{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T03:11:00Z","timestamp":1769569860579,"version":"3.49.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686448","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,27]]},"abstract":"<jats:p>With the rapid development of agricultural intelligence, the path planning problem of unmanned agricultural machinery in complex farmland environments has become increasingly important. Traditional path planning methods are confronted with problems of adaptability and robustness under dynamic obstacles and complex terrains, especially when dealing with irregular plots, the effect of path planning is not satisfactory. For this purpose, this study proposes a path optimization method for unmanned agricultural machinery based on ant colony algorithm and reinforcement learning (ACO-RL). By combining the global search ability of the ant colony algorithm with the strategic optimization advantages of reinforcement learning, a new collaborative optimization framework is proposed. The experimental results show that the ACO-RL algorithm performs significantly better than the existing mainstream path planning methods (DQN-RRT, PPO-A*, DDPG-GA) in three typical farmland environments. In plot A01, ACO-RL achieved a path coverage rate of 96.9% and a redundancy rate of 4.9%. The path length was 846.4 meters and the smoothness reached 0.89. In plots B12 and C07, ACO-RL also performed well in complex terterrain, with the redundancy rate controlled below 2.4% and the smoothness both above 0.88. Compared with other methods, ACO-RL not only has a significant improvement in the efficiency of path planning, but also shows strong advantages in path quality and stability. The successful application of this method indicates that ACO-RL can effectively solve the complexity and uncertainty in farmland path planning, and has strong practicability and promotion potential.<\/jats:p>","DOI":"10.3233\/faia251702","type":"book-chapter","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T13:20:18Z","timestamp":1769520018000},"source":"Crossref","is-referenced-by-count":0,"title":["Unmanned Agricultural Machinery Path Optimization Method Based on Reinforcement Learning"],"prefix":"10.3233","author":[{"given":"Yang","family":"Liu","sequence":"first","affiliation":[{"name":"Chinese Academy of Agricultural Mechanization Sciences Group Co., Ltd. Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaqian","family":"Zhao","sequence":"additional","affiliation":[{"name":"Chinese Academy of Agricultural Mechanization Sciences Group Co., Ltd. Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinrui","family":"Wang","sequence":"additional","affiliation":[{"name":"Chinese Academy of Agricultural Mechanization Sciences Group Co., Ltd. Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gaoteng","family":"Yuan","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huai\u2019an, 233002, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enbao","family":"Zhou","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huai\u2019an, 233002, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingen","family":"Zou","sequence":"additional","affiliation":[{"name":"Jianyu Intelligent Manufacturing (Beijing) Technology Co., Ltd, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining XI"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251702","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T13:20:18Z","timestamp":1769520018000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251702"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,27]]},"ISBN":["9781643686448"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251702","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,27]]}}}