{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T16:17:27Z","timestamp":1772122647778,"version":"3.50.1"},"reference-count":19,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T00:00:00Z","timestamp":1669075200000},"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>This paper studies the cooperative control of multiple unmanned aerial vehicles (UAVs) with sensors and autonomous flight capabilities. In this paper, an architecture is proposed that takes a small quadrotor as a mission UAV and a large six-rotor as a platform UAV to provide an aerial take-off and landing platform and transport carrier for the mission UAV. The design of a tracking controller for an autonomous docking and landing trajectory system is the focus of this research. To examine the system\u2019s overall design, a dual-machine trajectory-tracking control simulation platform is created via MATLAB\/Simulink. Then, an autonomous docking and landing trajectory-tracking controller based on radial basis function proportional\u2013integral\u2013derivative control is designed, which fulfills the trajectory-tracking control requirements of the autonomous docking and landing process by efficiently suppressing the external airflow disturbance according to the simulation results. A YOLOv3-based vision pilot system is designed to calibrate the rate of the aerial docking and landing position to eight frames per second. The feasibility of the multi-rotor aerial autonomous docking and landing technology is verified using prototype flight tests during the day and at night. It lays a technical foundation for UAV transportation, autonomous take-off, landing in the air, and collaborative networking. In addition, compared with the existing technologies, our research completes the closed loop of the technical process through modeling, algorithm design and testing, virtual simulation verification, prototype manufacturing, and flight test, which have better realizability.<\/jats:p>","DOI":"10.3390\/s22239066","type":"journal-article","created":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T03:48:12Z","timestamp":1669175292000},"page":"9066","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Research on Aerial Autonomous Docking and Landing Technology of Dual Multi-Rotor UAV"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8115-1094","authenticated-orcid":false,"given":"Liang","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China"},{"name":"Shanghai Aerospace Systems Engineering Institute, Shanghai 201108, China"}]},{"given":"Xiangqian","family":"Jiang","sequence":"additional","affiliation":[{"name":"Shanghai Aerospace Systems Engineering Institute, Shanghai 201108, China"}]},{"given":"Di","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Aerospace Systems Engineering Institute, Shanghai 201108, China"}]},{"given":"Lisheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Aerospace Systems Engineering Institute, Shanghai 201108, China"}]},{"given":"Zhijun","family":"Tu","sequence":"additional","affiliation":[{"name":"Shanghai Aerospace Systems Engineering Institute, Shanghai 201108, China"}]},{"given":"Jianliang","family":"Ai","sequence":"additional","affiliation":[{"name":"Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,22]]},"reference":[{"key":"ref_1","unstructured":"(2022, October 05). Helicarrier. Available online: https:\/\/en.wikipedia.org\/wiki\/Helicarrier."},{"key":"ref_2","unstructured":"(2022, October 05). Zveno_project. Available online: https:\/\/en.wikipedia.org\/wiki\/Zveno_project."},{"key":"ref_3","unstructured":"(2022, October 05). Curtiss_F9C_Sparrowhawk. Available online: https:\/\/en.wikipedia.org\/wiki\/Curtiss_F9C_Sparrowhawk."},{"key":"ref_4","unstructured":"(2022, October 05). DARPA Wants to Turn Existing Planes into Drone Motherships. Available online: https:\/\/arstechnica.com\/information-technology\/2014\/11\/darpa-wants-to-turn-existing-planes-into-drone-motherships\/."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1016\/j.jfranklin.2021.04.044","article-title":"Distributed Multi-Uav Trajectory Optimization over Directed Networks","volume":"358","author":"Liu","year":"2021","journal-title":"J. Frankl. Inst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.comcom.2020.01.072","article-title":"A Bidirectional Congestion Control Transport Protocol for the Internet of Drones","volume":"153","author":"Sharma","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Nguyen, T.-M., Nguyen, T.H., Cao, M., Qiu, Z., and Xie, L. (2019, January 20\u201324). Integrated Uwb-Vision Approach for Autonomous Docking of Uavs in Gps-Denied Environments. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793851"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, C., Xiang, J., Ye, Z., Yan, W., Wang, S., Wang, Z., Chen, P., and Xiao, M.J.D. (2022). Deep Learning-Based Energy Optimization for Edge Device in Uav-Aided Communications. Drones, 6.","DOI":"10.3390\/drones6060139"},{"key":"ref_9","first-page":"63","article-title":"Control and Estimation of a Quadcopter Dynamical Model","volume":"6","author":"Kurak","year":"2018","journal-title":"Period. Eng. Nat. Sci. (PEN)"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kuantama, E., Craciun, D., and Tarca, R.J.A.U.O. (2016). Quadcopter Body Frame Model and Analysis. Ann. Univ. Oradea, 71\u201374.","DOI":"10.15660\/AUOFMTE.2016-1.3205"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gheorghi\u0163\u0103, D., V\u00eentu, I., Mirea, L., and Br\u0103escu, C. (2015, January 14\u201316). Quadcopter Control System. Proceedings of the 2015 19th International Conference on System Theory, Control and Computing (ICSTCC), Cheile Gradistei, Romania.","DOI":"10.1109\/ICSTCC.2015.7321330"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1109\/72.822511","article-title":"Output Feedback Control of Nonlinear Systems Using Rbf Neural Networks","volume":"11","author":"Seshagiri","year":"2000","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Al-Darraji, I., Piromalis, D., Kakei, A.A., Khan, F.Q., Stojmenovic, M., Tsaramirsis, G., and Papageorgas, P.G.J.E. (2021). Adaptive Robust Controller Design-Based Rbf Neural Network for Aerial Robot Arm Model. Electronics, 10.","DOI":"10.3390\/electronics10070831"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"113036","DOI":"10.1016\/j.cam.2020.113036","article-title":"Adaptive Gaussian Radial Basis Function Methods for Initial Value Problems: Construction and Comparison with Adaptive Multiquadric Radial Basis Function Methods","volume":"381","author":"Gu","year":"2021","journal-title":"J. Comput. Appl. Math."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1080\/00207179.2016.1213423","article-title":"A New Adaptive Control Strategy for a Class of Nonlinear System Using Rbf Neuro-Sliding-Mode Technique: Application to Seig Wind Turbine Control System","volume":"90","author":"Fotso","year":"2017","journal-title":"Int. J. Control"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1109\/TMECH.2019.2962081","article-title":"Rbf-Neural-Network-Based Adaptive Robust Control for Nonlinear Bilateral Teleoperation Manipulators with Uncertainty and Time Delay","volume":"25","author":"Chen","year":"2019","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"012029","DOI":"10.1088\/1742-6596\/1004\/1\/012029","article-title":"Understanding of Object Detection Based on Cnn Family and Yolo","volume":"1004","author":"Du","year":"2018","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"\u0106orovi\u0107, A., Ili\u0107, V., \u00d0uri\u0107, S., Marijan, M., and Pavkovi\u0107, B. (2018, January 20\u201321). The Real-Time Detection of Traffic Participants Using Yolo Algorithm. Proceedings of the 2018 26th Telecommunications Forum (TELFOR), Belgrade, Serbia.","DOI":"10.1109\/TELFOR.2018.8611986"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ammar, A., Koubaa, A., Ahmed, M., and Saad, A. (2019). Aerial Images Processing for Car Detection Using Convolutional Neural Networks: Comparison between Faster R-Cnn and Yolov3. arXiv.","DOI":"10.20944\/preprints201910.0195.v1"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9066\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:24:37Z","timestamp":1760145877000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9066"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,22]]},"references-count":19,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22239066"],"URL":"https:\/\/doi.org\/10.3390\/s22239066","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,22]]}}}