI":"10.3390\/jmse10081047"},{"issue":"2","key":"28_CR30","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1177\/1475090220970102","volume":"235","author":"Y Li","year":"2021","unstructured":"Li, Y., Zheng, J.: Deep learning structure for collision avoidance planning of unmanned surface vessel. Proc. Inst. Mech. Eng. Part M: J. Eng. Marit. Environ. 235(2), 511\u2013520 (2021). https:\/\/doi.org\/10.1177\/1475090220970102","journal-title":"Proc. Inst. Mech. Eng. Part M: J. Eng. Marit. Environ."},{"key":"28_CR31","doi-asserted-by":"publisher","unstructured":"Liang, C., Zhang, X., Watanabe, Y., Deng, Y.: Autonomous collision avoidance of unmanned surface vehicles based on improved a star and minimum course alteration algorithms. Appl. Ocean Res. 113 (2021). https:\/\/doi.org\/10.1016\/j.apor.2021.102755","DOI":"10.1016\/j.apor.2021.102755"},{"key":"28_CR32","doi-asserted-by":"publisher","first-page":"102450","DOI":"10.1109\/ACCESS.2021.3097945","volume":"9","author":"HY Lin","year":"2021","unstructured":"Lin, H.Y., Peng, X.Z.: Autonomous quadrotor navigation with vision based obstacle avoidance and path planning. IEEE Access 9, 102450\u2013102459 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3097945","journal-title":"IEEE Access"},{"key":"28_CR33","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R.B., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2999\u20133007 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"28_CR34","doi-asserted-by":"publisher","first-page":"114840","DOI":"10.1109\/ACCESS.2023.3325483","volume":"11","author":"X Mu","year":"2023","unstructured":"Mu, X., Gao, W., Li, X., Li, G.: Coverage path planning for UAV based on improved back-and-forth mode. IEEE Access 11, 114840\u2013114854 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3325483","journal-title":"IEEE Access"},{"key":"28_CR35","doi-asserted-by":"crossref","unstructured":"Mu\u00f1oz, P., R-Moreno, M.D.: S-theta: low steering path-planning algorithm. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX, pp. 109\u2013121. Springer London, London (2012)","DOI":"10.1007\/978-1-4471-4739-8_8"},{"key":"28_CR36","doi-asserted-by":"publisher","unstructured":"Mu\u00f1oz, P., R-Moreno, M.D., Casta\u00f1o, B.: 3Dana: a path planning algorithm for surface robotics. Eng. Appl. Artif. Intell. 60, 175\u2013192 (2017). https:\/\/doi.org\/10.1016\/j.engappai.2017.02.010, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0952197617300337","DOI":"10.1016\/j.engappai.2017.02.010"},{"key":"28_CR37","unstructured":"Oliphant, T.E., et\u00a0al.: SciPy: Open source scientific tools for Python (2001). https:\/\/www.scipy.org\/. [Online; accessed <today\u2019s date>]"},{"key":"28_CR38","unstructured":"Otis, M., Deshmukh, Y.: Images for inland navigation and vessel optimal path planning. [dataset] Borealis (2024). https:\/\/doi.org\/10.5683\/SP3\/KZNEOU"},{"key":"28_CR39","doi-asserted-by":"crossref","unstructured":"Perez, T., Smogelif, o., Fossenf, T., Sorensen, A.: An overview of the marine systems simulator (MSS): a Simulink toolbox for marine control systems. Model. Ident. Control 27(4), 259\u2013275 (2006)","DOI":"10.4173\/mic.2006.4.4"},{"key":"28_CR40","doi-asserted-by":"publisher","unstructured":"Ren, J., Zhang, J., Cui, Y.: Autonomous obstacle avoidance algorithm for unmanned surface vehicles based on an improved velocity obstacle method. ISPRS Int. J. Geo-Inf. 10(9) (2021). https:\/\/doi.org\/10.3390\/ijgi10090618","DOI":"10.3390\/ijgi10090618"},{"key":"28_CR41","unstructured":"Salah, I.B., Otis, M., Rahem, R., Ardhaoui, M.: Objects detection on the water surface using satellite imagery, drones and vessel-based imaging applied for logistics. In: International conference on Robotic, Computer Vision and Intelligent Systems (ROBOVIS). Springer CCIS (2025)"},{"key":"28_CR42","doi-asserted-by":"crossref","unstructured":"Schoeman, R.P., Patterson-Abrolat, C., Pl\u00f6n, S.: A global review of vessel collisions with marine animals. Front. Marine Sci. 7 (2020)","DOI":"10.3389\/fmars.2020.00292"},{"key":"28_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.biocon.2023.110422","volume":"289","author":"JL Scott","year":"2024","unstructured":"Scott, J.L., et al.: The whalereport alert system: mitigating threats to whales with citizen science. Biol. Cons. 289, 110422 (2024)","journal-title":"Biol. Cons."},{"key":"28_CR44","doi-asserted-by":"publisher","unstructured":"Seo, C., Noh, Y., Abebe, M., Kang, Y.J., Park, S., Kwon, C.: Ship collision avoidance route planning using cri-based a* algorithm. Int. J. Naval Archit. Ocean Eng. 15 (2023). https:\/\/doi.org\/10.1016\/j.ijnaoe.2023.100551","DOI":"10.1016\/j.ijnaoe.2023.100551"},{"key":"28_CR45","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)"},{"key":"28_CR46","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.oceaneng.2018.09.016","volume":"169","author":"Y Singh","year":"2018","unstructured":"Singh, Y., Sharma, S., Sutton, R., Hatton, D., Khan, A.: A constrained a* approach towards optimal path planning for an unmanned surface vehicle in a maritime environment containing dynamic obstacles and ocean currents. Ocean Eng. 169, 187\u2013201 (2018). https:\/\/doi.org\/10.1016\/j.oceaneng.2018.09.016","journal-title":"Ocean Eng."},{"issue":"20","key":"28_CR47","doi-asserted-by":"publisher","first-page":"19773","DOI":"10.1109\/JIOT.2022.3168589","volume":"9","author":"N Su","year":"2022","unstructured":"Su, N., Wang, J.B., Zeng, C., Zhang, H., Lin, M., Li, G.Y.: Unmanned-surface-vehicle-aided maritime data collection using deep reinforcement learning. IEEE Internet Things J. 9(20), 19773\u201319786 (2022). https:\/\/doi.org\/10.1109\/JIOT.2022.3168589","journal-title":"IEEE Internet Things J."},{"key":"28_CR48","doi-asserted-by":"crossref","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge\u00a0Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 240\u2013248. Lecture notes in computer science, Springer, Cham (2017)","DOI":"10.1007\/978-3-319-67558-9_28"},{"key":"28_CR49","doi-asserted-by":"crossref","unstructured":"S\u00e8be, M., Kontovas, C.A., Pendleton, L.: A decision-making framework to reduce the risk of collisions between ships and whales. Marine Policy 109 (2019)","DOI":"10.1016\/j.marpol.2019.103697"},{"key":"28_CR50","doi-asserted-by":"crossref","unstructured":"S\u00e8be, M., Kontovas, C.A., Pendleton, L.: Reducing whale-ship collisions by better estimating damages to ships. Sci. Total Environ. 713 (2020)","DOI":"10.1016\/j.scitotenv.2020.136643"},{"issue":"5","key":"28_CR51","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1017\/S0373463322000315","volume":"75","author":"Y Tao","year":"2022","unstructured":"Tao, Y., Du, J.: Agile collision avoidance for unmanned surface vehicles based on collision shielded model prediction control algorithm. J. Navig. 75(5), 1243\u20131267 (2022). https:\/\/doi.org\/10.1017\/S0373463322000315","journal-title":"J. Navig."},{"key":"28_CR52","doi-asserted-by":"publisher","unstructured":"Van\u00a0Dang, C., Ahn, H., Lee, D.S., Lee, S.C.: A path planning method based on theta-star search for non-holonomic robots. In: 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems, pp.\u00a01\u20136 (2022). https:\/\/doi.org\/10.1109\/SCISISIS55246.2022.10002141","DOI":"10.1109\/SCISISIS55246.2022.10002141"},{"key":"28_CR53","unstructured":"Vooban: Smoothly-blend-image-patches (2017). https:\/\/github.com\/Vooban\/Smoothly-Blend-Image-Patches"},{"key":"28_CR54","doi-asserted-by":"publisher","first-page":"49233","DOI":"10.1109\/ACCESS.2021.3058288","volume":"9","author":"D Wang","year":"2021","unstructured":"Wang, D., Zhang, J., Jin, J., Mao, X.: Local collision avoidance algorithm for a unmanned surface vehicle based on steering maneuver considering colregs. IEEE Access 9, 49233\u201349248 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3058288","journal-title":"IEEE Access"},{"key":"28_CR55","doi-asserted-by":"publisher","unstructured":"Wang, P., Yao, X., Fei, Q., Meng, J.: Research on local path planning for autonomous collision avoidance of USV. In: Chinese Automation Congress, pp. 5368\u20135373 (2022). https:\/\/doi.org\/10.1109\/CAC57257.2022.10055154","DOI":"10.1109\/CAC57257.2022.10055154"},{"key":"28_CR56","doi-asserted-by":"publisher","unstructured":"Wang, Z., Chen, P., Mou, J., Chen, L.: Colregs-compliant autonomous collision avoidance method based on deep reinforcement learning for USVs. In: IEEE International Conference on Transportation Information and Safety, pp. 1575\u20131598 (2023). https:\/\/doi.org\/10.1109\/ICTIS60134.2023.10243729","DOI":"10.1109\/ICTIS60134.2023.10243729"},{"key":"28_CR57","doi-asserted-by":"publisher","unstructured":"Wenming, W., Jialu, D., Yihan, T.: A dynamic collision avoidance solution scheme of unmanned surface vessels based on proactive velocity obstacle and set-based guidance. Ocean Eng. 248 (2022). https:\/\/doi.org\/10.1016\/j.oceaneng.2022.110794","DOI":"10.1016\/j.oceaneng.2022.110794"},{"key":"28_CR58","doi-asserted-by":"crossref","unstructured":"Wiig, M.S., Pettersen, K.Y., Ruud, E.L.M., Krogstad, T.R.: An integral line-of-sight guidance law with a speed-dependent lookahead distance. In: European Control Conference, p. 1269\u20131276 (2018)","DOI":"10.23919\/ECC.2018.8550243"},{"issue":"1","key":"28_CR59","doi-asserted-by":"publisher","first-page":"7","DOI":"10.5957\/JSPD.30.1.120052","volume":"30","author":"DK Wittekind","year":"2014","unstructured":"Wittekind, D.K.: A simple model for the underwater noise source level of ships. J. Ship Prod. Design 30(1), 7\u201314 (2014)","journal-title":"J. Ship Prod. Design"},{"key":"28_CR60","doi-asserted-by":"publisher","unstructured":"Woo, J., Kim, N.: Collision avoidance for an unmanned surface vehicle using deep reinforcement learning. Ocean Eng. 199 (2020). https:\/\/doi.org\/10.1016\/j.oceaneng.2020.107001","DOI":"10.1016\/j.oceaneng.2020.107001"},{"key":"28_CR61","doi-asserted-by":"publisher","unstructured":"Xia, G., Han, Z., Zhao, B., Wang, X.: Local path planning for unmanned surface vehicle collision avoidance based on modified quantum particle swarm optimization. Complexity 2020 (2020). https:\/\/doi.org\/10.1155\/2020\/3095426","DOI":"10.1155\/2020\/3095426"},{"issue":"11","key":"28_CR62","doi-asserted-by":"publisher","first-page":"11262","DOI":"10.1109\/JSEN.2022.3222575","volume":"23","author":"J Xia","year":"2023","unstructured":"Xia, J., Zhu, X., Liu, Z., Luo, Y., Wu, Z., Wu, Q.: Research on collision avoidance algorithm of unmanned surface vehicle based on deep reinforcement learning. IEEE Sens. J. 23(11), 11262\u201311273 (2023). https:\/\/doi.org\/10.1109\/JSEN.2022.3222575","journal-title":"IEEE Sens. J."},{"key":"28_CR63","doi-asserted-by":"publisher","unstructured":"Xia, Z., Liu, T., Zhang, J., Liu, L.: Dynamic obstacle avoidance method of USV based on secure ACO with hazard index. In: IEEE International Conference on Computer and Communications, pp. 2370\u20132374 (2022). https:\/\/doi.org\/10.1109\/ICCC56324.2022.10065702","DOI":"10.1109\/ICCC56324.2022.10065702"},{"key":"28_CR64","doi-asserted-by":"publisher","unstructured":"Xie, R., Meng, Z., Zhou, Y., Ma, Y., Wu, Z.: Heuristic q-learning based on experience replay for three-dimensional path planning of the unmanned aerial vehicle. Sci. Progress 103(1) (2020). https:\/\/doi.org\/10.1177\/0036850419879024","DOI":"10.1177\/0036850419879024"},{"key":"28_CR65","doi-asserted-by":"publisher","unstructured":"Xu, X., Lu, Y., Liu, G., Cai, P., Zhang, W.: Colregs-abiding hybrid collision avoidance algorithm based on deep reinforcement learning for USVs. Ocean Eng. 247 (2022). https:\/\/doi.org\/10.1016\/j.oceaneng.2022.110749","DOI":"10.1016\/j.oceaneng.2022.110749"},{"key":"28_CR66","doi-asserted-by":"publisher","unstructured":"Xu, Y., Wei, Y., Wang, D., Jiang, K., Deng, H.: Multi-UAV path planning in GPS and communication denial environment. Sensors 23(6) (2023). https:\/\/doi.org\/10.3390\/s23062997","DOI":"10.3390\/s23062997"},{"key":"28_CR67","doi-asserted-by":"publisher","unstructured":"Yang, S., Huang, J., Xiang, X., Li, W.: Optimization of USV area coverage path planning based on confidence ellipsoid;. Xi Tong Gong Cheng Yu Dian Zi Ji Shu\/Systems Engineering and Electronics 44(7), 2263\u20132269 (2022). https:\/\/doi.org\/10.12305\/j.issn.1001-506X.2022.07.22","DOI":"10.12305\/j.issn.1001-506X.2022.07.22"},{"key":"28_CR68","unstructured":"Yonetani, R., Taniai, T., Barekatain, M., Nishimura, M., Kanezaki, A.: Path planning using neural a* search. In: Machine Learning Research. vol.\u00a0139, pp. 12029\u201312039 (2021)"},{"key":"28_CR69","doi-asserted-by":"publisher","unstructured":"Yuan, S., Liu, Z., Sun, Y., Song, S., Wang, Z., Zheng, L.: EMPMR berthing scheme: a novel event-triggered motion planning and motion replanning scheme for unmanned surface vessels. Ocean Eng. 286 (2023). https:\/\/doi.org\/10.1016\/j.oceaneng.2023.115666","DOI":"10.1016\/j.oceaneng.2023.115666"},{"key":"28_CR70","doi-asserted-by":"publisher","unstructured":"Yuan, X., Tong, C., He, G., Wang, H.: Unmanned vessel collision avoidance algorithm by dynamic window approach based on colregs considering the effects of the wind and wave. J. Marine Sci. Eng. 11(9) (2023). https:\/\/doi.org\/10.3390\/jmse11091831, https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85172797778&doi=10.3390%2fjmse11091831 &partnerID=40 &md5=5960bbd04afb23fbfbc6fcd5f2d4e649 cited by: 3; All Open Access, Gold Open Access","DOI":"10.3390\/jmse11091831"},{"issue":"5","key":"28_CR71","doi-asserted-by":"publisher","first-page":"7252","DOI":"10.1109\/TAES.2023.3286823","volume":"59","author":"J Zhang","year":"2023","unstructured":"Zhang, J., Cui, Y., Li, G., Ren, J.: Dynamic path planning algorithm for unmanned surface vehicle under island-reef environment. IEEE Trans. Aerosp. Electron. Syst. 59(5), 7252\u20137268 (2023). https:\/\/doi.org\/10.1109\/TAES.2023.3286823","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"28_CR72","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1145\/357994.358023","volume":"27","author":"TY Zhang","year":"1984","unstructured":"Zhang, T.Y., Suen, C.Y.: A fast parallel algorithm for thinning digital patterns. Commun. ACM 27, 236\u2013239 (1984)","journal-title":"Commun. ACM"},{"key":"28_CR73","doi-asserted-by":"publisher","unstructured":"Zhang, W.L., Shan, L., Chang, L., Qi, Z.D., Dai, Y.W.: Distributed collision avoidance algorithm for multiple unmanned surface vessels based on improved DWA. Kongzhi yu Juece\/Control and Decision 38(4), 951\u2013962 (2023). https:\/\/doi.org\/10.13195\/j.kzyjc.2021.1744","DOI":"10.13195\/j.kzyjc.2021.1744"},{"key":"28_CR74","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Xiao, Z., Yuan, X., Li, S., Liang, S.: Obstacle avoidance of two-wheeled mobile robot based on DWA algorithm. In: Chinese Automation Congress, pp. 5701\u20135706 (2019). https:\/\/doi.org\/10.1109\/CAC48633.2019.8996425","DOI":"10.1109\/CAC48633.2019.8996425"},{"issue":"3","key":"28_CR75","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1108\/JICV-01-2022-0001","volume":"5","author":"J Zhao","year":"2022","unstructured":"Zhao, J., Ma, X., Yang, B., Chen, Y., Zhou, Z., Xiao, P.: Global path planning of unmanned vehicle based on fusion of a* algorithm and voronoi field. J. Intell. Connected Veh. 5(3), 250\u2013259 (2022). https:\/\/doi.org\/10.1108\/JICV-01-2022-0001","journal-title":"J. Intell. Connected Veh."},{"key":"28_CR76","doi-asserted-by":"publisher","unstructured":"Zhu, X., Yan, B., Yue, Y.: Path planning and collision avoidance in unknown environments for USVs based on an improved d* lite. Appl. Sci. (Switzerland) 11(17) (2021). https:\/\/doi.org\/10.3390\/app11177863","DOI":"10.3390\/app11177863"},{"key":"28_CR77","doi-asserted-by":"crossref","unstructured":"Zuiderveld, K.J.: Contrast limited adaptive histogram equalization. In: Graphics gems (1994)","DOI":"10.1016\/B978-0-12-336156-1.50061-6"}],"container-title":["Communications in Computer and Information Science","Robotics, Computer Vision and Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-00986-9_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T09:55:49Z","timestamp":1782467749000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-00986-9_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,1]]},"ISBN":["9783032009852","9783032009869"],"references-count":77,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-00986-9_28","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,1]]},"assertion":[{"value":"1 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ROBOVIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Robotics, Computer Vision and Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Porto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 February 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 February 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"robovis2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/robovis.scitevents.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}