{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T06:26:26Z","timestamp":1766298386531,"version":"build-2065373602"},"reference-count":21,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T00:00:00Z","timestamp":1646870400000},"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>Considering that the actual operating environment of UAV is complex and easily disturbed by the space environment of urban buildings, the RoutE Planning Algorithm of Resilience Enhancement (REPARE) for UAV 3D route planning based on the A* algorithm and artificial potential fields algorithm is carried out in a targeted manner. First of all, in order to ensure the safety of the UAV design, we focus on the capabilities of the UAV body and build a risk identification, assessment, and modeling method such that the mission control parameters of the UAV can be determined. Then, the three-dimensional route planning algorithm based on the artificial potential fields algorithm is used to ensure the safe operation of the UAV online and in real time. At the same time, by adjusting the discriminant coefficient of potential risks in real time to deal with time-varying random disturbance encountered by the UAV, the resilience of the UAV 3D flight route planning can be improved. Finally, the effectiveness of the algorithm is verified by the simulation. The simulation results show that the REPARE algorithm can effectively solve the traditional route planning algorithm\u2019s insufficiency in anti-disturbance. It is safer than a traditional A* route planning algorithm, and its running time is shorter than that of the traditional artificial potential field route planning algorithm. It solves the problems of local optimization, enhances the UAV\u2019s ability to tolerate general uncertain disturbances, and eventually improves resilience of the system.<\/jats:p>","DOI":"10.3390\/s22062151","type":"journal-article","created":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T20:19:10Z","timestamp":1646943550000},"page":"2151","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["An Anti-Disturbance Resilience Enhanced Algorithm for UAV 3D Route Planning"],"prefix":"10.3390","volume":"22","author":[{"given":"Zhining","family":"Xu","sequence":"first","affiliation":[{"name":"National Key Laboratory of Science and Technology on Information System Security, Systems Engineering Institute, Academy of Military Science, Beijing 100141, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1633-4051","authenticated-orcid":false,"given":"Long","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Information System Security, Systems Engineering Institute, Academy of Military Science, Beijing 100141, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6990-847X","authenticated-orcid":false,"given":"Xiaoshan","family":"Ma","sequence":"additional","affiliation":[{"name":"Postgraduate College, Air Force Engineering University, Xi\u2019an 710043, China"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Information System Security, Systems Engineering Institute, Academy of Military Science, Beijing 100141, China"}]},{"given":"Lin","family":"Yang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Information System Security, Systems Engineering Institute, Academy of Military Science, Beijing 100141, China"}]},{"given":"Feng","family":"Yang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Information System Security, Systems Engineering Institute, Academy of Military Science, Beijing 100141, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xu, Z., Lin, Y., Yang, F., Zhang, L., Ma, X., and Liu, Y. (2022, January 4). A 3D Flight Route Planning Algorithm for UAV Based on the Idea of Resilience. Proceedings of the 2021 10th International Conference on Computing and Pattern Recognition, Online.","DOI":"10.1145\/3497623.3497668"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1142\/S2301385020500181","article-title":"Swarm and Multi-agent Time-based A* Path Planning for LTA3 Systems","volume":"8","author":"Gibson","year":"2020","journal-title":"Unmanned Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"108709","DOI":"10.1016\/j.oceaneng.2021.108709","article-title":"The hybrid path planning algorithm based on improved A* and artificial potential field for unmanned surface vehicle formations","volume":"223","author":"Sang","year":"2021","journal-title":"Ocean Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8959","DOI":"10.1109\/TVT.2020.2998137","article-title":"Ant-Colony-Based Complete-Coverage Path-Planning Algorithm for Underwater Gliders in Ocean Areas with Thermoclines","volume":"69","author":"Han","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8557","DOI":"10.1109\/TIE.2018.2886798","article-title":"New Robot Navigation Algorithm Based on a Double-Layer Ant Algorithm and Trajectory Optimization","volume":"66","author":"Yang","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_6","unstructured":"Perumal, N., Elamvazuthi, I., Tageldeen, M.K., Ahamed Khan, M.K.A., and Parasuraman, S. (2016, January 25\u201327). Mobile robot path planning using Ant Colony Optimization. Proceedings of the 2016 2nd IEEE International Symposium on Robotics and Manufacturing Automation (ROMA), Ipoh, Malaysia."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"107230","DOI":"10.1016\/j.cie.2021.107230","article-title":"Path planning optimization of indoor mobile robot based on adaptive ant colony algorithm","volume":"156","author":"Miao","year":"2021","journal-title":"Comput. Ind. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Huo, L., Zhu, J., Wu, G., and Li, Z. (2020). A novel simulated annealing based strategy for balanced UAV task assignment and path planning. Sensors, 20.","DOI":"10.3390\/s20174769"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Daryanavard, H., and Harifi, A. (2019, January 17\u201318). UAV Path Planning for Data Gathering of IoT Nodes: Ant Colony or Simulated Annealing Optimization. Proceedings of the 2019 3rd International Conference on Internet of Things and Applications (IoT), Isfahan, Iran.","DOI":"10.1109\/IICITA.2019.8808834"},{"key":"ref_10","unstructured":"Fu, W., and Yin, J. (2018, January 28\u201330). A Hybrid Path Planning Algorithm Based on Simulated Annealing Particle Swarm for The Self-driving Car. Proceedings of the 2018 International Computers, Signals and Systems Conference (ICOMSSC), Dalian, China."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.eswa.2015.12.047","article-title":"Mix-opt: A new route operator for optimal coverage path planning for a fleet in an agricultural environment","volume":"54","author":"Pajares","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106857","DOI":"10.1016\/j.asoc.2020.106857","article-title":"A knee-guided differential evolution algorithm for unmanned aerial vehicle path planning in disaster management","volume":"98","author":"Yu","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1162\/EVCO_r_00180","article-title":"Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review","volume":"25","author":"Bonyadi","year":"2017","journal-title":"Evol. Comput."},{"key":"ref_14","unstructured":"Arjoune, Y., Ghazi, H.E., Kaabouch, N., and El Majd, B.A. (2018, January 8\u201310). A particle swarm optimization based algorithm for primary user emulation attack detection. Proceedings of the IEEE Computing & Communication Workshop & Conference, Las Vegas, NV, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"109298","DOI":"10.1016\/j.oceaneng.2021.109298","article-title":"An application-orientated anti-collision path planning algorithm for unmanned surface vehicles","volume":"235","author":"Ni","year":"2021","journal-title":"Ocean Eng."},{"key":"ref_16","unstructured":"Liu, L., Shi, R., Li, S., and Wu, J. (2016, January 12\u201314). Path planning for UAVS based on improved artificial potential field method through changing the repulsive potential function. Proceedings of the Guidance, Navigation & Control Conference, Nanjing, China."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1016\/j.ijleo.2017.12.169","article-title":"Tangent navigated robot path planning strategy using particle swarm optimized artificial potential field","volume":"158","author":"Zhou","year":"2018","journal-title":"Optik"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1166","DOI":"10.1049\/el.2018.5018","article-title":"Artificial potential field algorithm for path control of unmanned ground vehicles formation in highway","volume":"54","author":"Wang","year":"2018","journal-title":"Electron. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bounini, F., Gingras, D., Pollart, H., and Grurer, D. (2017, January 11\u201314). Modified Artificial Potential Field Method for Online Path Planning Applications. Proceedings of the Intelligent Vehicles Symposium, Los Angeles, CA, USA.","DOI":"10.1109\/IVS.2017.7995717"},{"key":"ref_20","unstructured":"Mitra, S., Ming, Z., Seifert, N., Mak, T.M., and Kim, K.S. (June, January 30). Built-In Soft Error Resilience for Robust System Design. Proceedings of the Integrated Circuit Design and Technology, Austin, TX, USA."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.ress.2015.08.006","article-title":"A review of definitions and measures of system resilience","volume":"145","author":"Hosseini","year":"2016","journal-title":"Reliab. Eng. Syst. Saf."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2151\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:34:13Z","timestamp":1760135653000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2151"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,10]]},"references-count":21,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22062151"],"URL":"https:\/\/doi.org\/10.3390\/s22062151","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,3,10]]}}}