{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T16:16:20Z","timestamp":1785428180034,"version":"3.56.0"},"reference-count":22,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T00:00:00Z","timestamp":1778716800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62402200"],"award-info":[{"award-number":["62402200"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["62402200"],"award-info":[{"award-number":["62402200"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>To address the challenges of high computational complexity, inferior path performance, and the balance between path quality and efficiency in traditional 3D omnidirectional path planning algorithms for UAVs, this study proposes an innovative precision algorithm for solving 3D omnidirectional shortest paths. The algorithm innovatively introduces the concepts of circling path and overpass path, reducing three-dimensional omnidirectional path computation to two-dimensional processing. It designs a three-view obstacle detection algorithm to achieve efficient obstacle avoidance judgment, formulates separate path-solving strategies for discrete and continuous obstacles, respectively, and obtains optimal solutions through recursive adjustments and path optimization. Experimental results demonstrate that compared to A* and Theta* algorithms, our approach achieves shorter path lengths with superior stability; the proposed algorithm achieves a 21.86% reduction compared to RRT*, 10.48% compared to A*, and 0.89% compared to Lazy_Theta*. In addition, the proposed algorithm exhibits enhanced adaptability in high-obstacle environments (particularly irregular obstacles). These findings provide an effective solution for complex spatial path planning in UAV applications.<\/jats:p>","DOI":"10.3390\/a19050393","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T16:27:56Z","timestamp":1778776076000},"page":"393","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Three-Dimensional UAV Omnidirectional Path Planning Algorithm Based on Urban Obstacle Environment"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1594-1789","authenticated-orcid":false,"given":"Yijie","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214200, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jizhou","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214200, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7426913","DOI":"10.1155\/2016\/7426913","article-title":"Survey of robot 3D path planning algorithms","volume":"2016","author":"Yang","year":"2016","journal-title":"J. Control Sci. Eng."},{"key":"ref_2","first-page":"85","article-title":"Any-angle path planning","volume":"34","author":"Nash","year":"2013","journal-title":"AI Mag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Chen, J. (2025). The True Shortest Path of Obstacle Grid Graph Is Solved by SGP Vertex Extraction and Filtering Algorithm. Algorithms, 18.","DOI":"10.3390\/a18070400"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hamandi, M., Ali, A.M., Tzes, A., and Khorrami, F. (2025). Experimental Evaluation of Safe Trajectory Planning for an Omnidirectional UAV. 2025 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE.","DOI":"10.1109\/IROS60139.2025.11246251"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, G., Chen, Z., Wang, J., and Huang, P. (2026). A Cooperative Keypoint\u2013Sparse Cache and Improved PPO Framework for Rapid 3D UAV Path Planning. Drones, 10.","DOI":"10.3390\/drones10050330"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Cheriet, H., Badra, K.K., and Samira, C. (2024). Comparative analysis of UAV path planning algorithms for efficient navigation in urban 3D environments. 2024 International Conference of the African Federation of Operational Research Societies (AFROS), IEEE.","DOI":"10.1109\/AFROS62115.2024.11037069"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yakovlev, K., Andreychuk, A., and Stern, R. (2024). Optimal and bounded suboptimal any-angle multi-agent pathfinding. 2024 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE.","DOI":"10.1109\/IROS58592.2024.10801691"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Niu, Q., Fu, Y., and Dong, X. (2024). Omnidirectional AGV Path Planning Based on Improved Genetic Algorithm. World Electr. Veh. J., 15.","DOI":"10.3390\/wevj15040166"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yew, L.J., Safar, M.J.A., Basaruddin, K.S., Som, M.H.M., and Hassan, M.K.A. (2024). Shortest Path Planning for Rectangular Holonomic Omnidirectional Mobile Robot Using Improved PRM Algorithm. International Conference on Sustainability and Emerging Technologies for Smart Manufacturing, Springer Nature.","DOI":"10.1007\/978-981-97-7083-0_69"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, L., Li, Y., Yu, Y., and Retscher, G. (2026). A UAV Path-Planning Method Based on Multi-Mechanism Improved Dung Beetle Optimizer Algorithm in Complex Constrained Environments. Symmetry, 18.","DOI":"10.3390\/sym18020383"},{"key":"ref_11","first-page":"1177","article-title":"Theta*: Any-angle path planning on grids","volume":"7","author":"Nash","year":"2007","journal-title":"AAAI"},{"key":"ref_12","first-page":"147","article-title":"Lazy Theta*: Any-angle path planning and path length analysis in 3D","volume":"24","author":"Nash","year":"2010","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_13","first-page":"253","article-title":"Strict Theta*: Shorter motion path planning using taut paths","volume":"26","author":"Oh","year":"2016","journal-title":"Proc. Int. Conf. Autom. Plan. Sched."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Reijgwart, V., Cadena, C., Siegwart, R., and Ott, L. (2026). Efficient hierarchical any-angle path planning on multi-resolution 3D grids. arXiv.","DOI":"10.15607\/RSS.2025.XXI.049"},{"key":"ref_15","unstructured":"LaValle, S.M., and Kuffner, J.J. (2001). Rapidly-exploring random trees: Progress and prospects. Algorithmic and Computational Robotics, A K Peters\/CRC Press."},{"key":"ref_16","first-page":"20","article-title":"A comparison of RRT, RRT* and RRT*-smart path planning algorithms","volume":"16","author":"Noreen","year":"2016","journal-title":"Int. J. Comput. Sci. Netw. Secur."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Islam, F., Nasir, J., Malik, U., Ayaz, Y., and Hasan, O. (2012). Rrt\u2217-smart: Rapid convergence implementation of rrt\u2217 towards optimal solution. 2012 IEEE International Conference on Mechatronics and Automation, IEEE.","DOI":"10.5772\/56718"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gammell, J.D., Srinivasa, S.S., and Barfoot, T.D. (2014). Informed RRT*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic. 2014 IEEE\/RSJ International Conference on Intelligent Robots and Systems, IEEE.","DOI":"10.1109\/IROS.2014.6942976"},{"key":"ref_19","unstructured":"Colorni, A., Dorigo, M., and Maniezzo, V. (1992). An Investigation of some Properties of an \u201cAnt Algorithm\u201d. Proceedings of the Parallel Problem Solving from Nature Conference (PPSN 92), Brussels, Belgium, 28\u201330 September 1992, Elsevier Publishing."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"113528","DOI":"10.1016\/j.knosys.2025.113528","article-title":"An efficient grid-based path planning approach using improved artificial bee colony algorithm","volume":"318","author":"Yildirim","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/TSSC.1968.300136","article-title":"A formal basis for the heuristic determination of minimum cost paths","volume":"4","author":"Hart","year":"1968","journal-title":"IEEE Trans. Syst. Sci. Cybern."},{"key":"ref_22","first-page":"267","article-title":"Incremental sampling-based algorithms for optimal motion planning","volume":"104","author":"Karaman","year":"2010","journal-title":"Robot. Sci. Syst. 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