{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T23:36:38Z","timestamp":1783121798890,"version":"3.54.6"},"reference-count":67,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T00:00:00Z","timestamp":1757030400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Tongling University Talent Research Start-up Fund Project","award":["2024tlxyrc019"],"award-info":[{"award-number":["2024tlxyrc019"]}]},{"name":"Tongling University Talent Research Start-up Fund Project","award":["2024AH053415"],"award-info":[{"award-number":["2024AH053415"]}]},{"name":"Anhui Provincial KeyResearch and Development Project","award":["2024tlxyrc019"],"award-info":[{"award-number":["2024tlxyrc019"]}]},{"name":"Anhui Provincial KeyResearch and Development Project","award":["2024AH053415"],"award-info":[{"award-number":["2024AH053415"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Three-dimensional path planning is critical for the successful operation of unmanned aerial vehicles (UAVs), automated guided vehicles (AGVs), and robots in industrial Internet of Things (IIoT) applications. In 3D path planning, the standard Particle Swarm Optimization (PSO) algorithm suffers from premature convergence and a tendency to fall into local optima, leading to significant deviations from the optimal path. This paper proposes an improved PSO (IPSO) algorithm that enhances particle diversity and randomness through the introduction of logistic chaotic mapping, while employing dynamic learning factors and nonlinear inertia weights to improve global search capability. Experimental results demonstrate that IPSO outperforms traditional methods in terms of path length and computational efficiency, showing potential for real-time path planning in complex environments.<\/jats:p>","DOI":"10.3390\/fi17090406","type":"journal-article","created":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T14:53:01Z","timestamp":1757083981000},"page":"406","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["3D Spatial Path Planning Based on Improved Particle Swarm Optimization"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3373-7394","authenticated-orcid":false,"given":"Junxia","family":"Ma","sequence":"first","affiliation":[{"name":"College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zixu","family":"Yang","sequence":"additional","affiliation":[{"name":"Anhui Houpu Digital Technology Co., Ltd., Hefei 230000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science, Tongling University, Tongling 244061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Urrea, C., and Kern, J. (2025). Recent Advances and Challenges in Industrial Robotics: A Systematic Review of Technological Trends and Emerging Applications. Processes, 13.","DOI":"10.3390\/pr13030832"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhu, X., Chen, T., and Dou, G. (2025). Optimal Dynamics Control in Trajectory Tracking of Industrial Robots Based on Adaptive Gaussian Pseudo-Spectral Algorithm. Algorithms, 18.","DOI":"10.3390\/a18010018"},{"key":"ref_3","first-page":"1470","article-title":"Kinematic Calibration of Industrial Robots Based on Binocular Vision Distance Error Measurement","volume":"45","author":"Jiang","year":"2024","journal-title":"Jiliang Xuebao\/Acta Metrol. Sin."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Igwenagu, U.T.I., Debnath, R., Ahmed, A.A., and Alam, M.J.B. (2025). An Integrated Approach for Earth Infrastructure Monitoring Using UAV and ERI: A Systematic Review. Drones, 9.","DOI":"10.3390\/drones9030225"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"109577","DOI":"10.1016\/j.anucene.2022.109577","article-title":"Development of fault diagnosis for nuclear power plant using deep learning and infrared sensor equipped UAV","volume":"181","author":"Jin","year":"2023","journal-title":"Ann. Nucl. Energy"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Karegar, P.A., Al-Hamid, D.Z., and Chong, P.H.J. (2024). Deep Reinforcement Learning for UAV-Based SDWSN Data Collection. Future Internet, 16.","DOI":"10.3390\/fi16110398"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jiang, C., Yang, L., Gao, Y., Zhao, J., Hou, W., and Xu, F. (2025). An Intelligent 5G Unmanned Aerial Vehicle Path Optimization Algorithm for Offshore Wind Farm Inspection. Drones, 9.","DOI":"10.3390\/drones9010047"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Li, Y., and Liu, L. (2024). Research on AGV Path Planning Based on Improved Directed Weighted Graph Theory and ROS Fusion. Actuators, 13.","DOI":"10.3390\/act13100404"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Vaccari, L., Coruzzolo, A.M., Lolli, F., and Sellitto, M.A. (2024). Indoor Positioning Systems in Logistics: A Review. Logistics, 8.","DOI":"10.3390\/logistics8040126"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e8064","DOI":"10.1002\/cpe.8064","article-title":"Multi objective optimization scheduling of unmanned warehouse handling robots based on A star algorithm","volume":"36","author":"Li","year":"2024","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Han, L., Ding, J., Liu, S., and Meng, M. (2025). The Path Planning Problem of Robotic Delivery in Multi-Floor Hotel Environments. Sensors, 25.","DOI":"10.3390\/s25061783"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gon\u00e7alves, A., Pereira, T., Lopes, D., Cunha, F., Lopes, F., Coutinho, F., Barreiros, J., Dur\u00e3es, J., Santos, P., and Sim\u00f5es, F. (2025). Enhancing Nut-Tightening Processes in the Automotive Industry: Integration of 3D Vision Systems with Collaborative Robots. Automation, 6.","DOI":"10.3390\/automation6010008"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Li, H., Duan, J., Qin, J., and Zhou, Y. (2023). Multi-Objective Point Motion Planning for Assembly Robotic Arm Based on IPQ-RRT* Connect Algorithm. Actuators, 12.","DOI":"10.3390\/act12120459"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, Y., Feng, Q., Sun, J., Peng, C., Gao, L., and Chen, L. (2025). Compliant Motion Planning Integrating Human Skill for Robotic Arm Collecting Tomato Bunch Based on Improved DDPG. Plants, 14.","DOI":"10.3390\/plants14050634"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Merei, A., Mcheick, H., Ghaddar, A., and Rebaine, D. (2025). A Survey on Obstacle Detection and Avoidance Methods for UAVs. Drones, 9.","DOI":"10.3390\/drones9030203"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Choi, Y., and Kim, H. (2025). Obstacle-Aware Crowd Surveillance with Mobile Robots in Transportation Stations. Sensors, 25.","DOI":"10.3390\/s25020350"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mohammed, H., Ibrahim, M., Raoof, A., Jaleel, A., and Al-Dujaili, A.Q. (2025). Modified Ant Colony Optimization to Improve Energy Consumption of Cruiser Boundary Tour with Internet of Underwater Things. Computers, 14.","DOI":"10.3390\/computers14020074"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, L., Yu, S., Li, M., and Wei, X. (2024). Multi-Task Agent Hybrid Control in Sparse Maps and Complex Environmental Conditions. Appl. Sci., 14.","DOI":"10.21203\/rs.3.rs-4255412\/v1"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1080\/02533839.2023.2227879","article-title":"A formation cooperative reconnaissance strategy for multi-UGVs in partially unknown environment","volume":"46","author":"Zhang","year":"2023","journal-title":"J. Chin. Inst. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez-Morgado, A., Ollero, A., and Heredia, G. (2024). Control Barrier Functions in Multirotors: A Safety Filter for Obstacle Avoidance, Springer.","DOI":"10.1007\/978-3-031-59167-9_2"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Jayaweera, H.M.P.C., and Hanoun, S. (2021). UAV Path Planning for Reconnaissance and Look-Ahead Coverage Support for Mobile Ground Vehicles. Sensors, 21.","DOI":"10.3390\/s21134595"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Adam, M.S., Abdullah, N.F., Abu-Samah, A., Amodu, O.A., and Nordin, R. (2025). Advanced Path Planning for UAV Swarms in Smart City Disaster Scenarios Using Hybrid Metaheuristic Algorithms. Drones, 9.","DOI":"10.3390\/drones9010064"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yuan, D., Zhong, Y., Zhu, X., Chen, Y., Jin, Y., Du, X., Tang, K., and Huang, T. (2025). Trajectory Planning for Unmanned Vehicles on Airport Apron Under Aircraft\u2013Vehicle\u2013Airfield Collaboration. Sensors, 25.","DOI":"10.3390\/s25010071"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Mungu\u00eda, A., Guerra-\u00c1vila, P.L., Islas-Ojeda, E., Flores-S\u00e1nchez, J.L., V\u00e1zquez-Mart\u00ednez, O., Garc\u00eda-Mungu\u00eda, A.M., and Garc\u00eda-Mungu\u00eda, O. (2024). A Review of Drone Technology and Operation Processes in Agricultural Crop Spraying. Drones, 8.","DOI":"10.3390\/drones8110674"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, Z., and Li, G. (2024). Research on Path Planning Algorithm of Driverless Ferry Vehicles Combining Improved A* and DWA. Sensors, 24.","DOI":"10.3390\/s24134041"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Suanpang, P., and Jamjuntr, P. (2024). Optimizing Autonomous UAV Navigation with D* Algorithm for Sustainable Development. Sustainability, 16.","DOI":"10.3390\/su16177867"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Meng, X., and Fang, X. (2024). A UGV Path Planning Algorithm Based on Improved A* with Improved Artificial Potential Field. Electronics, 13.","DOI":"10.3390\/electronics13050972"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Noroozi, F., Daneshmand, M., and Fiorini, P. (2023). Conventional, Heuristic and Learning-Based Robot Motion Planning: Reviewing Frameworks of Current Practical Significance. Machines, 11.","DOI":"10.3390\/machines11070722"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Qin, H., Shao, S., Wang, T., Yu, X., Jiang, Y., and Cao, Z. (2023). Review of Autonomous Path Planning Algorithms for Mobile Robots. Drones, 7.","DOI":"10.3390\/drones7030211"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tang, Y., Zakaria, M.A., and Younas, M. (2025). Path Planning Trends for Autonomous Mobile Robot Navigation: A Review. Sensors, 25.","DOI":"10.3390\/s25041206"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fu, S., Yang, D., Mei, Z., and Zheng, W. (2025). Progress in Construction Robot Path-Planning Algorithms: Review. Appl. Sci., 15.","DOI":"10.3390\/app15031165"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5515","DOI":"10.1007\/s00170-023-11503-0","article-title":"Performance evaluation of extruded polystyrene foam for aerospace engineering applications using frequency analyses","volume":"126","author":"Karpenko","year":"2023","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"593","DOI":"10.15632\/jtam-pl\/152970","article-title":"Vibration damping characteristics of the cork-based composite material in line to frequency analysis","volume":"60","author":"Karpenko","year":"2022","journal-title":"J. Theor. Appl. Mech."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Li, Y., Cheng, J., Li, C., and Hu, S. (2025). Two-Stage Hierarchical 4D Low-Risk Trajectory Planning for Urban Air Logistics. Drones, 9.","DOI":"10.3390\/drones9040267"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ji, Y., Liu, Q., Zhou, C., Han, Z., and Wu, W. (2025). Hybrid Swarm Intelligence and Human-Inspired Optimization for Urban Drone Path Planning. Biomimetics, 10.","DOI":"10.3390\/biomimetics10030180"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhou, X., Shi, G., and Zhang, J. (2024). Improved Grey Wolf Algorithm: A Method for UAV Path Planning. Drones, 8.","DOI":"10.3390\/drones8110675"},{"key":"ref_37","first-page":"16","article-title":"Flight path planning for urban logistics UAV based on improved A*-APF algorithm","volume":"40","author":"Liu","year":"2022","journal-title":"Flight Dyn."},{"key":"ref_38","first-page":"105","article-title":"Path Planning of Robot Combing Safety A* Algorithm and Dynamic Window Approach","volume":"48","author":"Zhan","year":"2022","journal-title":"Comput. Eng."},{"key":"ref_39","first-page":"245","article-title":"Flexible Needle Rrt Path Planning Algorithm Based On Cost Function Optimization","volume":"39","author":"Cai","year":"2022","journal-title":"Comput. Appl. Softw."},{"key":"ref_40","first-page":"40","article-title":"Narrow Channel Path Planning of Intelligent Inspection Robot Based on Improved RRT","volume":"10","author":"Chen","year":"2022","journal-title":"Modul. Mach. Tool Autom. Manuf. Tech."},{"key":"ref_41","first-page":"51","article-title":"Dynamic path planning based on neural network improved particle swarm algorithm","volume":"49","author":"Chen","year":"2021","journal-title":"J. Huazhong Univ. Sci. Technol. (Nat. Sci. Ed.)"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"012039","DOI":"10.1088\/1757-899X\/1172\/1\/012039","article-title":"Obstacle avoidance for multi-UAV path planning based on particle swarm optimization","volume":"1172","author":"Mobarez","year":"2021","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"48382","DOI":"10.1109\/ACCESS.2019.2907126","article-title":"Disturbance observer based on biologically inspired integral sliding Mode control for trajectory tracking of mobile robots","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_44","first-page":"39","article-title":"Robot path planning based on improved ant colony algorithm","volume":"35","author":"Li","year":"2020","journal-title":"Autom. Instrum."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Fu, Y., Yang, S., Liu, B., Xia, E., and Huang, D. (2023). Multi-UAV Cooperative Trajectory Planning Based on the Modified Cheetah Optimization Algorithm. Entropy, 25.","DOI":"10.3390\/e25091277"},{"key":"ref_46","first-page":"116","article-title":"Adaptive guided ant colony algorithm to optimize the path planning of mobile robots","volume":"37","author":"Wang","year":"2020","journal-title":"Appl. Res. Comput."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ma, J., Liu, Q., Yang, Z., and Wang, B. (2025). Improved Trimming Ant Colony Optimization Algorithm for Mobile Robot Path Planning. Algorithms, 18.","DOI":"10.3390\/a18050240"},{"key":"ref_48","first-page":"257","article-title":"Autonomous flight path planning for UAVs based on improved artificial fish swarm optimization with ant colony algorithm","volume":"43","author":"Ma","year":"2022","journal-title":"J. Arms Equip. Eng."},{"key":"ref_49","first-page":"43","article-title":"Multi-target UAV path planning based on improved NSGA-II algorithm","volume":"47","author":"Fan","year":"2022","journal-title":"Firepower Command Control"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"18382","DOI":"10.1109\/ACCESS.2017.2746752","article-title":"Collision avoidance for cooperative UAVs with optimized artificial potential field algorithm","volume":"5","author":"Sun","year":"2017","journal-title":"IEEE Access"},{"key":"ref_51","first-page":"86","article-title":"Three-dimensional path planning based on improved particle swarm algorithm","volume":"28","author":"Fu","year":"2021","journal-title":"Electro-Opt. Control"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"110776","DOI":"10.1016\/j.asoc.2023.110776","article-title":"A novel hybrid Coyote\u2013Particle Swarm Optimization Algorithm for three-dimensional constrained trajectory planning of Unmanned Aerial Vehicle","volume":"147","author":"Gupta","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Meng, Q., Chen, K., and Qu, Q. (2024). PPSwarm: Multi-UAV Path Planning Based on Hybrid PSO in Complex Scenarios. Drones, 8.","DOI":"10.3390\/drones8050192"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Rosas-Carrillo, A.S., Sol\u00eds-Santom\u00e9, A., Silva-S\u00e1nchez, C., and Camacho-Nieto, O. (2025). UAV Path Planning Using an Adaptive Strategy for the Particle Swarm Optimization Algorithm. Drones, 9.","DOI":"10.3390\/drones9030170"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Qi, Z., Shao, Z., Ping, Y.S., Hiot, L.M., and Leong, Y.K. (2010, January 26\u201328). An improved heuristic algorithm for uav path planning in 3d environment. Proceedings of the 2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics, Nanjing, China.","DOI":"10.1109\/IHMSC.2010.165"},{"key":"ref_56","first-page":"390","article-title":"Unmanned aerial vehicle path planning based on improved genetic algorithm","volume":"41","author":"Huang","year":"2021","journal-title":"J. Comput. Appl."},{"key":"ref_57","first-page":"61","article-title":"Research on Mine Rescue UAV Path Planning Based on an Improved Artificial Jellyfish Search Algorithm","volume":"51","author":"Zheng","year":"2025","journal-title":"Ind. Mine Autom."},{"key":"ref_58","first-page":"610","article-title":"Research on RRT* Path Planning Algorithm Based on Uniformly Distributed Logistic Chaotic Sequence","volume":"41","author":"Ma","year":"2022","journal-title":"Mech. Sci. Technol. Aerosp. Eng."},{"key":"ref_59","first-page":"117","article-title":"Research on 3D Path Planning Based on an Improved Particle Swarm Optimization Algorithm","volume":"55","author":"Yang","year":"2019","journal-title":"Comput. Eng. Appl."},{"key":"ref_60","first-page":"173","article-title":"Research on cable path planning method based on improved PSO algorithm","volume":"51","author":"Qu","year":"2023","journal-title":"Mach. Tool Hydraul."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2430","DOI":"10.1016\/j.apm.2012.05.032","article-title":"Some geometric aggregation operators based on interval intuitionistic uncertain linguistic variables and their application to group decision making","volume":"37","author":"Liu","year":"2013","journal-title":"Appl. Math. Model."},{"key":"ref_62","first-page":"13","article-title":"Particle Swarm Optimization Algorithm with Nonlinear Inertia Weight and t-Distribution Disturbance","volume":"45","author":"Zhang","year":"2025","journal-title":"J. Hangzhou Dianzi Univ. (Nat. Sci.)"},{"key":"ref_63","first-page":"1759","article-title":"Research on Robot Path Planning Optimization Based on Particle Swarm Optimization Algorithm","volume":"41","author":"Wu","year":"2022","journal-title":"Mech. Sci. Technol. Aerosp. Eng."},{"key":"ref_64","first-page":"4433","article-title":"3D path planning for unmanned aerial vehicle in complex landscape based on improved artificial fish swarm algorithm","volume":"23","author":"Zhang","year":"2023","journal-title":"Sci. Technol. Eng."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1109\/TII.2012.2198665","article-title":"Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning","volume":"9","author":"Roberge","year":"2013","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_66","first-page":"397","article-title":"Path planning in disaster scenarios based on improved artificial bee colony algorithm","volume":"40","author":"Zhu","year":"2023","journal-title":"J. Univ. Chin. Acad. Sci."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"116288","DOI":"10.1016\/j.seppur.2019.116288","article-title":"Optimization and heat integration of hybrid R-HIDiC and pervaporation by combining GA and PSO algorithm in TAME synthesis","volume":"236","author":"Babaie","year":"2020","journal-title":"Sep. Purif. Technol."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/9\/406\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:40:58Z","timestamp":1760035258000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/9\/406"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,5]]},"references-count":67,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["fi17090406"],"URL":"https:\/\/doi.org\/10.3390\/fi17090406","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,5]]}}}