王郸维，新加坡南洋理工大学电子与电机工程学院教授，新加坡工程院院士，IEEE Fellow，德国洪堡Fellow. 1989年于美国密歇根大学安娜堡分校获博士学位。自2019年以来，担任IEEE IROS 主编. 他发表了6本英文专著，7个书中章节，9个专利和500多篇国际杂志和会议论文。王教授涉及的研究领域包括机器人和力控制，先进控制系统设计，智能系统，学习控制，移动机器人，移动机器人路径和轨迹控制，卫星编队飞行和故障容错姿态控制，复杂系统的故障诊断和预测，交通灯控制等方面。至2023年2月，王教授的论文被科学引文索引（SCI）引用次数超过8800次以及在谷歌学术数据库引用超过17，000次。

Prof. Danwei Wang is Fellow, Academy of Engineering Singapore, Fellow of IEEE, Fellow, AvH (Germany) and recipient of the First-Class Award of Shanghai Science and Technology. He received his Ph.D degree from the University of Michigan, Ann Arbor, USA. Currently, he is a professor at School of Electrical and Electronic Engineering, NTU. He is Editor, IEEE IROS (International Conference on Intelligent Robotics and Systems) since 2019. He has published 6 books, 7 book chapters, 9 patents and over 500 technical papers and articles in international refereed journals and conferences. SCI citations to his papers amount 8800+ as of Feb 2023 and Google Scholar citations are well over 17,000. He also set up a spin-off company to commercialize his research results in the area of sensing systems and autonomous systems.

Title: Robust Perception for Intelligent Systems

Abstract: Perception is a key module to any intelligent systems which operate in outdoor environment. The perception must be robust and consistent in all adverse weather conditions. Outdoor applications and industries require intelligent and autonomous systems to work continuously under changing lighting and weather conditions. Robust perception capability enables the decision making and execution at various levels and loops of a complex intelligent autonomous systems. This talk presents some achievements with common sensors, such as LiDAR and cameras, as well as multi-modal sensing and perception for reliable and robust outdoor scenarios. The combination of different sensors will lead to 3D digital twin, seamless wide FOV video streams and robust perception in complex outdoor natural environment and adverse weather conditions.

孙富春，清华大学计算机科学与技术系教授，博士生导师，IEEE/CAAI/CAA Fellow, 国家杰出青年基金获得者；兼任清华大学校学术委员会委员，计算机科学与技术系长聘教授委员会副主任，清华大学人工智能研究院智能机器人中心主任。兼任担任国家重点研发计划机器人总体专家组成员，中国人工智能学会副理事长，中国自动化学会和中国认知科学学会常务理事，中国计算机学会智能机器人专委会主任。兼任国际刊物《Cognitive Computation and Systems》和《AI and Autonomous  Systems》主编，《CAAI Artificial Intelligence Research》执行主编，国际刊物《IEEE Trans. on Cognitive and Development Systems》，《IEEE Trans. on Fuzzy Systems》和《International Journal of Control, Automation, and Systems (IJCAS)》副主编或领域主编，刊物《Journal of Biomimetic Intelligence and Robotics》和《Robots and Autonomous Systems》编委。主要从事无人系统的主动感知、跨模态学习和机器人灵巧操作方面的研究。成果获得2003年韩国Choon-Gang国际学术一等奖、2016年吴文俊智能科学创新和进步一等奖。论文获得IEEE Trans. on IM 2017年Andy Chi最佳论文奖，专著入选2018年Springer中国学者最具影响力出版物榜单。发表SCI论文193篇，SCI他引近万次，Google引用21359，H指数65。

Dr. Fuchun Sun is a full professor of Department of Computer Science and Technology and President of Academic Committee of the Department, Tsinghua University, deputy director of State Key Lab. of Intelligent Technology & Systems, Beijing, China. He also serves as Vice president of China Artificial Intelligence Society and executive director of China Automation Society. His research interests include robotic perception and intelligent control. He has won the Champion of Autonomous Grasp Challenges in IROS2016 and IROS 2019. Dr. Sun is the recipient of the excellent Doctoral Dissertation Prize of China in 2000 by MOE of China and the Choon-Gang Academic Award by Korea in 2003, and was recognized as a Distinguished Young Scholar in 2006 by the Natural Science Foundation of China. He is elected as IEEE Fellow and CAAI Fellow in 2019, CAA Fellow in 2020.He served as an associated editor of IEEE Trans. on Neural Networks during 2006-2010, IEEE Trans. On Fuzzy Systems during 2011-2018, IEEE Trans. on Cognitive and Development since 2018 and IEEE Trans. on Systems, Man and Cybernetics: Systems since 2015. He was invited to make a plenary talk or keynote speech at the international summit ICRA2021, IROS2019, AAAI2021, etc.

题目：无人系统的多模态协同感知与行为学习

摘要：针对现有无人系统感知能力低，缺少感知与行为的协同，特别是对新目标和新场景的认知能力，难以适应开放和对抗环境下场景感知与理解，本报告提出了一种多无人机协同的多模态联合感知方法，包括开放环境下的无人机行为协同感知、目标定位跟踪、有价值目标的检测与零样本识别；此次，介绍了多无人机多模态感知的融合理论方法，报告单无人机的同构模态融合和多无人机的异构模态融合方法。接着介绍了级联无人机的协同感知与行为学习。最后，总结展望了无人系统感知与行为学习的的发展趋势。

Title: Multimodal Collaborative Perception and Behavior Learning for Unmanned Systems

Abstract: In response to the low perception ability of existing unmanned systems and the lack of collaborative perception and behavior learning, especially the cognitive ability to new targets and scenes, which makes it difficult to adapt to scene perception and understanding in open and adversarial environments, this report proposes a multimodal joint perception method for multi drone collaboration, including collaborative perception of drone behavior in open environments, target positioning and tracking, detection of valuable targets, and zero shot recognition. Then, the fusion theory and method of multimodal perception for multiple unmanned aerial vehicles are developed, and reported on the homomorphic mode fusion of single unmanned aerial vehicles and the heterogeneous mode fusion method of multiple unmanned aerial vehicles. Subsequently, the collaborative perception and behavior learning of cascaded drones were reported. Finally, the development trend of perception and behavior learning for unmanned systems is summarized and prospected.

Mahdi Jalili received his PhD in Computer and Communication Sciences from Swiss Federal Institute of Technology in Lausanne (EPFL) in 2009. He is currently a Professor of AI and Electrical Engineering and Director of EV Living Lab at RMIT University, Melbourne Australia. He was an Australia Research Council DECRA Fellow and RMIT VC Research Fellow. His research interests are in complex systems and networks, energy analytics and applications of machine learning in sustainable energy systems. He has received several recognitions and research awards including 2022 RMIT Research Excellence Award and the Neville Thiele Eminence Award (2021), the most prestigious prize of ITEE College of Engineers Australia, the peak engineering body in Australia. He is a Senior Member of IEEE and Fellow of Engineers Australia.

Title: Resilient Control of Networked Systems

Abstract: Consensus in a network of interconnected nodes has a wide range of applications, including in cooperative control of connected autonomous vehicles, distributed control of power grids, and coordinated swarm of drones and robots. The control algorithm in these applications is usually considered to be distributed, meaning the nodes only use local information obtained from their neighbours to update their control input. The objective is typically for the state of all nodes to converge to a common value, which is agreed upon along the way. However, some of the nodes might not behave as expected due to technical failures, cyberattacks, or other unexpected events. Resilient consensus is the ability of the normal nodes to withstand such misbehaviours and reach an agreement among themselves. This talk will focus on resilient consensus algorithms. The state-of-the-art will be discussed and a number of novel resilient algorithms will be presented.

刘宏，北京大学教授，国家“万人计划”首批科技创新领军人才，科技部国家重点研发计划“智能机器人”总体专家组专家，中国人工智能学会副理事长。长期从事计算机视觉与智能机器人、机器学习与智能人机交互等领域的教学和科研工作。先后承担 20余项国家级重要科研项目，发表学术论文300多篇。近年的研究成果被国内外同行研究机构引用9000余次，申报/获得国家发明专利40余项。获国家航天科技进步奖、吴文俊人工智能科学技术奖、日内瓦国际发明博览会奖、北京大学教学优秀奖等荣誉。刘宏教授长期专注“机器人视觉感知与自主学习”领域，是我国“智能科学与技术”新学科建设的积极倡导者、智能机器人领域科技创新的潜心实践者。

Hong Liu is a full professor at Peking University, primarily focusing on research in robot vision. Professor Liu is one of the first batch of leading talents in scientific and technological innovation of the National “Ten Thousand Talents Program”, a member of the expert group of “Intelligent Robot” of the National Key Research and Development Program of the Ministry of Science and Technology, the vice president of the Chinese Society for Artificial Intelligence. Prof. Liu has long been engaged in teaching and scientific research in the fields of computer vision and intelligent robots, machine learning and intelligent human robot interaction. He has undertaken more than 20 national-level important scientific research projects and has published more than 300 academic papers. In recent years, his research results have been cited more than 9,000 times by domestic and foreign research institutions, and he has applied for/acquired more than 40 national invention patents. He has won the National Aerospace Science and Technology Progress Award, Wu Wenjun Artificial Intelligence Science and Technology Award, Geneva International Invention Fair Award, Peking University Teaching Excellence Award, etc. Prof. Liu has long been dedicated to the field of “Robot Visual Perception and Autonomous Learning”. He is an active advocate of the new discipline construction of “Intelligent Science and Technology” in our country and a dedicated practitioner in the field of technological innovation in intelligent robotics.

题目：面向复杂场景的人体运动感知

摘要：人体运动感知在智能监控、人机交互、服务机器人和自主无人系统等领域具有广泛而重要的应用价值。复杂场景下的人体运动感知受到人体姿态与外观变化、环境照明及噪声干扰、人机物之间空间关系随时变化等多种因素的挑战。报告针对上述问题，提出一种基于多元可信架构建立通用视觉特征自主学习的新机制，全面系统地总结人体目标跟踪、人体姿态估计和人体行为识别等领域的相关工作和本团队的最新成果。通过智能导购机器人系统，展示了上述学术成果的应用系统集成。

Title: Human Motion Perception for Complex Scenes

Abstract: Human motion perception has extensive and significant application value in the fields of intelligent surveillance, human-computer interaction, service robots and autonomous unmanned systems. In complex scenes, human motion perception confronts challenges from a variety of factors, such as variations in human pose and appearance, ambient light and noise interference, and dynamic spatial relationships among humans, machines, and objects. This report addresses these challenges by introducing a novel general feature learning mechanism for robot vision based on Trusted Multiplex Networks. It comprehensively and systematically reviews the related work and the latest achievements of our team in the fields of human target tracking, human pose estimation and human behavior recognition. The application system integration of the above academic achievements has been demonstrated through an intelligent shopping guide robot system we developed.

陈霸东，西安交通大学人工智能与机器人研究所教授，教育部长江学者特聘教授。2008年毕业于清华大学计算机科学与技术专业获博士学位。研究领域包含信号处理、机器学习、人工智能、脑机接口、类脑计算、机器人等。在国际知名期刊及会议发表学术论文300余篇，论文被引用1万余次（H因子55）。获授权国家发明专利20余件，出版英文学术专著4部。入选世界排名前2%科学家名单和爱思唯尔中国高被引学者榜单。获教育部自然科学一等奖、中国自动化学会自然科学一等奖、中国自动化学会青年科学家奖等。担任中国认知科学学会理事、IEEE汇刊TNNLS/TCDS/TCSVT编委。主持了国家自然科学基金重大研究计划重点支持项目、联合基金重点项目、973计划课题、国家重点研发计划课题等科研项目。

Badong Chen received the Ph.D. degree in Computer Science and Technology from Tsinghua University, Beijing, China, in 2008. He is currently a professor with the Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China. His research interests are in machine learning, artificial intelligence, brain computer interfaces and robotics. He has authored or coauthored over 300 articles in various journals and conference proceedings (with h-index 55), and has won the 2022 Outstanding Paper Award of IEEE TCDS. Dr. Chen serves as a Member of the Machine Learning for Signal Processing Technical Committee of the IEEE Signal Processing Society, and serves (or has served) as an Associate Editor for several international journals including IEEE TNNLS, IEEE TCDS, IEEE TCSVT, Neural Networks and Journal of The Franklin Institute. He has served as a PC or SPC Member for prestigious conferences including UAI, IJCAI and AAAI, and served as a General Co-Chair of 2022 IEEE International Workshop on Machine Learning for Signal Processing.

题目：信息论学习

摘要：信息论在机器学习和信号处理领域获得广泛应用并引起越来越多研究者的关注。针对不同的机器学习问题提出了各种信息论学习方法，如监督学习中的最小误差熵（Minimum Error Entropy）准则和表示学习中的信息瓶颈（Information Bottleneck）原则。本报告介绍信息论学习的基本原理和范式，阐述新的学习准则与算法，并探讨信息论学习在脑机接口及脑疾病诊断中的应用。

Title: Information Theoretic Learning

Abstract: Information theory has attracted increasing attention in the fields of machine learning and signal processing in recent years. Novel information theoretic approaches have been proposed for different learning problems, such as error entropy estimator for supervised learning with the minimum error entropy (MEE) criterion, and mutual information estimator for representation learning with the information bottleneck (IB) principle. In general, information theoretic quantities can capture higher-order statistics and offer potentially significant performance improvement in the adaptation of linear and nonlinear models. This talk introduces the basic principles and paradigms of information theoretic learning (ITL), elaborates on new learning criteria and algorithms, and explores the applications in brain computer interfaces and brain disease diagnosis.

徐昕，国防科技大学智能科学学院教授，博士生导师，国家杰出青年科学基金获得者。主要从事智能无人系统的自主控制与机器学习等方面研究，获国家自然科学二等奖1项、湖南省自然科学一等奖2项，作为学术带头人之一获湖南省科技创新团队奖1项。主持国家自然科学基金重点项目2项、国家重点研发计划项目课题、973项目课题、装备预研项目等20余项。任中国自动化学会自适应动态规划与强化学习专业委员会副主任、平行控制与管理专业委员会副主任、机器人智能专业委员会顾问委员，中国指挥与控制学会无人系统专业委员会副主任。出版专著2部，发表SCI论文100余篇，代表性论文发表在IEEE TNNLS, J. AI Research, J of Field Robotics, IEEE TSMC:Systems, IEEE TPAMI, IEEE TCST, IEEE TITS等期刊。任IEEE Transactions on SMC: Systems, IEEE Trans. Intelligent Vehicles, Information Sciences, International Journal of Robotics and Automation、IET Cyber-systems and Robotics等国际期刊的Associate Editor，CAAI Transactions on Intelligence Technology副主编以及《控制理论与应用》编委。

Prof. Xin Xu received the B.S. degree in electrical engineering from the Department of Automatic Control, National University of Defense Technology (NUDT), Changsha, P. R. China, in 1996 and the Ph.D. degree in control science and engineering from the College of Mechatronics and Automation (CMA), NUDT. Currently, he is a full professor with the College of Intelligence Science and Technology, National University of Defense Technology, Changsha, P.R. China. Prof. Xu’s main research fields include machine learning and autonomous control of robots and intelligent unmanned systems. He received the Distinguished Young Scholars’ Funds of National Natural Science Foundation of China. He is one of the recipients of the second-class National Natural Science Award of China and 2 first-class Natural Science Awards of Hunan Province, China. He has published 2 monographs and more than 200 papers. He is a senior member of IEEE and an associate editor of IEEE Transactions on System, Man and Cybernetics: Systems, IEEE Transactions on Intelligent Vehicles, Information Sciences, International Journal of Robotics and Automation, associate Editor-in-Chief of CAAI transactions on Intelligence Technology, and an Editorial Board Member of the Journal of Control Theory and Applications.

题目：无人系统自主学习理论与技术研究进展

摘要：随着工业、医疗、国防等领域对各类机器人和无人系统应用需求的增加，需要研究和探索复杂不确定环境中无人系统智能感知与优化控制的高效自主学习理论和方法，减少对人工标记样本或者实际交互数据的依赖。报告在分析相关技术需求的基础上，介绍了无人系统目标识别的鲁棒半监督学习、三维对称性预测的弱监督学习、正则化强化学习、在线学习预测控制、迁移强化学习的研究进展，以及在仿生机器人抓取、智能车辆优化控制中应用的若干研究进展。最后对进一步的工作进行了分析和展望。

Title: Advances in Autonomous Learning Theory and Techniques for Unmanned Systems

Abstract: The demands for applications of intelligent robots and unmanned systems in industry, medical services, and national defense are increasing in recent years. This talk analyzes the research challenges in sensing, decision-making and control of unmanned systems and intelligent robots. In order to deal with the above problems, sample efficient autonomous learning methods are introduced from several perspectives. Firstly, semi-supervised learning and weak-supervised learning algorithms are developed for object recognition and 3-D sensing of robots. Secondly, some recent advances in transfer reinforcement learning and model-based reinforcement learning are discussed for robot learning with high data efficiency. Application examples of autonomous learning in robot grasping and intelligent vehicles will also be presented. Future research works on open topics will also be discussed.

鲁仁全，广东工业大学教授，博士生导师，长江特聘教授、万人计划领军人才，国家杰出青年基金项目获得者，国家自然科学基金委创新群体负责人、科技部重点领域创新团队带头人、科技部创新人才培养基地负责人、教育部工程研究中心负责人，广东省本土创新团队负责人、广东省重点实验室主任，享受国务院特殊津贴，担任中国自动化学会监事。荣获教育部自然科学一等奖（两项）、广东省自然科学一等奖和广东省科学进步一等奖各一项，浙江省科学技术奖一等奖一项，教育部科学技术进步奖二等奖、中国纺织工业协会科学技术进步奖二等奖等奖励。主要研究工作包括变结构无人自主系统，网络化系统控制理论和应用、协同控制及量化反馈系统的鲁棒控制。至今为止，发表SCI论文300余篇，包括Automatica论文15篇，IEEE汇刊论文60余篇，SCI他引7600余次，高被引论文28篇。

Prof. Renquan Lu is the Distinguished Professor of the "Changjiang Scholar Program", the Leading Talent of the National High-level Personnel Special Support Program (the “Ten Thousand Talents Program”), and the recipient of The National Science Fund for Distinguished Young Scholars. He is the leader of the Creative Research Groups of the National Natural Science Foundation of China, the principal of the Innovation Team of the Ministry of Science and Technology, the Engineering Research Center of the Ministry of Education, and the leader of the Local Innovation and Entrepreneurship Team of "Guangdong Special Support Program".  In addition, Prof. LU also serves as the director the Key Laboratories of Guangdong Province, and a supervisor of the Chinese Society of Automation and enjoys the State Council's Special Allowance.  Prof. LU has won the first prize of Natural Science of the Ministry of Education (twice), the first prize of Natural Science of Guangdong Province, the first prize of Scientific Progress of Guangdong Province, the first prize of Science and Technology Award of Zhejiang Province, the second prize of Science and Technology Progress Award of the Ministry of Education, and the second prize of Science and Technology Progress Award of China Textile Industry Association. His main research area covers unmanned autonomous systems with variable structures, control theory and application of networked systems, and cooperative control and robust control of quantized feedback systems. To date, he has published more than 300 SCI papers, including 15 papers in Automatica, more than 60 papers in IEEE transactions.  He has more than 7600 SCI citations, and 28 highly cited papers.

题目：变结构无人自主系统的理论与方法

摘要：本报告阐述了机构突变，环境突变，网络拓扑突变对无人自主系统的运动性能的影响，提出了变结构无人自主系统姿态控制，减振控制，协同控制理论和方法，通过自主研发的水空两栖无人机，陆空两栖无人机，载人无人机等变结构无人载体验证了所提出方法的有效性与实用性。

Titile: Theory and Methods for Unmanned Autonomous Systems with Variable Structures

Abstract: This report discusses the dynamics performance of unmanned autonomous systems (UASs) under abrupt changes including structure changes, environment changes and network topology changes. The theories and techniques are proposed for attitude control, vibration control and cooperative control of UASs with variable structures. The effectiveness and practicability of the proposed methods are verified on the self-developed unmanned vehicles with variable structure, such as aquatic-aerial amphibious unmanned aerial vehicle (UAV), terrestrial -aerial amphibious UAV, and human-carrying UAV.

兰旭光，西安交通大学教授，博士生导师，国家杰出青年科学基金获得者。2005年12月在西安交通大学模式识别与智能系统专业获得工学博士学位。研究领域为计算机视觉、机器人学习及人机共融协作等。担任中国自动化学会共融机器人专委会主任委员，中国认知科学学会理事、副秘书长，人工智能学会“认知系统与信息处理”专委会副主任委员，仿真学会“智能无人系统建模仿真”专委会副主任委员。在人工智能与机器人领域的著名期刊和会议上如IEEE Trans和ICML/CVPR/RSS等发表论文100余篇。主持国家杰出青年基金、基金重点、国家科技重大专项、科技创新2030人工智能重大项目等科研项目10余项。曾担任IEEE CYBER2019 和ICIRA2021大会联合程序主席，IEEE RCAR2023大会主席，IEEE 高级会员。

Xuguang Lan received Ph.D. degree in Pattern Recognition and Intelligent System from Xi'an Jiaotong University in 2005. He was a visiting scholar of Northwestern University from Sep. 2013 to Oct. 2014, and Ecole Centrale de Lyon from May. 2005 to Sep. 2005. Currently, he is a professor and  vice dean of the College of Artificial Intelligence and Robotics in Xi’an Jiaotong University. He has published more than 100 papers in journals and conferences, and more than 10 national invention patents. We gratefully acknowledge the support of National Natural Science Foundation for Distinguished Young Scholars, etc. He is the director of the Trico-Robot Committee of the Chinese Society of Automation, and a member of a council and deputy secretary-general of the Chinese Society of Cognitive Sciences, etc. He serves as the associate editor of the journal IET "Cognitive Computation and Systems", "Pattern Recognition and Artificial Intelligence", etc. He was one of Program Co-Chairs of IEEE CYBER 2019 and ICIRA2021. He is a senior member of IEEE.

题目：大模型时代机器人自主作业的挑战：行为智能的因果推理与学习

摘要：报告简要介绍人工智能大模型方面的进展，包括自然语言处理，视觉与语言交互以及分割模型等，特别是机器人在行为智能方面的进展和面临的挑战，提出了非结构场景基于视觉推理的机器人自主作业方法，在部分可观测场景下，将学习与规划进行交互迭代，使得机器人能够对动态非结构场景进行视觉推理，以最优的方式完成特定物体的自主作业；进一步提出了基于信赖域策略优化的动态交互的机器学习方法、基于贪婪值分解和有向图意图传播的多机器人自主协同方法及相关应用。

Title: Challenges for Autonomous Robot Operations in the Era of Large AI Models: Causal Reasoning and Learning in Behavioral Intelligence

Abstract: This talk provides a brief overview of advancements in the field of AI Foundation models, such as natural language processing, visual and language interaction, and segmentation models, specifically highlighting the progress and challenges in the realm of behavioral intelligence of robots. We proposes an autonomous operation method for robots based on visual reasoning in unstructured environments. In partially observable scenarios, it facilitates an iterative interaction between learning and planning, enabling the robot to engage in visual reasoning for dynamic unstructured scenes and accomplish specific object-related tasks optimally. Furthermore, we presents a machine learning method for dynamic interactions based on trust-region policy optimization, multi-robot autonomous collaboration using greedy value decomposition and directed graph intent propagation, along with their relevant applications.

邓方，北京理工大学基础科学研究院院长，特聘教授，博导。主要从事自主智能系统、可穿戴泛在系统等研究。承担国家自科基金重点项目、科技创新2030重大项目等项目多项。发表学术论文140余篇，授权发明专利108件，出版专著、教材各1部。国家杰出青年科学基金获得者，入选中组部青年拔尖人才、北京市科技新星。获中国青年科技奖、北京市科技奖杰出青年中关村奖、中国自动化学会青年科学家奖、国家科技进步奖二等奖、日内瓦国际发明展金奖、国家教学成果二等奖、北京市教学成果一、二等奖、CAA高等教育教学成果奖一等奖各1项，获省部级科技奖、优秀论文奖多项。担任中国自动化学会副秘书长、青工委副主任、中国人工智能学会、中国指挥与控制学会和中国自动化学会多个专业委员会委员，IEEE TSMCS、TIV、自动化学报、指挥与控制学报等编委。

Fang Deng, is the Director of Institute of Basic Science and a Distinguished Professor and Ph.D. Supervisor at Beijing Institute of Technology. His research mainly focuses on autonomous intelligent systems and wearable ubiquitous systems. He has led numerous projects, including key projects funded by the National Natural Science Foundation of China and the National Science and Technology Innovation 2030--Major program of “New generation of artificial intelligence”. He has published over 140 academic papers, obtained 108 authorized invention patents, and published one monograph and one textbook. Prof. Fang Deng has received several prestigious awards and honours, including the Distinguished Young Scholar of the National Science Foundation of China, selection as the National Youth Talent Support Program of China and recognition as a Beijing Science and Technology Rising Star. He has been awarded the China Youth Science and Technology Award, Outstanding Youth Zhongguancun Award, the Young Scientist Award of the Chinese Association of Automation, the National Science and Technology Progress Award (Second Class), the Gold Award at the Geneva International Invention Exhibition, the National Teaching Achievement Award (Second Class), the Beijing Teaching Achievement Awards (First and Second Class), and the CAA Higher Education Teaching Achievement Award (First Class). He has also received multiple provincial and ministerial-level science and technology awards and outstanding paper awards. Prof. Fang Deng serves as the Deputy Secretary-General of the Chinese Association of Automation, Deputy Director of the Youth Working Committee, and a member of several professional committees of the Chinese Association of Artificial Intelligence, the Chinese Association of Command and Control, and the Chinese Association of Automation. He is an editorial board member for journals such as IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE Transactions on Intelligent Vehicles, Acta Automatica Sinica, China, and Journal of Command and Control.

题目：智能群系统的协同感知与优化决策

摘要：智能群系统包括移动智能终端、各类无人系统等，其种类繁多，用途广泛，是新一代人工智能的重要组成部分。目前智能群系统存在感知能力不足与智能决策水平有限的问题，在复杂动态和对抗环境下表现尤为明显。本报告将从协同感知与优化决策两个方面阐述取得的最新研究进展，具体包括跨域多模态协同智能感知，跨平台多源目标检测与定位，大规模资源集群任务调度，分布式多体协作与智能博弈。旨在推动人机共融和集群智能的基础理论和关键技术突破，推广和普及智能群系统相关理论、技术和应用研究。

Title: Cooperative Perception, Optimization and Decision Making of Intelligent Swarm Systems

Abstract: Intelligent swarm systems, which encompass various mobile intelligent terminals and unmanned systems, are diverse in their types and widely applicable, making them an integral part of the new generation of artificial intelligence. However, current intelligent swarm systems face challenges related to inadequate perception capabilities and limited levels of intelligent decision-making, particularly evident in complex and adversarial environments. This report aims to present the latest research advancements in intelligent swarm systems from two aspects: cooperative perception, optimization and decision making. Specifically, it will cover topics such as cross-domain multimodal collaborative intelligent perception, cross-platform multi-source target detection and localization, large-scale resource cluster task scheduling, and distributed multi-agent cooperation and intelligent game theory. The goal is to promote foundational theories and key technological breakthroughs in human-machine coexistence and collective intelligence, as well as to disseminate and popularize theoretical, technical, and applied research in intelligent swarm systems.

刘连庆，沈阳自动化所机器人学国家重点实室研究员，主要研究方向：类生命机器人，微纳机器人，智能机器与系统。在 Nature Communications、Science Advances、IEEE TRO/TMECH/TASE等汇刊期刊发表论150余篇，26篇论文被Lab on a chip, Engineering, Small, IEEE TBME等选为封面故事，5 次获IEEE-ROBIO等国际会议最佳论文奖。研究成果先后入选美国实验室自动化与筛选学会年度十大技术突破 (2017 SLAS TOP 10)和中国智能制造十大科技进展(2020)。获得万人计划青年拔尖(2014年)、中科院卓青项目(2015)、国家自然科学基金优秀青年基金(2015年)、杰出青年基金(2019年)等人才项目支持；当选IEEE机器人与自动化学会副主席(2018-2019)、资深顾问(2020-2021)，担任国家机器人标准化总体组秘书长、“智能机器人”国家重点研发计划专家委员会委员、中国自动化学会机器人专业委员会主任委员、共融机器人专业委员会副主任委员、中国机械工程学会机器人分会副主任委员、中国微纳米学会微纳机器人分会副理事长等。获辽宁省自然科学一等奖、中国自动化学会技术发明一等奖、中国自动化学会优秀博士学位论文导师奖、中国科学院优秀导师奖、IEEE机器人与自动化学会青年科学家奖(IEEE RAS Early Career Award) ，中国自动化领域年度人物，首届熊有伦智湖优秀青年学者奖，中国科学院卢嘉锡青年人才奖，中科院院长优秀奖，辽宁省青年五四奖章，辽宁省五一劳动奖章等荣誉。

Lianqing Liu received his Ph.D. degree in Pattern Recognition and Intelligent System from university of Chinese Academy of Sciences, China in 2008, and B.S. degree in Industry Automation from Zhengzhou University, China in 2002. He started his career in 2006 at Shenyang Institute of Automation, Chinese Academy of Sciences, and holds the position of Assistant Professor (2006-2008), Associate Professor (2009-2010) and Professor (2011 to now) respectively. Currently his research interests include Biosyncretic systems, Micro/Nanorobotics, Intelligent control. He has published over 100 peer reviewed international journal papers and led more than 20 funded research projects as Principal Investigator. He was awarded the Early Career Award by the IEEE Robotics and Automation Society in 2011, Outstanding Young Scientist of Chinese Academy of Sciences in 2014, Rising Star Award of 3M-Nano Society in 2015, Talent Young Scholar Funds of NSFC in 2015, National Program for support of Top-Notch Young Professionals in 2015, Xiongyoulun Outstanding Youth Award in 2018, Distinguished Young Scholar Funds of NSFC in 2019. He is the winner of Best Student/Conference paper Award for ROBIO, IEEE-NANOMED and IEEE-3M-NANO, and delivered plenary/Keynote talks at IROS, IEEE-NANO, IEEE-NANOMED, IEEE-NEMS, ICIUS， MARSS and so on. He has served as guest editor for IEEE TNANO, Sensors, TIMC, Journal of automous robotics, Journal of Healthcare Engineering，associate editor of Mechtroncis, IET Cyber-Systems and Robotics, Control Theory and Applications. He has been elected as the vice president of IEEE Robotics and Automation Society for the term of 2018-2019, served as a member of long range planning committee of RAS. 

题目：弱信息条件下仿细胞形态智能机器人集群

摘要：实现集群机器人应用的关键挑战是开发一种可编程和可靠的自组织方法，允许在全局模式下实现机器人的复杂编队和控制，尤其是在弱信息环境下，如空间、深海或人体内，更需要机器人集群以最小的计算和感知来实现复杂的全局控制。然而，目前集群机器人的控制算法往往通过简单地模仿动物群的行为而实现，这需要单体机器人具有较好的传感和控制性能，难于适用于空间、深海等极端弱信息环境。因此，开发可靠的弱信息条件下机器人集群算法将从理论和应用上推进集群机器人的进展。在本报告中，我将介绍课题组的最新进展：直接将细胞集群的内在原理应用于机器人集群的形成，通过对细胞集群迁移的研究，细胞只需要通过简单粘弹性的调节，就可自组织成复杂的形状。受此启发，我们验证了仅仅是通过模拟细胞的粘附性调节，单体机器人无需复杂的感知和控制能力，即可实现机器人的集群控制，该研究为机器人集群算法提供了新的思路，与此同时，机器人集群的研究又反向加深了对细胞复杂集群行为的理解。

Title: Cell-like Morpho-intelligence of robot swarms in denied environments

Abstract: A key challenge in realizing the potential applications of swarm robotics is to develop a programmable and reliable self-organization approach that allows robot swarms to achieve complex global patterns. The expected harsh working environments for robot swarms, such as space, deep sea, and the human body, require robot swarms with minimal computation and perception (MCPR) to achieve complex global patterns. Meanwhile, current nature-swarm-inspired algorithms in swarm robotics are derived by simply imitating the behaviors of animal swarms rather than directly applying intrinsic principles due to the capability gap between animals and robots. Thus, the development of reliable control algorithms for swarms of MCPRs will advance swarm robotics from both application and theoretical perspectives. In this talk, we will introduce our work on directly applying intrinsic principles of cell collectives to the formation of MCPR swarms. Based on this finding, we can program modular robots to form functional morphologies by tuning their adhesion. This work advances swarm robotics in forming functional morphologies and allows us to quantitatively study morphogenesis in cell collectives using robot swarms.

祝连庆，北京信息科技大学二级教授，博士生导师，中国仪器仪表学会会士，教育部科技委委员，教育部教指委委员，装备发展部光电领域专家，北京学者，国家级百千万人才，享受国务院政府特殊津贴专家，国家有突出贡献中青年专家，北京市劳模、北京市优秀教师，科技北京百名领军人才，北京市有突出贡献科学人才，北京市高创计划杰出人才，教育部长江学者创新团队带头人，北京市战略科技团队带头人，北京市首届优秀研究生指导教师团队带头人，第七届全国优秀科技工作者，第十二届、第十三届、第十四届全国政协委员。主持国家863重大课题、教育部长江学者与创新团队发展计划项目、国家重大科技研发专项、国家重大科学仪器设备研制专项、国家自然科学基金、总装备部预先研究项目等国家级、省部级项目30余项。近年来发表学术论文160余篇，出版专著4部，获国家发明专利授权130余项，培养博士、硕士研究生110余人。以第一完成人获国家科技进步二等奖1项，教育部技术发明一等奖1项，北京市科学技术二等奖2项，中国仪器仪表学会技术发明一等奖1项，中国计量测试学会科技进步一等奖1项，获北京市教学成果一等奖1项、二等奖1项。

Lianqing Zhu, second-level professor at Beijing University of Information Science and Technology, doctoral supervisor, fellow of China Instrument and Control Society, member of the Science and Technology Committee of the Ministry of Education, teaching director of the Ministry of Education Committee member, expert in the field of optoelectronics of the Ministry of Equipment Development, Beijing scholar, national-level talent, expert enjoying special government allowances from the State Council, national young and middle-aged expert with outstanding contributions, Beijing model worker, Beijing outstanding teacher, one of Beijing's 100 leading talents in science and technology, There are scientific talents with outstanding contributions in Beijing, outstanding talents from the Beijing High-tech Innovation Plan, leader of the Yangtze River Scholars Innovation Team of the Ministry of Education, leader of the Beijing Strategic Science and Technology Team, leader of the first outstanding postgraduate instructor team in Beijing, and the seventh National Outstanding Science and Technology Worker, member of the 12th, 13th and 14th National Committee of the Chinese People's Political Consultative Conference. He hosted more than 30 projects, including national, provincial, and ministerial level projects, such as the National 863 Major Project, the Ministry of Education Yangtze River Scholars and Innovation Team Development Plan Project, the National Major Science and Technology Research and Development Project, the National Major Scientific Instrument and Equipment Development Project, the National Natural Science Foundation, and the General Armament Department Preliminary Research Project. In recent years, he has published more than 160 academic papers and 4 monographs, obtained more than 130 national invention patent authorizations, and trained more than 110 doctoral and master students. As the first completer, he won 1 second-class National Science and Technology Progress Award, 1 first-class Technical Invention Award from the Ministry of Education, 2 second-class Beijing Science and Technology Awards, 1 first-class Technical Invention Award from the Chinese Instrument and Control Society, and China Metrology Test He won 1 first prize for scientific and technological progress of the Society, 1 first prize and 1-second prize for Beijing Teaching Achievements.

题目：无人系统中的红外探测技术

摘要：制冷器红外探测技术在无人光电吊舱、红外导引头以及高低轨卫星装备中具有广泛应用，6.1 Å的III/V超晶格材料体系红外探测器因其波长调控简单、材料稳定性高以及低的俄歇复合及隧穿暗电流优点，被认为是继碲镉汞材料体系的下一代红外探测材料。本报告从红外探测器在无人系统中应用角度出发，分析超晶格红外探测器的研究挑战及亟待解决的问题，阐明高量子效率超晶格红外探测成像模型与实现机制、超晶格探测器材料界面缺陷对探测器暗电流影响机制及调控方法、高性能二类超晶格扩展短波/中波/长波波段面阵探测器材料外延工艺、InAs/GaSb/AlSb扩展短波超晶格-InAs/InAsSb高温中波超晶格-InAs/GaSb长波应变超晶格红外探测器制备方法、高灵敏度低噪声红外雪崩探测器能带结构最优构型设计方法，最后总结超晶格个探测器下一步研究计划。

Title: Infrared detection technology in unmanned systems

Abstract: Cooler infrared detection technology is widely used in unmanned photoelectric pods, infrared missile seekers, and high and low orbit satellite equipment. The 6.1 Å III/V superlattice material system infrared detector has simple wavelength control and high material stability. As well as the advantages of low Auger recombination and tunneling dark current, it is considered to be the next generation infrared detection material following the mercury cadmium telluride material system. This report starts from the perspective of the application of infrared detectors in unmanned systems, analyzes the research challenges and problems that need to be solved in superlattice infrared detectors, and explains the high quantum efficiency superlattice infrared detection imaging model and implementation mechanism, superlattice detection The mechanism and control method of the impact of interface defects in detector materials on detector dark current, high-performance type II superlattice extended shortwave/mediumwave/longwave band area array detector material epitaxial process, InAs/GaSb/AlSb extended shortwave superlattice-InAs /InAsSb high-temperature medium wave superlattice-InAs/GaSb long wave strain superlattice infrared detector preparation method, high sensitivity and low noise infrared avalanche detector energy band structure optimal configuration design method, and finally summarize the superlattice detector research plan.

陈谋，南京航空航天大学自动化学院院长，教授、博士生导师，享受国务院政府津贴。2018年国家自然科学基金杰出青年基金获得者、2019年国家“百千万”人才工程入选者。先后在南京航空航天大学获学士与博士学位，并先后在英国拉夫堡大学、新加坡国立大学和澳大利亚阿德莱德大学做访问或博士后研究。目前担任SCI收录英文期刊《IEEE Transactions on Systems, Man, and Cybernetics: Systems》等编委、担任《中国科学.信息科学》、《自动化学报》、《控制理论与应用》、《航空学报》等编委等。同时担任教育部高等学校教学指导委员会兵器类委员、中国人工智能学会智能空天专业委员会副主任委员、中国指挥与控制学会群集智能与协同控制专业委员会副主任委员、自动化学会信息物理系统控制与决策专业委员会副主任等。先后获国家自然科学二等奖1项(排名第二)、江苏省科学技术奖一等奖1项(排名第一)、江苏省青年杰出贡献奖、国防科技进步二等奖2项(排名第一)，申请授权发明专利40余项。出版中英文专著3部，发表学术论文200余篇。

Mou Chen, IEEE Senior Member. He is now a professor and Dean of the College of Automation Engineering, Nanjing University of Aeronautics and Astronautics. He received the BSc degree and the PhD degree in Nanjing University of Aeronautics and Astronautics. He was granted by the National Science Fund for Distinguished Young Scholars in 2018, was awarded by the Millions of Talent Projects National candidates in 2019, and was elected to the Program for New Century Excellent Talents in University of Ministry of Education of China in 2011. He visited the Loughborough University, UK, from November 2007 to February 2008. He was a postdoctoral fellow in the National University of Singapore, Singapore, from June 2008 to September 2009. He was a senior research fellow in the University of Adelaide, Australia, from May 2014 to November 2014. He has actively served in the editorial boards of a number of international journals as an associate editor, including IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE Access, Neurocomputing, International Journal of Advanced Robotic Systems, Chinese Journal of Aeronautics, SCIENCE CHINA Information Sciences, etc. He was awarded two Second Prize in China's State Natural Science Award (ranking second), one First Prize in Natural Science Award of Ministry of Education (ranking second), First prize of Science and technology of Jiangsu Province (ranking first), two Second Prize in National Defense Science and Technology Progress (ranking first), and applied over 40 invention patents. He has published two English monograp, one Chinese monograph and over 200 academic papers.

题目：多约束下无人机鲁棒飞行控制技术

摘要：由于无人机执行的任务具有多样性，工作环境不断动态变化，经常受到阵风、风切变和紊流等动态扰动的影响，而且需满足输出性能约束、状态受限约束和输入饱和约束等多种约束条件，导致难以有效实现无人机的鲁棒稳定性控制。另一方面，无人机各个通道严重耦合，外界干扰和系统多约束更容易造成无人机飞行运动品质急剧下降，甚至导致摔机。本报告针对多约束条件下的受扰无人机，介绍了基于事件触发的无人机最优机动控制方法、输入输出约束下的安全跟踪控制方法以及航迹约束下的无人机自适应模糊安全优化飞行控制，保证了无人机在各类约束条件下的飞行控制精度，提高了无人机的任务完成能力。

Title: Robust flight control under multiple constraints for UAV

Abstract: Due to the multiple task requirement of UAV, the dynamic change of working environment, the influence of dynamic disturbance such as wind gust, wind shear and turbulence, and the need to meet the output performance constraints, state constraints and input saturation constraints, it is difficult to effectively achieve robust stability control of UAVs. On the other hand, the various channels of the UAV are seriously coupled, and external disturbance and multiple constraints of the system are more likely to cause a sharp performance degradation in the flight process of the UAV, and even lead to a crash. For the disturbed UAV under multiple constraints, this report introduces the optimal maneuvering control method of UAV based on event triggering mechanism, the safety tracking control method under input and output constraints, and the adaptive fuzzy safety optimization flight control of UAV under trajectory constraints, which ensures the flight control precision of UAV under various constraints and improves the task completion ability of UAV.