Optimizing Automated Picking Systems in Warehouse Robots Using Machine Learning
AMA University（AMA大学）；University of the People（人民大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Disney entertainment and sports LLC（迪士尼娱乐体育有限责任公司）；University of Toronto（多伦多大学）；Northwestern University（西北大学）；Northern Arizona University（北亚利桑那大学）
Keqin Li, Jin Wang, Xubo Wu, Xirui Peng, Runmian Chang, Xiaoyu Deng, Yiwen Kang, Yue Yang, Fanghao Ni, Bo Hong
备注：
Published in IEEE Xplore
发表信息：
Proceedings of the 7th International Conference on Mechatronics and Computer Technology Engineering (MCTE 2024), pp. 1383-1387, 2024
With the rapid growth of global e-commerce, the demand for automation in the logistics industry is increasing. This study focuses on automated picking systems in warehouses, utilizing deep learning and reinforcement learning technologies to enhance picking efficiency and accuracy while reducing system failure rates. Through empirical analysis, we demonstrate the effectiveness of these technologies in improving robot picking performance and adaptability to complex environments. The results show that the integrated machine learning model significantly outperforms traditional methods, effectively addressing the challenges of peak order processing, reducing operational errors, and improving overall logistics efficiency. Additionally, by analyzing environmental factors, this study further optimizes system design to ensure efficient and stable operation under variable conditions. This research not only provides innovative solutions for logistics automation but also offers a theoretical and empirical foundation for future technological development and application.
Joint order assignment and picking station scheduling in KIVA warehouses with multiple stations
School of Economics and Management, Beijing Jiaotong University（北京交通大学经济管理学院）；Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University（香港理工大学物流及航运学系）；Management School, University of Liverpool（利物浦大学管理学院）
DoraPicker: An Autonomous Picking System for General Objects
Dorabot Inc.（多拉机器人公司）；University of Hong Kong（香港大学）；Advanced Industrial Science and Technology（日本产业技术综合研究所）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；City University of Hong Kong（香港城市大学）
Analysis and Observations from the First Amazon Picking Challenge
University of Colorado at Boulder（科罗拉多大学博尔德分校）；Rutgers University（罗格斯大学）；University of Michigan（密歇根大学）；Technische Universität Berlin（柏林工业大学）；Nanyang Technological University（南洋理工大学）；Duke University（杜克大学）；the University of Tokyo（东京大学）；MIT（麻省理工学院）；Kiva Systems
Multi-Robot Routing with Time Windows: A Column Generation Approach
University of Maryland（马里兰大学）；University of Southern California（南加州大学）；Verisk（Verisk公司）；ESG UQAM（魁北克大学蒙特利尔分校 ESG 学院）；GERAD（GERAD研究院）；University of California, Irvine（加州大学欧文分校）；Laminaar（Laminaar公司）
Optimizing Drug Delivery in Smart Pharmacies: A Novel Framework of Multi-Stage Grasping Network Combined with Adaptive Robotics Mechanism
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science（中国科学院苏州生物医学工程技术研究所）；Fuzhou University（福州大学）；Monash University（莫纳什大学）；The First Affiliated Hospital, Nanjing Medical University（南京医科大学第一附属医院）
