Prof. Giancarlo Fortino (IEEE Fellow), University of Calabria, Italy 

Giancarlo Fortino (IEEE Fellow 2022) is Full Professor of Computer Engineering at the Dept. of Informatics, Modeling, Electronics, and Systems of the University of Calabria (Unical), Italy. He received a PhD in Computer Engineering from Unical in 2000. He is also distinguished professor at Wuhan University of Technology (China), high-end expert of many chinese universities, including Huazhong University of Science and Technology, South China University of Technology, Shanghai Maritime University, etc., senior research fellow at the Italian ICAR-CNR Institute, CAS PIFI Group international fellow at SIAT (Shenzhen), and Distinguished Lecturer for IEEE Sensors Council, SMC society, and IoT TC. He was also visiting researcher at ICSI, Berkeley (USA), in 1997 and 1999 and visiting professor at Queensland University of technology in 2009. At Unical, he is the chair of the PhD School in ICT, the director of the Postgraduate Master course in AI-driven Radiomics, and the director of the SPEME lab, the Radioamica lab, as well as co-chair of Joint labs on IoT established between Unical and WUT, SMU and HZAU Chinese universities, respectively, and a Joint lab on AI-driven Robotics established with indian istitutions. Fortino is currently the scientific responsible of the Unical group of the Italian CINI National Laboratory of Digital Health and of the Unical group of the CINI Cyber Humanities WG. He is Highly Cited Researcher 2020-2025 in Computer Science by Clarivate (the only Italian professor currently ranked). He had 25+ highly cited papers in WoS, and h-index=91 with 36K+ citations in Google Scholar. His research interests include wearable computing systems, e-Health, Internet of Things, and agent-based computing. He is author of 800+ papers in int’l journals, conferences and books. He is (founding) series editor of IEEE Press Book Series on Human-Machine Systems and EiC of Springer Internet of Things series and AE of premier int’l journals such as IEEE TASE (senior editor), IEEE TAFFC-CS, IEEE THMS, IEEE T-AI, IEEE SJ, IEEE JBHI, IEEE OJEMB, IEEE OJCS, Information Fusion, BDCC, etc. He chaired many int’l workshops and conferences (140+), was involved in a huge number of int’l conferences/workshops (800+) as IPC member, is/was guest-editor of many special issues (80+). He is cofounder and CEO of SenSysCal S.r.l., a Unical spinoff focused on innovative IoT systems, and recently cofounder and vice-CEO of the spin-off Bigtech S.r.l, focused on big data, AI and IoT technologies. Fortino is the VP of Cybernetics (term 2026-2027) of the IEEE SMCS, member of the IEEE SMCS ExCom, and former chair of the IEEE SMCS Italian Chapter.

Title: Generative Digital Twins: Principles, Architecture, Methodology, and Applications

Abstract: Digital Twins (DTs) are software replicas that not only mirrors physical entities but can also proactively predict, control, optimize and simulate their behavior. Born in the manufacturing sector, this concept after an initial hype stayed untouched for decades. The rise of Internet of Things (IoT) and Artificial Intelligence (AI) enabled DT, respectively, to exchange real-world data and to fully exploit it for fulfilling its own goals. Very recently, Generative AI (Gen-AI) methods started being sporadically applied to DT in different contexts and with different targets. In this talk, starting from our experiences on design, implementation and evaluation of DTs and, more recently, of Opportunistic DTs, we first provide a definition for the Generative DT (GDT) which embraces main distinctive aspects and potential of current and future Gen-Al-aided DTs. In particular, we disclose the role of Gen-AI in conciliating the model- and the data-driven approach for the development of DTs. Then, we analyze the added value of main Gen-AI architectures and development methodologies for maximizing the effectiveness and the performance of DTs operating in the IoT domain and deployed in the device-edge-cloud continuum. Finally, we illustrate the potential of GDT in emblematic use cases in the Smart City, Smart Manufacturing, Smart Water Systems, Smart Robotics, Smart Education and, more in general, in Smart IoT-driven domains.

Prof., Dr.  Michael Basin (IEEE Fellow), Autonomous University of Nuevo Leon, Mexico

Michael V. Basin received his Ph.D. degree in Physical and Mathematical Sciences with a major in Automatic Control and System Analysis from Moscow Aviation University (MAI) in 1992. He is currently Full Professor with Autonomous University of Nuevo Leon, Mexico. Starting from 1992, Dr. Basin published more than 400 research papers in international refereed journals and conference proceedings. He is the author of the monograph “New Trends in Optimal Filtering and Control for Polynomial and Time-Delay Systems,” published by Springer.  His works are cited 10K+ times (h index = 53).  Dr. Basin has supervised 17 doctoral and 11 master's theses. He has served as the Editor/Co-Editor-in-Chief of IEEE Transactions on Industrial Informatics and Journal of The Franklin Institute, Senior Editor of IEEE Transactions on Systems, Man and Cybernetics: Systems and IEEE/ASME Transactions on Mechatronics, an Associate Editor of IEEE Transactions on Fuzzy Systems, Automatica, Neural Networks, International Journal of Systems Science. Dr. Basin is an IEEE Fellow; he was awarded a title of Highly Cited Researcher by Thomson Reuters, the publisher of Science Citation Index, in 2009; he has received the Kimura Best Paper Award 2022 from the Asian Control Association; he is a regular member of the Mexican Academy of Sciences, and placed at 30,287 in the Stanford 2% list.  His research interests include variable structure systems and sliding mode control, fully actuated systems, nonlinear and robust control, stochastic systems, and applications to mechatronic, robotic, aerospace, and transportation systems. 

Title: From Finite- to Predefined-Time Convergent Control: Design and Applications to Unmanned Aerial Systems

Abstract: This talk presents a brief overview of developing finite-time convergent control algorithms from a conventional first-order sliding mode control to a predefined-time convergent continuous controller designed to stabilize a multi-dimensional system, using a scalar linear time-varying control input. For the permanent-magnet synchronous motor system, three cases are considered: disturbance-free, in presence of unmeasured states, and in presence of both unmeasured states and a deterministic disturbance satisfying a Lipschitz condition. Numerical simulations are conducted for a permanent-magnet synchronous motor system to validate the obtained theoretical results in each of the considered three cases. The simulation results demonstrate that the employed values of the predefined-time convergent control inputs are applicable in practice and verify the algorithm efficiency in each considered case. Then, the unknown moving object (UMO) fencing and monitoring problem is presented for a multi-agent system (MAS) of unmanned aerial vehicles (UAV). The MAS agents are able to perform unidirectional communication with its immediate neighbors, focusing on the target UMO that is moving with a variable and unknown velocity. Finally, hardware-in-the-loop (HIL) experiments validate practical viability of the proposed framework, showcasing its performance in realistic settings with real-time computational constraints.

张福民教授（IEEE会士及ASME会士），香港科技大学

Prof. Fumin Zhang (IEEE Fellow/ASME Fellow), Hong Kong University of Science and Technology, China

Dr. Fumin Zhang is Chair Professor at the Hong Kong University of Science and Technology. He received a PhD degree in 2004 from the University of Maryland (College Park) in Electrical Engineering and held a postdoctoral position in Princeton University from 2004 to 2007. His research interests include mobile sensor networks, maritime robotics, control systems, and theoretical foundations for cyber-physical systems. He received the NSF CAREER Award in September 2009 and the ONR Young Investigator Program Award in April 2010. He has served as associate editors for IEEE Transactions on Automatic Control, and IEEE Transactions on Control of Networked Systems, IEEE Journal of Oceanic Engineering, and International Journal of Robotics Research. He is currently serving as the co-chair for the IEEE RAS Technical Committee on Marine Robotics. He is an IEEE and ASME Fellow.

Title: Dynamics and Control of Miniature Autonomous Blimps

Abstract: Significant recent advances in unmanned aerial vehicles calls for convenient platforms to support experiments and demonstrations. Unmanned aerial vehicles such as quad-rotors and multi-copters have become popular for this purpose.  However, the indoor usage of these unmanned aerial vehicles (UAVs)  is limited by flight duration per battery charge (typically less than 20 minutes) and  safety concerns to humans sharing the same physical space.  Safety nets or cages provide protection, but sacrifice the potential for human robot interaction experiments. We develop the Miniature Autonomous Blimp (MAB) as flying vehicles for indoor experiments that support safe-interaction between human and robot swarm.  The MAB  has relatively long flight duration up to two hours per battery charge. Furthermore, the blimps are naturally cushioned and do not cause any pain when collide with human. It offers a fun experience that often encourage physical contacts with humans. We have developed vision-based feedback control laws that enable the MAB to detect and track humans. We will report recent progress made on the modeling and control of the MAB. Advanced control techniques achieve more stable flights and more agile motion that enable new applications for the MAB. We will demonstrate examples of human-robot interaction and human-swarm interaction based on MAB.

杨辰光教授（IEEE/IET/AAIA会士），香港理工大学

Prof. Chenguang Yang (IEEE/IET/AAIA Fellow), The Hong Kong Polytechnic University, China

杨辰光，香港理工大学电子计算学系教授，曾于英国利物浦大学、西英格兰大学和华南理工大学担任教授。担任多个国际权威学术组织会士或成员，包括电气电子工程师学会会士（IEEE Fellow）、英国工程技术学会会士（IET Fellow）、英国机械工程师学会会士（IMechE Fellow）、亚太人工智能学会会士（AAIA Fellow），同时为欧洲科学与艺术院成员（EASA Member）及国家人工智能研究院成员（NAAI Member），并为英国特许工程师（Chartered Engineer）。以第一作者先后于2012年和2022年获得“IEEE Transactions on Robotics”和“IEEE Transactions on Neural Networks and Learning Systems”最佳论文奖，并以第一完成人获2022年中国自动化学会自然科学一等奖。

题目：机器人仿人控制、技能学习和人机协作

摘要：报告人提出了仿人柔顺调节的安全接触控制方法，构建了力位耦合操作的技能学习泛化框架，探索了个性化协作策略的优化机理，开发了具备动态适应能力的人机协作技术。尤其针对力接触式任务中力位信息缺乏耦合表征、技能学习与底层控制相割裂的核心瓶颈，提出了涵盖运动、力控、刚度以及可操作度的多模式完整技能基元体系，建立了面向力位耦合操作的统一技能表征方法。报告人的工作将自适应控制融入技能学习算法，充分利用自适应控制补偿不确定性的能力，增强了未知新场景下的技能泛化能力。

Professor Chenguang Yang (Charlie) received the B.Eng. in Measurement and Control Technology from Northwestern Polytechnical University, China, and the Ph.D. in Adaptive and Neural Network Control from the National University of Singapore. He completed postdoctoral research in human robotics at Imperial College London, UK. Previously, he held professorships at University of Liverpool, University of the West of England (UWE Bristol) as well as South China University of Technology. He was leading the Robotics and Autonomous Systems Group at University of Liverpool and was leading the Robot Teleoperation Group at Bristol Robotics Laboratory. He holds fellowships with Institute of Electrical and Electronics Engineers (IEEE), Institute of Engineering and Technology (IET), Institution of Mechanical Engineers (IMechE), Aisa-Pacific AI Association (AAIA), and British Computer Society (BCS). He is a member of European Academy of Sciences and Arts (EASA) and a member of National Academy of Artificial Intelligence (NAAI). Professor Yang was selected as a Featured Author on IEEE Xplore in 2023. As lead author, he received the IEEE Transactions on Robotics Best Paper Award in 2012 and IEEE Transactions on Neural Networks and Learning Systems Outstanding Paper Award in 2022. He also was a lead contributor to the 1st Prize of Chinese Association of Automation (CAA) Natural Science Award in 2022. His current research focuses on embodied AI for robot learning, human robot interaction, and intelligent system design.

Title: Humam-like Robot Control, Learning and Human-robot Collaboration

Abstract: This presentation will provide a broad overview of my research in robotics with a focus on learning from Demonstration (LfD). LfD allows robots to acquire and generalize task skills through human demonstrations, creating a seamless integration of artificial intelligence and robotics. Most LfD approaches often overlook the importance of demonstrated forces and rely on manually configured impedance parameters. In response, my team has developed a series of biomimetic impedance and force controllers inspired by neuroscientific findings on motor control mechanisms in humans, enabling robots to imitate compliant manipulation skills. The presentation also covers collaborative control strategies for human-robot interaction, and long-horizon manipulation based on subgoal planning. Tactile sensing designs, soft gripper designs, perception and mapping methods, and advancements in dynamic SLAM and object tracking are also included. Particularly, we designed anthropomorphic visual tactile sensors that assess contact force, surface texture, and shape, to improve robot skill learning through enhanced perceptual capabilities.

王龙教授（长江学者），北京大学系统与控制研究中心主任

Prof. Long Wang, Peking University, China

王龙，出生于中国西安。1986年获清华大学自动控制专业学士学位，1992年获北京大学动力学与控制专业博士学位。他曾先后在加拿大多伦多大学（1993年，与Bruce A. Francis教授合作）和位于德国慕尼黑的德国航空航天中心（1995-1997年，与Juergen E. Ackermann教授合作）从事研究工作。现任动力学与控制领域长江学者特聘教授、北京大学系统与控制研究中心主任。主要研究领域包括复杂网络化系统、演化博弈动力学、人工智能和仿生机器人。王龙教授曾于1999年和2017年两次荣获国家自然科学奖，并获得关肇直控制理论奖、张嗣瀛决策与控制奖，以及《控制理论与应用》《中国科学：信息科学》等期刊的优秀论文奖。他已指导50余名博士研究生，并曾多次在中国控制会议、中国系统科学大会、中国智能自动化会议等重要学术会议上作大会报告。

题目：从演化博弈到综合集成智慧

摘要：群体智能源于个体之间的局部相互作用与适应性行为更新，表现为一种从分布式行为中涌现出的认知能力。演化博弈论为刻画从微观决策到宏观秩序的演进过程提供了统一的分析框架。本报告首先介绍博弈论的基本概念，指出完全理性假设的局限性，并阐述有限理性条件下的演化视角；随后讨论复制子方程以及结构化群体中的演化动力学。在此基础上，本报告将综述运用演化博弈论研究群体智能的两类代表性工作：第一类侧重于构建群体估计任务模型，以刻画群体智能的涌现机制；第二类侧重于激励机制设计，探讨如何通过调整支付结构，有效提升群体在预测任务中的整体表现。最后，本报告将总结演化博弈论在连接微观行为与宏观结果方面的优势，并展望未来的研究方向。

Long Wang was born in Xi'an, China. He received the bachelor's degree in automatic control from Tsinghua University in 1986, and Ph.D. degree in Dynamics and Control from Peking University in 1992. He has held research positions at the University of Toronto, Canada (1993, with Professor Bruce A. Francis), and the German Aerospace Center, Munich, Germany (1995-1997, with Professor Juergen E. Ackermann). He is currently the Cheung-Kong Chair Professor of Dynamics and Control, and the Director of Center for Systems and Control of Peking University. His research interests include complex networked systems, evolutionary game dynamics, artificial intelligence, and bio-mimetic robotics. Prof. Wang was the recipient of the National Science Prize (twice in 1999 and 2017), Guan Zhaozhi Control Theory Award, Zhang Siying Award in Decision and Control, and the Best Paper Awards for journal publications in Control Theory and Applications, Science China Information Sciences, etc. He has supervised more than 50 Ph.D. students. He has also given a number of plenary lectures at major conferences, including Chinese Control Conference, Chinese Conference on Systems Science, Chinese Conference on Intelligent Automation, etc.

Title: From Evolutionary Games to Meta-synthetic Wisdoms

Abstract: Collective intelligence arises from local interactions and adaptive behavioral updates among individuals, manifesting as an emergent cognitive capability derived from distributed behaviors. Evolutionary game theory provides a unified analytical framework for characterizing the transition from microscopic decision-making to macroscopic order. This talk first introduces the fundamental concepts of game theory, highlighting the limitations of the full rationality assumption and presenting the evolutionary perspective under bounded rationality. We then discuss replicator equations and evolutionary dynamics in structured populations. Building on this foundation, this talk surveys two representative lines of research that employ evolutionary game theory to study collective intelligence. The first focuses on constructing models of collective estimation tasks to characterize how such intelligence emerges. The second emphasizes incentive design, exploring how adjustments to payoff structures can effectively enhance group performance in prediction tasks. Finally, the talk will summarize the advantages of evolutionary game theory in integrating microscopic behavior with macroscopic outcomes and outline directions for future research.

王贺升教授（国家杰青），上海交通大学浦江国际学院院长

Prof. Hesheng Wang, Shanghai Jiao Tong University, China

王贺升博士，上海交通大学唐君远讲席教授，浦江国际学院院长。担任中国自动化学会混合智能专委会副主任, 中国仪器仪表学会智能车与机器人分会副主任。现/曾担任国际期刊TRO, TASE, RAL, IJHR, RIA的编委，TMECH高级编辑，Advanced Intelligent Systems的顾问编委，Robot Learning的主编。作为项目负责人，主持包括国家重点研发计划项目，国家自然科学基金杰出青年基金、优秀青年基金、联合基金重点等多个项目。获得第八届科学探索奖、全国宝钢优秀教师称号等。担任机器人顶会IROS 2025的大会主席以及RCAR 2016和ROBIO 2022的大会主席。

题目：机器人具身导航与操作

摘要：本报告聚焦服务机器人移动与操作两大核心功能，围绕复杂开放环境中的自主感知、智能决策与具身操作展开。首先概述服务机器人产业与技术发展现状及其在动态环境、长时序任务和复杂物理交互中面临的关键挑战，随后介绍团队在移动与操作方面的主要研究进展。在移动方面，面向复杂大场景与动态环境，研究多模态融合感知、鲁棒定位、智能导航与场景记忆方法，提升机器人持续自主移动与前瞻决策能力；在操作方面，结合多模态大模型与世界模型，研究任务理解、环境状态表征、长时序规划与闭环执行，使机器人由预设动作执行向理解任务—自主决策—智能操作演进，并结合人类意图预测提升共融环境适应能力。最终形成面向真实复杂场景的移动与操作一体化智能技术体系，推动服务机器人向具身智能自主作业发展。

Dr. Hesheng Wang is a Tang Junyuan Chair Professor at Shanghai Jiao Tong University and Dean of Pujiang International College. He currently serves as Vice Chair of the Hybrid Intelligence Technical Committee of the Chinese Association of Automation and Vice Chair of the Intelligent Vehicles and Robotics Branch of the China Instrument and Control Society. He has served or currently serves on the editorial boards of TRO, TASE, RAL, IJHR, and RAS. He is also a Senior Editor of TMECH, an Advisory Board Member of Advanced Intelligent Systems, and Editor-in-Chief of Robot Learning. As principal investigator, he has led a number of major research projects, including projects under the National Key R&D Program of China, the National Science Fund for Distinguished Young Scholars, the National Science Fund for Excellent Young Scholars, and Key Projects of the NSFC Joint Funds. He is a recipient of the 8th XPLORER prize, the Baosteel Excellent Teacher Award, etc. He served as General Chair of IEEE/RSJ IROS 2025, as well as General Chair of IEEE RCAR 2016 and IEEE ROBIO 2022.

Title: Embodied Intelligence for Robotic Navigation and Manipulation

Abstract: This report focuses on the two core functions of service robots, mobility and manipulation, and discusses autonomous perception, intelligent decision-making, and embodied manipulation in complex and open environments. It first provides an overview of the current development of the service robotics industry and related technologies, as well as the key challenges posed by dynamic environments, long-horizon tasks, and complex physical interactions. It then presents the team’s major research progress in mobility and manipulation. For mobility, the research addresses large-scale and dynamic environments through multimodal perception fusion, robust localization, intelligent navigation, and scene memory, aiming to enhance robots’ capabilities for persistent autonomous mobility and anticipatory decision-making. For manipulation, multimodal large models and world models are integrated to investigate task understanding, environment state representation, long-horizon planning, and closed-loop execution, enabling robots to evolve from executing predefined actions toward task understanding, autonomous decision-making, and intelligent manipulation. Human intention prediction is also incorporated to improve adaptability in shared human–robot environments. Ultimately, the work aims to establish an integrated intelligent framework for mobility and manipulation in real-world complex scenarios, advancing service robots toward embodied-intelligence-enabled autonomous operation.

韩光洁教授，河海大学信息科学与工程学院院长

Prof. Guangjie Han, Hohai University, China

韩光洁，博士、二级教授/博导，河海大学信息科学与工程学院院长，IEEE/IET/AAIA Fellow。主要研究方向为水声通信与组网、工业物联网、人工智能、网络与安全等。近年来在IEEE JSAC, IEEE TMC, IEEE TPDS, IEEE TON等国际期刊上发表高水平SCI期刊论文300+篇（其中IEEE/ACM Trans. 系列汇刊160+篇），到目前为止Google Scholar引用次数25500+次，H-index为83。主持包括国家重点研发计划和国家自然科学基金重点项目等省部级以上项目共30余项，获得省部级以上奖励10项，授权国内外发明专利144件。连续7年（2019-2025）入选全球排名前2%的科学家榜单，连续6年（2020-2025）入选爱思唯尔中国高被引学者榜单，2025年入选Scholar GPS全球Top 0.05%顶尖科学家榜单。目前任10多种国际期刊（其中包括 IEEE TII, IEEE TCCN, IEEE TVT, IEEE Systems）编委。

题目：水声传感器网络中多维度动态信任管理机制

摘要：水下声学传感器网络（UASN）是实现“智能海洋”概念的关键组件，但在复杂的水生环境中，其潜力尚未得到充分利用。主要挑战在于缺乏有效方法来确保UASN的安全性和可靠的数据传输。本报告介绍了我们团队针对UASN的信任管理机制的研究。我们的主要研究领域包括：1) 基于能量预测模型的入侵检测算法；2) 基于模糊理论的多维信任计算算法；3) 基于云理论的信任评估算法；以及 4) 基于机器学习的信任预测算法。这些研究成果对于推进UASN的安全技术和应用具有重大的理论和实践意义。

Guangjie Han is a professor, currently serving as the Dean of the School of Information Science and Engineering at Hohai University. He is an IEEE Fellow, IET/IEE Fellow, and AAIA Fellow. His main research interests include smart oceans, industrial IoT, artificial intelligence, networks, and security. In recent years, he has published more than 300 high-level SCI journal papers, including over 160 papers in the IEEE/ACM Trans. series, in international journals such as IEEE JSAC, IEEE TMC, IEEE TPDS, and IEEE TCC. His publications have been cited over 25000 times on Google Scholar, with an H-index of 83. He has authored three monographs and translated one book. He has led more than 30 provincial and ministerial-level research projects, including national key R&D programs and national natural science foundation key projects. He has been granted 130 national invention patents and 6 PCT international authorized patents. He has received numerous awards, including the second prize of the China Business Federation Science and Technology Award, the third prize of the Jiangsu Provincial Science and Technology Award, the second prize of the Liaoning Provincial Science and Technology Progress Award, and the Best Paper Award of the IEEE Systems Journal in 2020. For seven consecutive years (2019-2025), he has been listed as one of the top 2% of scientists globally, as well as for the Chinese Highly Cited Researchers list for six consecutive years (2020-2025). Currently, he serves as an associate editor for more than ten international journals, including IEEE TII, IEEE TCCN, IEEE TVT and IEEE Systems. 

Title: Multi-Dimensional Dynamic Trust Management Mechanism in Underwater Acoustic Sensor Networks

Abstract: The underwater acoustic sensor network (UASN) is a pivotal component in realizing the concept of a "smart ocean." However, its potential remains underutilized in complex aquatic environments. The primary challenge lies in the absence of effective methods to ensure UASN security and reliable data transmission. This report presents our team's research on the trust management mechanisms for UASNs. Our main research areas include: 1) an intrusion detection algorithm based on energy prediction model; 2) a multi-dimensional trust calculation algorithm grounded in fuzzy theory; 3) a trust evaluation algorithm utilizing cloud theory; and 4) a trust prediction algorithm driven by machine learning. These research outcomes hold significant theoretical and practical implications for advancing the security technologies and applications of UASNs.

黄海教授（国家高层次人才），哈尔滨工程大学

Prof. Hai Huang, Harbin Engineering University, China

黄海，博士，哈尔滨工程大学教授、博导，国家高层次人才，雄安新区科技创新专项总体组专家。长年从事水下机器人自主探测/作业、遥控水下机器人等方面的研究，国家重点研发计划项目应用示范类项目首席，科技委基础加强重点项目首席，主持国家自然科学基金重点和联合基金重点项目各1项，基础科研项目等20余项。发表论文90余篇，授权发明专利30余项；第一作者出版中文著作三部，第一作者获得国家出版基金，国家级规划教材，国家一流虚拟仿真实验课程各一项。以第一完成人获中国造船工程学会科技进步一等奖，黑龙江省科技进步二等奖，全国机器人专利创业大赛一等奖；作为第二完成人获得上海市科技进步二等奖，电子学会二等奖等。

题目：水下机器人智能处置与协同作业技术

摘要：海洋资源丰富，开发潜力巨大，海底缆线是跨海电能、信息等资源传输的“大动脉和生命线”，一旦出现故障，将直接影响整个海上风电、海底观测网和海底油气田等国家重大工程的安全运行。水下机器人作为海洋船舶与智能机器人结合的交叉领域，近年来得到了广泛的重视和快速发展，其智能探测与处置作业是海洋资源勘探和海底设施巡检维护的重要手段。报告主要围绕水下机器人等无人系统的海底智能巡检探测展开汇报，针对水下机器人在海洋所能接收的外部环境信息和通信受限，处置作业所面临的艇-臂-手多冗余自由度水下协同运动等问题，介绍水下机器人智能处置作业的最新进展，探讨水下具身智能的方案与可行性，并以海底设施的巡检作业为例汇报团队在智能处置和协同作业方面的研究进展。

Hai Huang, Ph.D., is a Professor and doctoral supervisor at Harbin Engineering University, a national high-level talent, and an expert of the general group for Xiong’an New Area science and technology innovation special projects. He has long been engaged in research on autonomous detection and operation of underwater robots, remotely operated underwater vehicles (ROVs) and related technologies. He serves as the chief scientist of the application demonstration project of the National Key R&D Program and the key project of the Basic Strengthening Program of the Science and Technology Commission. He has presided over 1 key project and 1 joint key project of the National Natural Science Foundation of China, as well as more than 20 basic research projects and other provincial and ministerial level projects. He has published more than 90 academic papers and granted over 30 invention patents. As the first author, he has published 3 Chinese monographs, and won the National Publishing Fund, national planning textbook, and national first-class virtual simulation experiment course respectively. As the first completed person, he won the first prize of Science and Technology Progress Award of the Chinese Society of Naval Architecture and Marine Engineering, the second prize of Heilongjiang Provincial Science and Technology Progress Award, and the first prize of the National Robot Patent Entrepreneurship Competition. As the second completed person, he was awarded the second prize of Shanghai Municipal Science and Technology Progress Award, the second prize of China Institute of Electronics and other provincial and ministerial awards.

Title: Intelligent Manipulation and Cooperative Operation Technology of Underwater Vehicles

Abstract: Marine resources are abundant with huge development potential. Subsea cables serve as the “major arteries and lifelines” for the transmission of cross‑sea electric power and information. Once a fault occurs, it will directly endanger the safe operation of major national projects including offshore wind farms, seafloor observation networks and subsea oil‑gas fields. As an interdisciplinary field combining marine engineering and intelligent robotics, underwater robots have attracted extensive attention and achieved rapid development in recent years. Their intelligent detection and intervention capabilities constitute a vital technical means for marine resource exploration and the inspection‑maintenance of subsea facilities. This report will focus on subsea intelligent inspection and detection based on underwater unmanned systems. Aiming at practical bottlenecks of underwater robots, including limited accessible environmental information, constrained underwater communication, and the challenges of coordinated motion control for the vehicle‑manipulator‑end‑effector system with multiple redundant degrees of freedom, it will review state‑of‑the‑art advances in intelligent intervention operations of underwater robots. The schemes and engineering feasibility of underwater embodied intelligence will be discussed. Taking subsea‑facility inspection as a typical scenario, the research progress of our team in intelligent intervention and cooperative operations will be presented.