{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T19:55:59Z","timestamp":1781812559555,"version":"3.54.5"},"reference-count":24,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T00:00:00Z","timestamp":1781740800000},"content-version":"vor","delay-in-days":168,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Cyber-Phy Sys Theory &amp;amp; Ap"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Unmanned aerial vehicles have been widely adopted owing to their compact size, high maneuverability and low cost. Consequently, research on unmanned aerial vehicles guidance, navigation and control systems has emerged as a prominent topic. Path planning constitutes a critical component of guidance, navigation and control systems. To overcome the limitations of existing unmanned aerial vehicles path planning algorithms, including restricted environmental perception, low planning efficiency and diminished learning efficacy\u2014we propose a novel method that integrates the artificial potential field with deep reinforcement learning. We devise a reward strategy that incorporates artificial potential field and path fitting, and construct a hierarchical mechanism that fuses reinforcement learning with classical algorithms. This mechanism effectively optimises the search path, mitigates inefficient exploration and substantially accelerates the path planning process. Finally, experiments conducted in a simulation environment, along with comparative analyses against state\u2010of\u2010the\u2010art techniques, demonstrate the superiority of the proposed method in terms of path planning efficiency.<\/jats:p>","DOI":"10.1049\/cps2.70056","type":"journal-article","created":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T19:30:36Z","timestamp":1781811036000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Potential Field Based Reward Shaping for Efficient UAV Path Planning via Reinforcement Learning"],"prefix":"10.1049","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-9677-9121","authenticated-orcid":false,"given":"Zekai","family":"Liu","sequence":"first","affiliation":[{"name":"Guangdong Guangzhou Power Supply Bureau, China Southern Power Grid Co. Ltd  Guangzhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guangdong Guangzhou Power Supply Bureau, China Southern Power Grid Co. Ltd  Guangzhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Guangdong Guangzhou Power Supply Bureau, China Southern Power Grid Co. Ltd  Guangzhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Xu","sequence":"additional","affiliation":[{"name":"Guangdong Guangzhou Power Supply Bureau, China Southern Power Grid Co. Ltd  Guangzhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Ben","sequence":"additional","affiliation":[{"name":"Guangdong Guangzhou Power Supply Bureau, China Southern Power Grid Co. Ltd  Guangzhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keren","family":"Zhu","sequence":"additional","affiliation":[{"name":"Guangdong Guangzhou Power Supply Bureau, China Southern Power Grid Co. Ltd  Guangzhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"265","published-online":{"date-parts":[[2026,6,18]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12555\u2010010\u20100105\u2010z"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2909530"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1177\/0278364911406761"},{"key":"e_1_2_10_5_1","volume-title":"Small Unmanned Aircraft: Theory and Practice","author":"Beard R.","year":"2012"},{"key":"e_1_2_10_6_1","first-page":"1936","volume-title":"Proceedings of the American Control Conference (ACC)","author":"Richards A.","year":"2002"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/70.508439"},{"key":"e_1_2_10_8_1","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511546877","volume-title":"Planning Algorithms","author":"LaValle S.","year":"2006"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/mcom.2016.7470933"},{"key":"e_1_2_10_10_1","volume-title":"Reinforcement Learning: An Introduction","author":"Sutton R.","year":"2018"},{"key":"e_1_2_10_11_1","volume-title":"Artificial Intelligence: A Modern Approach","author":"Russell S.","year":"2010"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/msp.2017.2743240"},{"key":"e_1_2_10_13_1","volume-title":"Reinforcement Learning and Dynamic Programming Using Function Approximators","author":"Bu\u015foniu L.","year":"2010"},{"key":"e_1_2_10_14_1","volume-title":"Markov Decision Processes: Discrete Stochastic Dynamic Programming","author":"Puterman M.","year":"2014"},{"key":"e_1_2_10_15_1","unstructured":"T.Lillicrap J.Hunt A.Pritzel et\u00a0al. \u201cContinuous Control With Deep Reinforcement Learning \u201d preprint arXiv:1509.02971 (2015)."},{"key":"e_1_2_10_16_1","unstructured":"J.Schulman F.Wolski P.Dhariwal A.Radford andO.Klimov \u201cProximal Policy Optimization Algorithms \u201d preprint arXiv:1707.06347 (2017)."},{"key":"e_1_2_10_17_1","first-page":"649","volume-title":"Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)","author":"Westheider J.","year":"2023"},{"key":"e_1_2_10_18_1","unstructured":"R.Lowe Y.Wu A.Tamar J.Harb P.Abbeel andI.Mordatch \u201cMulti\u2010Agent Actor\u2010Critic for Mixed Cooperative\u2010Competitive Environments \u201d preprint arXiv:1706.02275 (2017)."},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14236"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2019.10.014"},{"key":"e_1_2_10_21_1","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1109\/ISCEIC59030.2023.10271160","volume-title":"2023 4th International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","author":"Wang J.","year":"2023"},{"key":"e_1_2_10_22_1","unstructured":"J.Hare \u201cDealing With Sparse Rewards in Reinforcement Learning \u201d preprint arXiv:1910.09281 (2019)."},{"key":"e_1_2_10_23_1","first-page":"278","volume-title":"Proceedings of the Sixteenth International Conference on Machine Learning (ICML)","author":"Ng A.","year":"1999"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.3390\/drones9030203"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.3390\/s25247463"}],"container-title":["IET Cyber-Physical Systems: Theory &amp; 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