{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:49:14Z","timestamp":1740149354922,"version":"3.37.3"},"reference-count":21,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"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":"The paper shows the simulation of the behavior of a swarm of underwater drones (AUV) diffused in a closed section of the sea and inserted from a single starting point: Based on a few essential rules, we will see how their behavior evolves and how they manage to spread throughout the area assigned to them. In the first part of this work, after defining the design of the vehicle, we introduce our vision of the swarm, its problems, and its strengths. Later, we show how to spread a series of underwater drones with \u201cdiffused intelligence\u201d (swarm) and its microscopic diffusion model. In the last part, we present the simulation that supports our approach to the swarm.<\/jats:p>","DOI":"10.3390\/s20174950","type":"journal-article","created":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T12:53:43Z","timestamp":1598964823000},"page":"4950","source":"Crossref","is-referenced-by-count":19,"title":["Simulation of Autonomous Underwater Vehicles (AUVs) Swarm Diffusion"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6135-8386","authenticated-orcid":false,"given":"Enrico","family":"Petritoli","sequence":"first","affiliation":[{"name":"Science Department, Universit\u00e0 degli Studi \u201cRoma Tre\u201d, Via della Vasca Navale n. 84, 00146 Rome, Italy"}]},{"given":"Marco","family":"Cagnetti","sequence":"additional","affiliation":[{"name":"Science Department, Universit\u00e0 degli Studi \u201cRoma Tre\u201d, Via della Vasca Navale n. 84, 00146 Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8152-2112","authenticated-orcid":false,"given":"Fabio","family":"Leccese","sequence":"additional","affiliation":[{"name":"Science Department, Universit\u00e0 degli Studi \u201cRoma Tre\u201d, Via della Vasca Navale n. 84, 00146 Rome, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Reynolds, C.W. (1987, January 2\u20134). Flocks, herds and schools: A distributed behavioral model. Proceedings of the 14th annual conference on Computer graphics and interactive techniques (SIGGRAPH \u201887), New York, NY, USA.","DOI":"10.1145\/37401.37406"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, X., Xu, X., Yan, L., Zhao, H., and Zhang, T. (2020). Energy-Efficient Data Collection Using Autonomous Underwater Glider: A Reinforcement Learning Formulation. Sensors, 20.","DOI":"10.3390\/s20133758"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Che, F., Niu, Y., Li, J., and Wu, L. (2020). Cooperative Standoff Tracking of Moving Targets Using Modified Lyapunov Vector Field Guidance. Appl. Sci., 10.","DOI":"10.3390\/app10113709"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sun, J., Hu, F., Jin, W., Wang, J., Wang, X., Luo, Y., Yu, J., and Zhang, A. (2020). Model-Aided Localization and Navigation for Underwater Gliders Using Single-Beacon Travel-Time Differences. Sensors, 20.","DOI":"10.3390\/s20030893"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Madridano, \u00c1., Al-Kaff, A., Mart\u00edn, D., and de la Escalera, A.A. (2020). 3D Trajectory Planning Method for UAVs Swarm in Building Emergencies. Sensors, 20.","DOI":"10.3390\/s20030642"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Rosenberg, L., Baltaxe, D., and Pescetelli, N. (2016, January 21\u201323). Crowds vs swarms, a comparison of intelligence. Proceedings of the 2016 Swarm\/Human Blended Intelligence Workshop (SHBI), Cleveland, OH, USA.","DOI":"10.1109\/SHBI.2016.7780278"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Munlin, M., and Anantathanavit, M. (2017, January 18\u201320). New social-based radius particle swarm optimization. Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA), Siem Reap, Cambodia.","DOI":"10.1109\/ICIEA.2017.8282956"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Shen, Y., Li, Y., Kang, H., Zhang, Y., Sun, X., Chen, Q., Peng, J., and Wang, H. (2018, January 7\u201310). Research on Swarm Size of Multi-swarm Particle Swarm Optimization Algorithm. Proceedings of the 2018 IEEE 4th International Conference on Computer and Communications (ICCC), Chengdu, China.","DOI":"10.1109\/CompComm.2018.8781013"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kirschenbaum, M., and Palmer, D.W. (2015, January 28\u201329). Perceptualization of particle swarm optimization. Proceedings of the 2015 Swarm\/Human Blended Intelligence Workshop (SHBI), Cleveland, OH, USA.","DOI":"10.1109\/SHBI.2015.7321681"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Shafer, B. (2015, January 28\u201329). Humans in the GPU swarm: A proposal. Proceedings of the 2015 Swarm\/Human Blended Intelligence Workshop (SHBI), Cleveland, OH, USA.","DOI":"10.1109\/SHBI.2015.7321686"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Woithe, H.C., Tilkidjieva, D., and Kremer, U. (2008). Towards a Resource-Aware Programming Architecture for Smart Autonomous Underwater Vehicles, Rutgers University. Technical Report, DCS-TR-637.","DOI":"10.1109\/IROS.2009.5354098"},{"key":"ref_12","unstructured":"Graver, J.G., Liu, J., Woolsey, C., and Leonard, N.E. (1998, January 15\u201317). Design and Analysis of an Underwater Vehicle for Controlled Gliding. Proceedings of the 1998 Conference on Information Sciences and Systems, Princeton, NJ, USA."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hussain, N.A.A., Ali, S.S.A., Saad, M.N.M., and Nordin, N. (2016, January 13\u201314). Underactuated nonlinear adaptive control approach using U-model for multivariable underwater glider control parameters. Proceedings of the 2016 IEEE International Conference on Underwater System Technology: Theory and Applications (USYS), Penang, Malaysia.","DOI":"10.1109\/USYS.2016.7893947"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1109\/48.972077","article-title":"SLOCUM: An underwater glider propelled by environmental energy","volume":"26","author":"Webb","year":"2001","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3","DOI":"10.21014\/acta_imeko.v7i2.535","article-title":"High accuracy attitude and navigation system for an autonomous underwater vehicle (AUV)","volume":"7","author":"Petritoli","year":"2018","journal-title":"Acta IMEKO"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Petritoli, E., and Leccese, F. (2017, January 11\u201313). A high accuracy navigation system for a tailless underwater glider. Proceedings of the IMEKO TC19 Workshop on Metrology for the Sea, MetroSea 2017: Learning to Measure Sea Health Parameters, Naples, Italy.","DOI":"10.1109\/MetroSea.2018.8657831"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1109\/48.972106","article-title":"Model-Based feedback control of autonomous underwater gliders","volume":"26","author":"Leonard","year":"2001","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Petritoli, E., Leccese, F., and Cagnetti, M. (2018, January 8\u201310). A High Accuracy Buoyancy System Control for an Underwater Glider. Proceedings of the 2018 IEEE International Workshop on Metrology for the Sea, Learning to Measure Sea Health Parameters (MetroSea), Bari, Italy.","DOI":"10.1109\/MetroSea.2018.8657831"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Smith, R.N., Pereira, A., Chao, Y., Li, P.P., Caron, A.D., Jones, H.B., and Sukhatme, G.S. (2010, January 3\u20137). Autonomous Underwater Vehicle trajectory design coupled with predictive ocean models: A case study. Proceedings of the 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, USA.","DOI":"10.1109\/ROBOT.2010.5509240"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Leccese, F., Cagnetti, M., Giarnetti, S., Petritoli, E., Luisetto, I., Tuti, S., \u00d0urovi\u0107-Pej\u010dev, R., \u00d0or\u0111evi\u0107, T., Toma\u0161evi\u0107, A., and Bursi\u0107, V. (2018, January 8\u201310). A Simple Takagi-Sugeno Fuzzy Modelling Case Study for an Underwater Glider Control System. Proceedings of the 2018 IEEE International Workshop on Metrology for the Sea, Bari, Italy.","DOI":"10.1109\/MetroSea.2018.8657877"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1109\/TC.1976.1674656","article-title":"A highly efficient redundancy scheme: Self-purging redundancy","volume":"6","author":"Losq","year":"1976","journal-title":"IEEE Trans. Comput."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/17\/4950\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T21:05:53Z","timestamp":1719954353000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/17\/4950"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,1]]},"references-count":21,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["s20174950"],"URL":"https:\/\/doi.org\/10.3390\/s20174950","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,9,1]]}}}