{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T22:34:26Z","timestamp":1781822066419,"version":"3.54.5"},"reference-count":45,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T00:00:00Z","timestamp":1690329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shenzhen Science and Technology Innovation Committee","award":["JCYJ20200109143006048"],"award-info":[{"award-number":["JCYJ20200109143006048"]}]},{"name":"Shenzhen Science and Technology Innovation Committee","award":["JCYJ20210324115813037"],"award-info":[{"award-number":["JCYJ20210324115813037"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the development of ocean exploration technology, the exploration of the ocean has become a hot research field involving the use of autonomous underwater vehicles (AUVs). In complex underwater environments, the fast, safe, and smooth arrival of target points is key for AUVs to conduct underwater exploration missions. Most path-planning algorithms combine deep reinforcement learning (DRL) and path-planning algorithms to achieve obstacle avoidance and path shortening. In this paper, we propose a method to improve the local minimum in the artificial potential field (APF) to make AUVs out of the local minimum by constructing a traction force. The improved artificial potential field (IAPF) method is combined with DRL for path planning while optimizing the reward function in the DRL algorithm and using the generated path to optimize the future path. By comparing our results with the experimental data of various algorithms, we found that the proposed method has positive effects and advantages in path planning. It is an efficient and safe path-planning method with obvious potential in underwater navigation devices.<\/jats:p>","DOI":"10.3390\/s23156680","type":"journal-article","created":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T02:14:48Z","timestamp":1690424088000},"page":"6680","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Improved Artificial Potential Field Algorithm Assisted by Multisource Data for AUV Path Planning"],"prefix":"10.3390","volume":"23","author":[{"given":"Tianyu","family":"Xing","sequence":"first","affiliation":[{"name":"Division of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohao","family":"Wang","sequence":"additional","affiliation":[{"name":"Division of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaiyang","family":"Ding","sequence":"additional","affiliation":[{"name":"Division of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8763-0061","authenticated-orcid":false,"given":"Kai","family":"Ni","sequence":"additional","affiliation":[{"name":"Division of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4212-4688","authenticated-orcid":false,"given":"Qian","family":"Zhou","sequence":"additional","affiliation":[{"name":"Division of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.oceaneng.2019.04.011","article-title":"Advancements in the field of autonomous underwater vehicle","volume":"181","author":"Sahoo","year":"2019","journal-title":"Ocean Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1002\/rob.22005","article-title":"Adaptive sampling with an autonomous underwater vehicle in static marine environments","volume":"38","author":"Stankiewicz","year":"2020","journal-title":"J. Field Robot."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"592","DOI":"10.26599\/TST.2021.9010002","article-title":"Convergence of broadband and broadcast\/multicast in maritime information networks","volume":"26","author":"Du","year":"2021","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cao, X., Ren, L., and Sun, C. (2022). Research on Obstacle Detection and Avoidance of Autonomous Underwater Vehicle Based on Forward-Looking Sonar. IEEE Trans. Neural Netw. Learn. Syst., 1\u201311.","DOI":"10.1109\/TNNLS.2022.3156907"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1109\/TVT.2018.2882130","article-title":"Path Planning for Autonomous Underwater Vehicles: An Ant Colony Algorithm Incorporating Alarm Pheromone","volume":"68","author":"Ma","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Cai, W., Zhang, M., 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_7","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.robot.2019.02.002","article-title":"Path planning of multiple autonomous marine vehicles for adaptive sampling using Voronoi-based ant colony optimization","volume":"115","author":"Xiong","year":"2019","journal-title":"Robot. Auton. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1687","DOI":"10.1007\/s00500-016-2433-2","article-title":"A novel versatile architecture for autonomous underwater vehicle\u2019s motion planning and task assignment","volume":"22","author":"Powers","year":"2018","journal-title":"Soft Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4273","DOI":"10.1109\/TII.2018.2815531","article-title":"Simultaneous Trajectory Planning and Tracking Using an MPC Method for Cyber-Physical Systems: A Case Study of Obstacle Avoidance for an Intelligent Vehicle","volume":"14","author":"Guo","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1109\/TCDS.2017.2727678","article-title":"Biologically Inspired Self-Organizing Map Applied to Task Assignment and Path Planning of an AUV System","volume":"10","author":"Zhu","year":"2017","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_11","first-page":"382","article-title":"An integrated AUV path planning algorithm with ocean current and dynamic obstacles","volume":"31","author":"Zhu","year":"2016","journal-title":"Int. J. Robot. Autom."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1080\/00207721.2014.929191","article-title":"UAV path planning using artificial potential field method updated by optimal control theory","volume":"47","author":"Chen","year":"2016","journal-title":"Int. J. Syst. Sci."},{"key":"ref_13","unstructured":"Chang, Z.-H., Tang, Z.-D., Cai, H.-G., Shi, X.-C., and Bian, X.-Q. (2005, January 18\u201321). GA path planning for AUV to avoid moving obstacles based on forward looking sonar. Proceedings of the 2005 International Conference on Machine Learning and Cybernetics, Guangzhou, China."},{"key":"ref_14","unstructured":"Dong, D., He, B., Liu, Y., Nian, R., and Yan, T. (2015, January 19\u201322). A novel path planning method based on extreme learning machine for autonomous underwater vehicle. Proceedings of the OCEANS 2015\u2014MTS\/IEEE Washington, Washington, DC, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.robot.2016.09.007","article-title":"An autonomous reactive architecture for efficient AUV mission time management in realistic dynamic ocean environment","volume":"87","author":"Powers","year":"2017","journal-title":"Robot. Auton. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, Y., Cheng, C., Zhang, Y., Li, X., and Sun, L. (2022). A Neural Network-Based Navigation Approach for Autonomous Mobile Robot Systems. Appl. Sci., 12.","DOI":"10.3390\/app12157796"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, B., Mao, J., Yin, S., Fu, L., and Wang, Y. (2022). Path Planning of Multi-Objective Underwater Robot Based on Improved Sparrow Search Algorithm in Complex Marine Environment. J. Mar. Sci. Eng., 10.","DOI":"10.3390\/jmse10111695"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1109\/JOE.2022.3177858","article-title":"A Hyperheuristic Algorithm Based on Evolutionary Strategy for Complex Mission Planning of AUVs in Marine Environment","volume":"47","author":"Wei","year":"2022","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: Trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.neunet.2022.03.037","article-title":"Deep learning, reinforcement learning, and world models","volume":"152","author":"Matsuo","year":"2022","journal-title":"Neural Netw."},{"key":"ref_22","unstructured":"Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. (2013). Playing atari with deep reinforcement learning. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/S1474-6670(17)35089-9","article-title":"A Genetic Algorithm for Autonomous Undetwater Vehicle Route Planning in Ocean Environments with Complex Space-Time Variability","volume":"34","author":"Alvarez","year":"2001","journal-title":"IFAC Proc. Vol."},{"key":"ref_24","unstructured":"Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D., and Riedmiller, M. (2014, January 21\u201326). Deterministic policy gradient algorithms. Proceedings of the International Conference on Machine Learning, Beijing, China."},{"key":"ref_25","unstructured":"Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D. (2015). Continuous control with deep reinforcement learning. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"112226","DOI":"10.1016\/j.oceaneng.2022.112226","article-title":"Path planning for underwater gliders in time-varying ocean current using deep reinforcement learning","volume":"262","author":"Lan","year":"2022","journal-title":"Ocean Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1109\/TIV.2022.3153352","article-title":"Path Planning Based on Deep Reinforcement Learning for Autonomous Underwater Vehicles Under Ocean Current Disturbance","volume":"8","author":"Chu","year":"2022","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"110452","DOI":"10.1016\/j.oceaneng.2021.110452","article-title":"AUV position tracking and trajectory control based on fast-deployed deep reinforcement learning method","volume":"245","author":"Fang","year":"2021","journal-title":"Ocean Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1109\/JIOT.2022.3205685","article-title":"A Time-Saving Path Planning Scheme for Autonomous Underwater Vehicles with Complex Underwater Conditions","volume":"10","author":"Yang","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"113407","DOI":"10.1016\/j.oceaneng.2022.113407","article-title":"Path-following optimal control of autonomous underwater vehicle based on deep reinforcement learning","volume":"268","author":"Wang","year":"2023","journal-title":"Ocean Eng."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bu, F., Luo, H., Ma, S., Li, X., Ruby, R., and Han, G. (2023). AUV-Aided Optical\u2014Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning. Sensors, 23.","DOI":"10.3390\/s23020578"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Fossen, T.I. (2011). Handbook of Marine Craft Hydrodynamics and Motion Control, John Wiley & Sons.","DOI":"10.1002\/9781119994138"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/j.2161-4296.2001.tb00223.x","article-title":"Precision Hybrid Inertial\/Acoustic Navigation System for a Long-Range Autonomous Underwater Vehicle","volume":"48","author":"Butler","year":"2001","journal-title":"Navigation"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.oceaneng.2015.10.007","article-title":"A survey on path planning for persistent autonomy of autonomous underwater vehicles","volume":"110","author":"Zeng","year":"2015","journal-title":"Ocean Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1386","DOI":"10.1109\/TIE.2006.878292","article-title":"Attitude Estimation by Multiple-Mode Kalman Filters","volume":"53","author":"Suh","year":"2006","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.jpowsour.2004.02.031","article-title":"Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1. Background","volume":"134","author":"Plett","year":"2004","journal-title":"J. Power Sources"},{"key":"ref_37","unstructured":"Walder, G., Campestrini, C., Kohlmeier, S., Lienkamp, M., and Jossen, A. (2013, January 8). Functionality and Behaviour of an Dual Kalman Filter implemented on a Modular Battery-Management-System. Proceedings of the Conference on Future Automotive Technology Focus Electromobility (CoFAT), Online."},{"key":"ref_38","unstructured":"Khatib, O. (1985, January 25\u201328). Real-time obstacle avoidance for manipulators and mobile robots. Proceedings of the 1985 IEEE International Conference on Robotics and Automation, St. Louis, MO, USA."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Fu, J., Lv, T., and Li, B. (2022). Underwater Submarine Path Planning Based on Artificial Potential Field Ant Colony Algorithm and Velocity Obstacle Method. Sensors, 22.","DOI":"10.3390\/s22103652"},{"key":"ref_40","first-page":"54","article-title":"3D obstacle-avoidance for a unmanned aerial vehicle based on the improved artificial potential field method","volume":"2022","author":"Zhou","year":"2022","journal-title":"J. East China Norm. Univ. (Nat. Sci.)"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1364\/OL.446277","article-title":"Three-dimensional morphology measurement of underwater objects based on the photoacoustic effect","volume":"47","author":"Ding","year":"2022","journal-title":"Opt. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/MSP.2017.2743240","article-title":"Deep Reinforcement Learning: A Brief Survey","volume":"34","author":"Arulkumaran","year":"2017","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"135426","DOI":"10.1109\/ACCESS.2020.3011438","article-title":"A Survey on Visual Navigation for Artificial Agents with Deep Reinforcement Learning","volume":"8","author":"Zeng","year":"2020","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1820","DOI":"10.1109\/TNNLS.2019.2927227","article-title":"Approximate Policy-Based Accelerated Deep Reinforcement Learning","volume":"31","author":"Wang","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Hu, Z., Wan, K., Gao, X., Zhai, Y., and Wang, Q. (2020). Deep Reinforcement Learning Approach with Multiple Experience Pools for UAV\u2019s Autonomous Motion Planning in Complex Unknown Environments. 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