{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T05:46:38Z","timestamp":1769751998392,"version":"3.49.0"},"reference-count":56,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,9,4]],"date-time":"2022-09-04T00:00:00Z","timestamp":1662249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"<jats:p>The aim of this research effort was to develop a framework for a structure from motion (SfM)-based 3D reconstruction approach with a team of autonomous small unmanned aerial systems (sUASs) using a distributed behavior model. The framework is composed of two major goals to accomplish this: a distributed behavior model for a team of sUASs and a SfM-based 3D reconstruction using team of sUASs. The developed distributed behavior model is based on the entropy of the system, and when the entropy of the system is high, the sUASs get closer to reducing the overall entropy. This is called the grouping phase. If the entropy is less than the predefined threshold, then the sUASs switch to the 3D reconstruction phase. The novel part of the framework is that sUASs are only given the object of interest to reconstruct the 3D model, and they use the developed distributed behavior to coordinate their motion for that goal. A comprehensive parameter analysis was performed, and optimum sets of parameters were selected for each sub-system. Finally, optimum parameters for two sub-systems were combined in a simulation to demonstrate the framework\u2019s operability and evaluate the completeness and speed of the reconstructed model. The simulation results show that the framework operates successfully and is capable of generating complete models as desired, autonomously.<\/jats:p>","DOI":"10.3390\/robotics11050089","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"89","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Design of a Rapid Structure from Motion (SfM) Based 3D Reconstruction Framework Using a Team of Autonomous Small Unmanned Aerial Systems (sUAS)"],"prefix":"10.3390","volume":"11","author":[{"suffix":"Jr.","given":"Douglas Shane","family":"Smith","sequence":"first","affiliation":[{"name":"Intelligent Systems & Robotics, University of West Florida, Pensacola, FL 32514, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8333-342X","authenticated-orcid":false,"given":"Hakki Erhan","family":"Sevil","sequence":"additional","affiliation":[{"name":"Intelligent Systems & Robotics, University of West Florida, Pensacola, FL 32514, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Schonberger, J.L., and Frahm, J.M. (2016, January 27\u201330). Structure-from-motion revisited. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.445"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1109\/70.736776","article-title":"Behavior-based formation control for multirobot teams","volume":"14","author":"Balch","year":"1998","journal-title":"IEEE Trans. Robot. Autom."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1109\/TRA.2003.819598","article-title":"A decentralized approach to formation maneuvers","volume":"19","author":"Lawton","year":"2003","journal-title":"IEEE Trans. Robot. Autom."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s10514-010-9198-8","article-title":"Attractor dynamics approach to formation control: Theory and application","volume":"29","author":"Monteiro","year":"2010","journal-title":"Auton. Robot."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"205759","DOI":"10.1155\/2014\/205759","article-title":"Behavior-based formation control of swarm robots","volume":"2014","author":"Xu","year":"2014","journal-title":"Math. Probl. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1109\/TAC.2005.864190","article-title":"Flocking for multi-agent dynamic systems: Algorithms and theory","volume":"51","year":"2006","journal-title":"IEEE Trans. Autom. Control."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"eaat3536","DOI":"10.1126\/scirobotics.aat3536","article-title":"Optimized flocking of autonomous drones in confined environments","volume":"3","author":"Somorjai","year":"2018","journal-title":"Sci. Robot."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2606","DOI":"10.1109\/ROBOT.2002.1013624","article-title":"A dynamical systems approach to behavior-based formation control","volume":"Volume 3","author":"Monteiro","year":"2002","journal-title":"Proceedings of the 2002 IEEE International Conference on Robotics and Automation (Cat. No. 02CH37292)"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1109\/TRA.2002.803458","article-title":"A general algorithm for robot formations using local sensing and minimal communication","volume":"18","author":"Fredslund","year":"2002","journal-title":"IEEE Trans. Robot. Autom."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2437","DOI":"10.1109\/TVT.2020.2964847","article-title":"Multi-UAV Formation Control Based on a Novel Back-Stepping Approach","volume":"69","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.comcom.2020.01.076","article-title":"A distributed flocking control strategy for UAV groups","volume":"153","author":"Liu","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s11370-017-0240-y","article-title":"Decentralized behavior-based formation control of multiple robots considering obstacle avoidance","volume":"11","author":"Lee","year":"2018","journal-title":"Intell. Serv. Robot."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"165420","DOI":"10.1109\/ACCESS.2019.2952924","article-title":"Consensus-Based Formation of Second-Order Multi-Agent Systems via Linear-Transformation-Based Partial Stability Approach","volume":"7","author":"Qu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cofta, P., Ledzi\u0144ski, D., \u015amigiel, S., and Gackowska, M. (2020). Cross-Entropy as a Metric for the Robustness of Drone Swarms. Entropy, 22.","DOI":"10.3390\/e22060597"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Albani, D., Manoni, T., Arik, A., Nardi, D., and Trianni, V. (2019, January 13\u201314). Field coverage for weed mapping: Toward experiments with a UAV swarm. Proceedings of the International Conference on Bio-Inspired Information and Communication, Pittsburgh, PA, USA.","DOI":"10.1007\/978-3-030-24202-2_10"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"36","DOI":"10.3389\/frobt.2020.00036","article-title":"Swarm Robotic Behaviors and Current Applications","volume":"7","author":"Schranz","year":"2020","journal-title":"Front. Robot."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s41018-018-0045-4","article-title":"Search and rescue with autonomous flying robots through behavior-based cooperative intelligence","volume":"3","author":"Arnold","year":"2018","journal-title":"J. Int. Humanit. Action"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yuheng, Z., Liyan, Z., and Chunpeng, L. (2019, January 16\u201319). 3-d deployment optimization of uavs based on particle swarm algorithm. Proceedings of the 2019 IEEE 19th International Conference on Communication Technology (ICCT), Xi\u2019an, China.","DOI":"10.1109\/ICCT46805.2019.8947140"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cao, H., Zhang, H., Liu, Z., Zhou, Y., and Wang, Y. (2021, January 22\u201324). UAV path planning based on improved particle swarm algorithm. Proceedings of the 2021 7th International Symposium on Mechatronics and Industrial Informatics (ISMII), Zhuhai, China.","DOI":"10.1109\/ISMII52409.2021.00067"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1238","DOI":"10.1016\/j.cja.2013.07.009","article-title":"Cooperative task assignment of multiple heterogeneous unmanned aerial vehicles using a modified genetic algorithm with multi-type genes","volume":"26","author":"Deng","year":"2013","journal-title":"Chin. J. Aeronaut."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/S0921-8890(03)00071-X","article-title":"Maintaining a common co-ordinate system for a group of robots based on vision","volume":"44","author":"Wildermuth","year":"2003","journal-title":"Robot. Auton. Syst."},{"key":"ref_22","first-page":"87","article-title":"Multi-robot dynamic role assignment and coordination through shared potential fields","volume":"2","author":"Vail","year":"2003","journal-title":"Multi-Robot. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lakas, A., Belkacem, A.N., and Al Hassani, S. (June, January 15). An Adaptive Multi-clustered Scheme for Autonomous UAV Swarms. Proceedings of the 2020 International Wireless Communications and Mobile Computing (IWCMC), Limassol, Cyprus.","DOI":"10.1109\/IWCMC48107.2020.9148449"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Barnes, L., Garcia, R., Fields, M., and Valavanis, K. (2008, January 22\u201326). Swarm formation control utilizing ground and aerial unmanned systems. Proceedings of the 2008 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Nice, France.","DOI":"10.1109\/IROS.2008.4651260"},{"key":"ref_25","unstructured":"Bayram, \u00c7., Sevil, H.E., and \u00d6zdemir, S. (2007, January 21\u201323). A distributed behavioral model for landmine detection robots. Proceedings of the International MultiConference of Engineers and Computer Scientists 2007, IMECS 2007, Hong Kong, China."},{"key":"ref_26","unstructured":"MacKenzie, D.C. (2003, January 17\u201319). Collaborative tasking of tightly constrained multi-robot missions. Proceedings of the Multi-Robot Systems: From Swarms to Intelligent Automata: Proceedings of the 2003 International Workshop on Multi-Robot Systems 2003, Washington, DC, USA."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, R., and Ma, H. (2020, January 27\u201328). Research on UAV Swarm Cooperative Reconnaissance and Combat Technology. Proceedings of the 2020 3rd International Conference on Unmanned Systems (ICUS), Harbin, China.","DOI":"10.1109\/ICUS50048.2020.9274902"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1111\/j.1477-9730.2005.00316.x","article-title":"3D building modelling with digital map, lidar data and video image sequences","volume":"20","author":"Zhang","year":"2005","journal-title":"Photogramm. Rec."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.isprsjprs.2015.05.006","article-title":"Automatic registration of optical aerial imagery to a LiDAR point cloud for generation of city models","volume":"106","author":"Abayowa","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.isprsjprs.2014.12.025","article-title":"Automatic registration of UAV-borne sequent images and LiDAR data","volume":"101","author":"Yang","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"S40","DOI":"10.1016\/j.isprsjprs.2011.09.012","article-title":"Data fusion of extremely high resolution aerial imagery and LiDAR data for automated railroad centre line reconstruction","volume":"66","author":"Beger","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.isprsjprs.2007.01.001","article-title":"Data fusion of high-resolution satellite imagery and LiDAR data for automatic building extraction","volume":"62","author":"Sohn","year":"2007","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.jvcir.2013.06.008","article-title":"Fusion of 3D-LIDAR and camera data for scene parsing","volume":"25","author":"Zhao","year":"2014","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sinsley, G., Long, L., Geiger, B., Horn, J., and Niessner, A. (2009, January 6\u20139). Fusion of unmanned aerial vehicle range and vision sensors using fuzzy logic and particles. Proceedings of the AIAA Infotech@ Aerospace Conference and AIAA Unmanned... Unlimited Conference, Seattle, WA, USA.","DOI":"10.2514\/6.2009-2008"},{"key":"ref_35","unstructured":"Korpela, I., Dahlin, B., Sch\u00e4fer, H., Bruun, E., Haapaniemi, F., Honkasalo, J., Ilvesniemi, S., Kuutti, V., Linkosalmi, M., and Mustonen, J. (2007, January 12\u201314). Single-tree forest inventory using lidar and aerial images for 3D treetop positioning, species recognition, height and crown width estimation. Proceedings of the ISPRS Workshop on Laser Scanning, Espoo, Finland."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Song, H.r., Choi, W.s., Lim, S.m., and Kim, H.d. (2014, January 26\u201328). Target localization using RGB-D camera and LiDAR sensor fusion for relative navigation. Proceedings of the Automatic Control Conference (CACS), 2014 CACS International, Kaohsiung, Taiwan.","DOI":"10.1109\/CACS.2014.7097178"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Carlevaris-Bianco, N., Mohan, A., McBride, J.R., and Eustice, R.M. (2011, January 25\u201330). Visual localization in fused image and laser range data. Proceedings of the Intelligent Robots and Systems (IROS), 2011 IEEE\/RSJ International Conference on, San Francisco, CA, USA.","DOI":"10.1109\/IROS.2011.6048631"},{"key":"ref_38","unstructured":"Sch\u00f6nberger, J.L. (2018). Robust Methods for Accurate and Efficient 3D Modeling from Unstructured Imagery. [Ph.D. Thesis, ETH Zurich]."},{"key":"ref_39","unstructured":"Nikolov, I., and Madsen, C. (November, January 31). Benchmarking close-range structure from motion 3D reconstruction software under varying capturing conditions. Proceedings of the Euro-Mediterranean Conference, Nicosia, Cyprus."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Bianco, S., Ciocca, G., and Marelli, D. (2018). Evaluating the performance of structure from motion pipelines. J. Imaging, 4.","DOI":"10.3390\/jimaging4080098"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Stathopoulou, E.K., and Remondino, F. (2019, January 2\u20133). Open-source image-based 3D reconstruction pipelines: Review, comparison and evaluation. Proceedings of the 6th International Workshop LowCost 3D\u2013Sensors, Algorithms, Applications, Strasbourg, France.","DOI":"10.5194\/isprs-archives-XLII-2-W17-331-2019"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Maxence, R., Uchiyama, H., Kawasaki, H., Thomas, D., Nozick, V., and Saito, H. (2019, January 16\u201319). Mobile photometric stereo with keypoint-based SLAM for dense 3D reconstruction. Proceedings of the 2019 International Conference on 3D Vision (3DV), Quebec City, Canada.","DOI":"10.1109\/3DV.2019.00069"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Mentasti, S., and Pedersini, F. (2019). Controlling the flight of a drone and its camera for 3D reconstruction of large objects. Sensors, 19.","DOI":"10.3390\/s19102333"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Lundberg, C.L., Sevil, H.E., and Das, A. (2018, January 12\u201315). A VisualSfM based Rapid 3-D Modeling Framework using Swarm of UAVs. Proceedings of the 2018 International Conference on Unmanned Aircraft Systems (ICUAS), Dallas, TX, USA.","DOI":"10.1109\/ICUAS.2018.8453396"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Daftry, S., Hoppe, C., and Bischof, H. (2015, January 26\u201330). Building with drones: Accurate 3D facade reconstruction using MAVs. Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7139681"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"11615","DOI":"10.1109\/JSEN.2020.3042810","article-title":"Local Feature Performance Evaluation for Structure-From-Motion and Multi-View Stereo Using Simulated City-Scale Aerial Imagery","volume":"21","author":"Gao","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Shah, S., Dey, D., Lovett, C., and Kapoor, A. (2018). Airsim: High-fidelity visual and physical simulation for autonomous vehicles. Field and Service Robotics, Springer.","DOI":"10.1007\/978-3-319-67361-5_40"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1007\/BF01016429","article-title":"Possible generalization of Boltzmann-Gibbs statistics","volume":"52","author":"Tsallis","year":"1988","journal-title":"J. Stat. Phys."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Bayram, C., Sevil, H.E., and Ozdemir, S. (2008). Swarm and entropic modeling for landmine detection robots. Trends in Intelligent Systems and Computer Engineering, Springer.","DOI":"10.1007\/978-0-387-74935-8_7"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Das, A.N., Doelling, K., Lundberg, C., Sevil, H.E., and Lewis, F. (2017, January 3\u20139). A Mixed reality based hybrid swarm control architecture for manned-unmanned teaming (MUM-T). Proceedings of the ASME International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, Tampa, FL, USA.","DOI":"10.1115\/IMECE2017-72076"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Das, A., Kol, P., Lundberg, C., Doelling, K., Sevil, H.E., and Lewis, F. (2018, January 23\u201326). A rapid situational awareness development framework for heterogeneous manned-unmanned teams. Proceedings of the NAECON 2018-IEEE National Aerospace and Electronics Conference, Dayton, OH, USA.","DOI":"10.1109\/NAECON.2018.8556769"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Sevil, H.E. (2020, January 6\u201310). Anomaly Detection using Parity Space Approach in Team of UAVs with Entropy based Distributed Behavior. Proceedings of the AIAA Scitech 2020 Forum, Orlando, FL, USA.","DOI":"10.2514\/6.2020-1625"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Szeliski, R. (2010). Computer Vision: Algorithms and Applications, Springer Science & Business Media.","DOI":"10.1007\/978-1-84882-935-0"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_55","unstructured":"M\u00f6nnig, J. (2021, October 08). ToHoku University Multi-View Stereo (THU-MVS) Datasets. Available online: http:\/\/www.aoki.ecei.tohoku.ac.jp\/mvs\/."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Smith, D.S. (2021). A Rapid Structure from Motion (SFM) Based 3-D Modeling Framework Using a Team of Autonomous Small Unmanned Aerial Systems (sUAS). [Master\u2019s Thesis, The University of West Florida].","DOI":"10.3390\/robotics11050089"}],"container-title":["Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2218-6581\/11\/5\/89\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:23:07Z","timestamp":1760142187000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2218-6581\/11\/5\/89"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,4]]},"references-count":56,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["robotics11050089"],"URL":"https:\/\/doi.org\/10.3390\/robotics11050089","relation":{},"ISSN":["2218-6581"],"issn-type":[{"value":"2218-6581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,4]]}}}