Special Session 15
Advances in Robotics and Autonomous Systems for Complex Environments
复杂环境下机器人学与自主系统的新进展
Introduction:
Robotics and autonomous systems have undergone tremendous advancements in recent years, particularly in the areas of autonomy, perception, control, and multi-agent collaboration. The evolution of these technologies has enabled applications in diverse and complex environments, ranging from industrial settings to emergency response and hazardous areas. This special session seeks to explore cutting-edge research on robotics and autonomous systems specifically designed for complex, dynamic, and constrained environments. The focus will be on innovative approaches to improving task planning, environmental modeling, fault-tolerant control, and safety in the presence of uncertainties.
The session will focus on the following topics:
(1) Autonomous Task Planning and Execution: Techniques for high-level task decomposition, multi-robot coordination, and robust execution in dynamic and partially observable environments.
(2) Environmental Modeling and Perception: Novel methods in multi-modal perception (vision, LIDAR, IMU) and real-time environmental modeling for localization, navigation, and interaction in confined or hazardous spaces.
(3) Fault Tolerant Control and Safety: Approaches to enhancing the reliability and robustness of autonomous systems, including fault detection, diagnosis, and recovery, as well as methods for ensuring safe operation in dynamic, uncertain environments.
(4) Human-Robot Interaction and Collaboration: Improving the synergy between robots and human operators in collaborative tasks, especially in remote or hazardous environments where human presence is limited.
(5) Robotics for Specific Applications: Use cases of robotic systems in complex applications such as industrial inspections, remote maintenance, search-and-rescue operations, and disaster recovery.
机器人学与自主系统近年来取得了巨大进展，尤其在自主性、环境感知、运动控制与多智能体协作领域。这些技术的进步使得机器人系统能够应用于从工业场景到应急救援、危险区域作业等多样化复杂环境。本专题会议旨在探索面向复杂动态受限环境的机器人及自主系统前沿研究，重点关注在不确定性条件下提升任务规划、环境建模、容错控制与安全性的创新方法。
会议主题包括：
（1）自主任务规划与执行：动态及部分可观测环境下的高层级任务分解、多机器人协调与鲁棒执行技术；
（2）环境建模与感知：面向狭窄或危险空间定位、导航与交互的多模态感知（视觉/激光雷达/LiDAR/惯性测量单元/IMU）及实时建模方法；
（3）容错控制与安全性：增强自主系统可靠性与鲁棒性的故障检测-诊断-恢复技术，以及动态不确定环境中的安全运行保障策略；
（4）人机交互与协作：远程或高危场景下机器人与人类操作员的任务协同优化方法；
（5）特定场景机器人技术：复杂应用中的机器人系统实践案例，如工业检测、远程运维、搜救行动与灾后重建。
Organizers:
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Jianliang Mao, Shanghai University of Electric Power, Shanghai, China Jianliang Mao is an Associate Professor of the College of Automation Engineering, Shanghai University of Electric Power, Shanghai, China. He received the B.Sc. degree in automation, the M.Sc. degree in control engineering, and the Ph.D. degree in control theory and control engineering from the School of Automation, Southeast University, Nanjing, China, in 2011, 2014, and 2018, respectively. From Oct. 2018 to Mar. 2021, he worked with the Research and Development Institute, Estun Automation Company, Ltd., Nanjing, China. From Apr. 2024 to Mar. 2025, he conducted academic research at Shanghai Jiao Tong University as a visiting scholar. Since Mar. 2021, he has been with the College of Automation Engineering, Shanghai University of Electric Power, Shanghai, China. He has published more than 30 international journal papers, including IEEE Trans. Cybern., IEEE Trans. Control Syst. Technol., IEEE Trans. Ind. Electron., etc. His research interests include model predictive control, vision-based interactive control, and their applications to robot system. |
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Wenbo Xie, Shanghai University, China Wen-Bo Xie is an Associate Professor of the School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China. He received the B.Eng. degree in automation and Ph.D. degree in control science and engineering from the College of Automation, Harbin Engineering University, Harbin, China, in 2008 and 2013, respectively. He was a Postdoctoral Research Fellow with the University of Adelaide, Adelaide, SA, Australia, from Oct. 2015 to Oct. 2016; a Visiting Scholar with the Harbin Institute of Technology, Harbin, China, from Sep. 2017 to Jul. 2018; a Postdoctoral with Shanghai University, Shanghai, China, from Dec. 2018 to Jul. 2021. He has published almost 40 SCI papers, with over 700 Google Scholar citations. His research focuses on autonomous planning, control, and decision-making systems for unmanned surface vehicles (USVs), as well as control of complex nonlinear systems. He currently serves as a Youth Editorial Board Member for Chinese Journal of Ship Research and holds membership in two technical committees under the Chinese Institute of Command and Control (CICC): the Technical Committee on Intelligent Control and Systems, and the Technical Committee on Large-Scale Models and Decision Intelligence. |
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Junxiao Wang, Zhejiang University of Technology, China Junxiao Wang is an Associate Professor of the College of Information Engineering, Zhejiang University of Technology, Hangzhou, China. He received his B.S. in automation and M.S. degrees in control theory and control engineering from School of Information Engineering, Henan University of Science and Technology (HAUST), Luoyang, China in 2008 and 2011, respectively, and the Ph.D. degree in control theory and control engineering from School of Automation, Southeast University (SEU), Nanjing, China, in 2017. From Sep. 2015 to Sep. 2016, he was a visiting Ph.D. student in the Institute for Electrical Drive Systems and Power Electronics at Technical University of Munich (TUM) in Germany. He has published more than 20 IEEE journal papers. He was also the recipient of 2017 IET Premium Award for best paper in IET Control Theory & Applications (First Author) and the recipient of 2022 Best Paper Award in Control Theory and Technology (First Author). His research interests include generalized disturbance estimation based control and optimization for mechatronic servo drives and autonomous systems. He serves as an Associate Editor of International Journal of Electronics and Letters. |
Submission Guideline:
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Please choose Special Session: Advances in Robotics and Autonomous Systems for Complex Environments