{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T21:32:22Z","timestamp":1793482342541,"version":"4.5.0"},"reference-count":39,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T00:00:00Z","timestamp":1705968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>In the field of logistics warehousing robots, collaborative operation and coordinated control have always been challenging issues. Although deep learning and reinforcement learning methods have made some progress in solving these problems, however, current research still has shortcomings. In particular, research on adaptive sensing and real-time decision-making of multi-robot swarms has not yet received sufficient attention.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>To fill this research gap, we propose a YOLOv5-PPO model based on A3C optimization. This model cleverly combines the target detection capabilities of YOLOv5 and the PPO reinforcement learning algorithm, aiming to improve the efficiency and accuracy of collaborative operations among logistics and warehousing robot groups.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Through extensive experimental evaluation on multiple datasets and tasks, the results show that in different scenarios, our model can successfully achieve multi-robot collaborative operation, significantly improve task completion efficiency, and maintain target detection and environment High accuracy of understanding.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>In addition, our model shows excellent robustness and adaptability and can adapt to dynamic changes in the environment and fluctuations in demand, providing an effective method to solve the collaborative operation problem of logistics warehousing robots.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnbot.2023.1329589","type":"journal-article","created":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T04:21:30Z","timestamp":1705983690000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Research on multi-robot collaborative operation in logistics and warehousing using A3C optimized YOLOv5-PPO model"],"prefix":"10.3389","volume":"17","author":[{"given":"Lei","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangjun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,1,23]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2037","DOI":"10.1109\/ACCESS.2021.3139080","article-title":"Multi-robot hybrid coverage path planning for 3D reconstruction of large structures","volume":"10","author":"Almadhoun","year":"2021","journal-title":"IEEE Access"},{"key":"B2","doi-asserted-by":"publisher","first-page":"7283","DOI":"10.1109\/ACCESS.2022.3233219","article-title":"Automated power system fault prediction and precursor discovery using multi-modal data","volume":"11","author":"Alqudah","year":"2022","journal-title":"IEEE Access"},{"key":"B3","doi-asserted-by":"publisher","first-page":"3753","DOI":"10.5194\/tc-2021-282","article-title":"Probabilistic gridded seasonal sea ice presence forecasting using sequence to sequence learning","volume":"16","author":"Asadi","year":"2021","journal-title":"Cryosphere Discuss"},{"key":"B4","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1109\/SSRR56537.2022.10018733","article-title":"\u201cMulti-robot system for autonomous cooperative counter-UAS missions: design, integration, and field testing,\u201d","author":"Barisic","year":"2022","journal-title":"2022 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)"},{"key":"B5","doi-asserted-by":"publisher","first-page":"4133","DOI":"10.1109\/IROS40897.2019.8968465","article-title":"\u201cMulti robot route planning (MRRP): extended spatial-temporal prioritized planning,\u201d","author":"Binder","year":"2019","journal-title":"2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)"},{"key":"B6","doi-asserted-by":"publisher","first-page":"2160","DOI":"10.3390\/s22062160","article-title":"Systematic mapping literature review of mobile robotics competitions","volume":"22","author":"Brancali\u00e3o","year":"2022","journal-title":"Sensors"},{"key":"B7","doi-asserted-by":"publisher","first-page":"653","DOI":"10.3390\/machines11060653","article-title":"Overview of multi-robot collaborative SLAM from the perspective of data fusion","volume":"11","author":"Chen","year":"2023","journal-title":"Machines"},{"key":"B8","doi-asserted-by":"publisher","first-page":"1402","DOI":"10.3390\/electronics12061402","article-title":"Distributed multi-robot-trailer scheduling based on communication between charging stations for robot being towed to recharge","volume":"12","author":"Deng","year":"2023","journal-title":"Electronics"},{"key":"B9","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1109\/MFI49285.2020.9235257","article-title":"\u201cAirMuseum: a heterogeneous multi-robot dataset for stereo-visual and inertial simultaneous localization and mapping,\u201d","author":"Dubois","year":"2020"},{"key":"B10","doi-asserted-by":"publisher","first-page":"103975","DOI":"10.1016\/j.robot.2021.103975","article-title":"ChildBot: multi-robot perception and interaction with children","volume":"150","author":"Efthymiou","year":"2022","journal-title":"Rob. 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