{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T23:54:15Z","timestamp":1791849255782,"version":"4.3.4"},"reference-count":30,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T00:00:00Z","timestamp":1670544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>This paper takes autonomous exploration in unknown environments on a small co-axial twin-rotor unmanned aerial vehicle (UAV) platform as the task. The study of the fully autonomous positioning in unknown environments and navigation system without global navigation satellite system (GNSS) and other auxiliary positioning means is carried out. Algorithms that are based on the machine vision\/proximity detection\/inertial measurement unit, namely the combined navigation algorithm and indoor simultaneous location and mapping (SLAM) algorithm, are not only designed theoretically but also realized and verified in real surroundings. Additionally, obstacle detection, the decision-making of avoidance motion and motion planning methods such as Octree are also proposed, which are characterized by randomness and symmetry. The demonstration of the positioning and navigation system in the unknown environment and the verification of the indoor obstacle-avoidance flight were both completed through building an autonomous navigation and obstacle avoidance simulation system.<\/jats:p>","DOI":"10.3390\/sym14122608","type":"journal-article","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T03:23:49Z","timestamp":1670556229000},"page":"2608","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Autonomous Navigation and Obstacle Avoidance for Small VTOL UAV in Unknown Environments"],"prefix":"10.3390","volume":"14","author":[{"given":"Cheng","family":"Chen","sequence":"first","affiliation":[{"name":"School of Aerospace Engineering, Shenyang Aerospace University, Shenyang 110000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3906-2180","authenticated-orcid":false,"given":"Zian","family":"Wang","sequence":"additional","affiliation":[{"name":"China Academy of Launch Vehicle Technology, Beijing 100076, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Gong","sequence":"additional","affiliation":[{"name":"Department of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengcheng","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3130-6497","authenticated-orcid":false,"given":"Chengxi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Control for Light Industry Processes, Ministry of Education, School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, Z., Lu, Y., Shi, Y., Wang, Z., Qiao, W., and Liu, Y. (2019). A Dyna-Q-based solution for UAV networks against smart jamming attacks. Symmetry, 11.","DOI":"10.3390\/sym11050617"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kuriki, Y., and Namerikawa, T. (2014, January 4\u20136). Consensus-based cooperative formation control with collision avoidance for a multi-UAV system. Proceedings of the 2014 American Control Conference, Portland, OR, USA.","DOI":"10.1109\/ACC.2014.6858777"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kwak, J., Park, J.H., and Sung, Y. (2016). Unmanned aerial vehicle flight point classification algorithm based on symmetric big data. Symmetry, 9.","DOI":"10.3390\/sym9010001"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.actaastro.2021.12.030","article-title":"Autonomous navigation for deep space small satellites: Scientific and technological advances","volume":"193","author":"Turan","year":"2022","journal-title":"Acta Astronaut."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"104112","DOI":"10.1016\/j.autcon.2021.104112","article-title":"Tag-based visual-inertial localization of unmanned aerial vehicles in indoor construction environments using an on-manifold extended Kalman filter","volume":"135","author":"Kayhani","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, Z., and Zhang, Y. (2022). Constrained ESKF for UAV Positioning in Indoor Corridor Environment Based on IMU and WiFi. Sensors, 22.","DOI":"10.3390\/s22010391"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Aldao, E., Gonz\u00e1lez-de Santos, L.M., and Gonz\u00e1lez-Jorge, H. (2022). LiDAR Based Detect and Avoid System for UAV Navigation in UAM Corridors. Drones, 6.","DOI":"10.3390\/drones6080185"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"289606","DOI":"10.1007\/s11433-022-1922-3","article-title":"Autonomous navigation method and technology implementation of high-precision solar spectral velocity measurement","volume":"65","author":"Zhang","year":"2022","journal-title":"Sci. China Phys. Mech. Astron."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ramezani Dooraki, A., and Lee, D.J. (2022). A Multi-Objective Reinforcement Learning Based Controller for Autonomous Navigation in Challenging Environments. Machines, 10.","DOI":"10.3390\/machines10070500"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s10846-022-01623-2","article-title":"Advanced Visual Predictive Control Scheme for the Navigation Problem","volume":"105","author":"Cadenat","year":"2022","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1515\/pomr-2017-0088","article-title":"Application of an autonomous\/unmanned survey vessel (ASV\/USV) in bathymetric measurements","volume":"nr 3","author":"Specht","year":"2017","journal-title":"Pol. Marit. Res."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xie, S., Wu, P., Peng, Y., Luo, J., Qu, D., Li, Q., and Gu, J. (2014, January 28\u201330). The obstacle avoidance planning of USV based on improved artificial potential field. Proceedings of the 2014 IEEE International Conference on Information and Automation (ICIA), Hailar, China.","DOI":"10.1109\/ICInfA.2014.6932751"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1109\/TCST.2007.899191","article-title":"Cooperative UAV formation flying with obstacle\/collision avoidance","volume":"15","author":"Wang","year":"2007","journal-title":"IEEE Trans. Control. Syst. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Manh\u00e3es, M.M.M., Scherer, S.A., Voss, M., Douat, L.R., and Rauschenbach, T. (2016, January 19\u201323). UUV Simulator: A Gazebo-based package for underwater intervention and multi-robot simulation. Proceedings of the OCEANS 2016 MTS\/IEEE Monterey, Monterey, CA, USA.","DOI":"10.1109\/OCEANS.2016.7761080"},{"key":"ref_15","first-page":"2701359","article-title":"An Improved Four-Rotor UAV Autonomous Navigation Multisensor Fusion Depth Learning","volume":"2022","author":"Liu","year":"2022","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sajjadi, S., Mehrandezh, M., and Janabi-Sharifi, F. (2022). A Cascaded and Adaptive Visual Predictive Control Approach for Real-Time Dynamic Visual Servoing. Drones, 6.","DOI":"10.3390\/drones6050127"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1007\/s10846-021-01522-y","article-title":"Embedded Fast Nonlinear Model Predictive Control for Micro Aerial Vehicles","volume":"103","author":"Nascimento","year":"2021","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1002\/rob.22046","article-title":"Experimental tests of hybrid VTOL unmanned aerial vehicle designed for surveillance missions and operations in maritime conditions from ship-based helipads","volume":"39","author":"Ambroziak","year":"2021","journal-title":"J. Field Robot."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hassan, S.A., Rahim, T., and Shin, S.Y. (2021). An Improved Deep Convolutional Neural Network-Based Autonomous Road Inspection Scheme Using Unmanned Aerial Vehicles. Electronics, 10.","DOI":"10.3390\/electronics10222764"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101474","DOI":"10.1016\/j.phycom.2021.101474","article-title":"Unmanned aerial vehicle passive detection for Internet of Space Things","volume":"49","author":"Zhu","year":"2021","journal-title":"Phys. Commun."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"107052","DOI":"10.1016\/j.ast.2021.107052","article-title":"Explainable Deep Reinforcement Learning for UAV autonomous path planning","volume":"118","author":"He","year":"2021","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/s40313-021-00828-4","article-title":"Autonomous Navigation System for a Delivery Drone","volume":"33","author":"Miranda","year":"2022","journal-title":"J. Control. Autom. Electr. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhao, X., Chong, J., Qi, X., and Yang, Z. (2021). Vision Object-Oriented Augmented Sampling-Based Autonomous Navigation for Micro Aerial Vehicles. Drones, 5.","DOI":"10.3390\/drones5040107"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1542","DOI":"10.1016\/j.asoc.2011.12.015","article-title":"DBCAMM: A novel density based clustering algorithm via using the Mahalanobis metric","volume":"12","author":"Ren","year":"2012","journal-title":"Appl. Soft Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1190\/1.1598124","article-title":"Robust inversion of seismic data using the Huber norm","volume":"68","author":"Guitton","year":"2003","journal-title":"Geophysics"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Elmokadem, T., and Savkin, A.V. (2021). Towards Fully Autonomous UAVs: A Survey. Sensors, 21.","DOI":"10.3390\/s21186223"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zammit, C., and Van Kampen, E.J. (2018, January 8\u201312). Comparison between A* and RRT algorithms for UAV path planning. Proceedings of the 2018 AIAA Guidance Navigation, and Control Conference, Kissimmee, FL, USA.","DOI":"10.2514\/6.2018-1846"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Aguilar, W.G., Morales, S., Ruiz, H., and Abad, V. (2017, January 14\u201316). RRT* GL based optimal path planning for real-time navigation of UAVs. Proceedings of the International Work-Conference on Artificial Neural Networks, C\u00e1diz, Spain.","DOI":"10.1007\/978-3-319-59147-6_50"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, C., and Meng, M.Q.H. (2016, January 6\u201310). Variant step size RRT: An efficient path planner for UAV in complex environments. Proceedings of the 2016 IEEE International Conference on Real-Time Computing and Robotics (RCAR), Angkor Wat, Cambodia.","DOI":"10.1109\/RCAR.2016.7784090"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1272","DOI":"10.1080\/01691864.2020.1815573","article-title":"Improving accuracy of VI-SLAM with fish-eye camera based on biases of map points","volume":"34","author":"Kang","year":"2020","journal-title":"Adv. Robot."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/12\/2608\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:37:01Z","timestamp":1760146621000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/12\/2608"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,9]]},"references-count":30,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["sym14122608"],"URL":"https:\/\/doi.org\/10.3390\/sym14122608","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,9]]}}}