{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T03:18:12Z","timestamp":1784171892997,"version":"3.55.0"},"reference-count":21,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,6,9]],"date-time":"2022-06-09T00:00:00Z","timestamp":1654732800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Robotics"],"published-print":{"date-parts":[[2022,6,9]]},"abstract":"<jats:p>Path planning is an important part of the unmanned aerial vehicle (UAV) to realize its autonomous capabilities. Aiming at the shortcomings of the traditional ant colony algorithm-based trajectory planning method, which has slow convergence speed and easy to fall into the local optimum, a path planning method based on the improved ant colony algorithm is proposed. First, a dynamic adjusting factor is added into the heuristic function to improve the directivity of path selection and search speed. Then, the state transition strategy is improved to solve the problem of slow convergence in the initial stage and easy to fall into local optimum in the later stage. Finally, the path inflection point is smoothly optimized through the cubic B-spline curve. Simulation results show that the improved ant colony algorithm can quickly converge to the optimal path and well adapt to the flight requirements of multirotor UAV.<\/jats:p>","DOI":"10.1155\/2022\/2168964","type":"journal-article","created":{"date-parts":[[2022,6,9]],"date-time":"2022-06-09T16:50:32Z","timestamp":1654793432000},"page":"1-9","source":"Crossref","is-referenced-by-count":15,"title":["Path Planning of Multirotor UAV Based on the Improved Ant Colony Algorithm"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5254-6310","authenticated-orcid":true,"given":"Duo","family":"Qi","sequence":"first","affiliation":[{"name":"Aviation Swarm Technology and Operational Application Laboratory, Air Traffic Control and Navigation College, Air Force Engineering University, Xi\u2019an, China"},{"name":"Shaanxi Province Laboratory of Meta-Synthesis for Electronic and Information System, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0230-1742","authenticated-orcid":true,"given":"Zhihao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aviation Swarm Technology and Operational Application Laboratory, Air Traffic Control and Navigation College, Air Force Engineering University, Xi\u2019an, China"},{"name":"Shaanxi Province Laboratory of Meta-Synthesis for Electronic and Information System, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1680-5877","authenticated-orcid":true,"given":"Qirui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Unit 93525 of PLA, Rikaze City, Xizang Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","article-title":"Application of path planning based on improved A-star algorithm in war gaming of naval warfare","author":"T. 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Neng","year":"2019","journal-title":"Journal of Chongqing University of Technology (Natural Science)"},{"issue":"4","key":"7","first-page":"704","article-title":"Path planning for mobile robot based on improved genetic algorithm","volume":"46","author":"W. Tong","year":"2020","journal-title":"Journal of Beijing University of Aeronautics and Astronautics"},{"issue":"5","key":"8","first-page":"943","article-title":"A hybrid algorithm of artificial fish swarm and genetic algorithm and its application in collision avoidance of unmanned surface vessels","volume":"41","author":"X. Liang","year":"2019","journal-title":"Computer Engineering and Science"},{"issue":"2","key":"9","first-page":"114","article-title":"Research on path planning of mobile robot based on improved ant colony algorithm","volume":"40","author":"M. 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