{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T06:59:09Z","timestamp":1769756349885,"version":"3.49.0"},"reference-count":44,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T00:00:00Z","timestamp":1705881600000},"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>In this paper, we propose a novel distributed algorithm based on model predictive control and alternating direction multiplier method (DMPC-ADMM) for cooperative trajectory planning of quadrotor swarms. First, a receding horizon trajectory planning optimization problem is constructed, in which the differential flatness property is used to deal with the nonlinear dynamics of quadrotors while we design a relaxed form of the discrete-time control barrier function (DCBF) constraint to balance feasibility and safety. Then, we decompose the original trajectory planning problem by ADMM and solve it in a fully distributed manner with peer-to-peer communication, which induces the quadrotors within the communication range to reach a consensus on their future trajectories to enhance safety. In addition, an event-triggered mechanism is designed to reduce the communication overhead. The simulation results verify that the trajectories generated by our method are real-time, safe, and smooth. A comprehensive comparison with the centralized strategy and several other distributed strategies in terms of real-time, safety, and feasibility verifies that our method is more suitable for the trajectory planning of large-scale quadrotor swarms.<\/jats:p>","DOI":"10.3390\/s24020707","type":"journal-article","created":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T12:01:13Z","timestamp":1705924873000},"page":"707","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Cooperative Safe Trajectory Planning for Quadrotor Swarms"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0627-6478","authenticated-orcid":false,"given":"Yahui","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Control Science and Engineering, Tongji University, Shanghai 201804, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2494-1505","authenticated-orcid":false,"given":"Peng","family":"Yi","sequence":"additional","affiliation":[{"name":"Department of Control Science and Engineering, Tongji University, Shanghai 201804, China"},{"name":"Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201210, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiguang","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Control Science and Engineering, Tongji University, Shanghai 201804, China"},{"name":"Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201210, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ryan, A., Zennaro, M., Howell, A., Sengupta, R., and Hedrick, J.K. 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