Chair: Prof. Xinjun Mao, National University of Defense Technology, China
Bio: Xinjun Mao received the Master degree and PhD degree at the National University of Defense Technology in 1995 and 1998 respectively. He was a visiting scholar at the University of Toronto from 2003-2004. He is currently a Professor at the National University of Defense Technology. His research interests include multi-agent system, crowd-based software engineering, open-source software technologies, and robotics software engineering. He teaches several courses including software engineering (undergraduate), software architecture and design(undergraduate), and intelligent software engineering(postgraduate).
Summary: In recent years great progresses have been made in the field of software engineering. Emerging requirements of software in areas like robotics challenge the existing methodologies and technologies of software development. Researchers and practitioners of software engineering try to seek novel methods and techniques to solve the problems. Undoubtedly, software engineering for robotics needs to tackle several issues that are tightly related with the special requirements of robotics, including 1) control, decision and schedule of robotics, 2) open operating environments that are dynamic and in which changes are unpredictable, 3) more dependable, robust, flexible, safe and security etc. This will present more challenges and opportunities to promote the researches and practices of the requirement, design, programming, testing, maintenance and operation technologies for robotics, and corresponding CASE tools and platforms.
Keywords: Software engineering, robotics, software system, software development technology, CASE tool
Software Analysis, Design and Modeling
Robotics Software Architecture
Crowd-based Software Engineering
Open Source Software for Robotics
Intelligent Software Engineering
Software Maintenance and Evolution
CASE Tools and Platforms for Robotics
Software Engineering Methods
Model-Driven Software Development
Agent-based software engineering
Software and system quality of service
Software and System Testing Methods
Safe of Robotics System
Security and Privacy of Robotics System
Workshop 2 - Design, Modeling, and Control for Surgical/Mobile Robots under Special working Environment
Chair: Assist. Prof. Mengtang Li, Sun Yat-Sen University, China
Bio: Mengtang Li received the B.S. degree with honor in Astronautics Engineering from Northwestern Polytechnical University, Xi'an, China, in 2014, the M.S. degree in Electrical Engineering and the Ph.D. degree in Mechanical Engineering from Vanderbilt University, Nashville, TN, USA, in 2016 and 2020, respectively. Dr. Li was a visiting scholar at Sarver Heart Center, School of Medicine, University of Arizona from 2018 to 2020. He is currently an Assistant Professor in School of Intelligent Systems Engineering in Sun Yat-Sen University, Shenzhen, China.His research interests include fluid powered robotic system design and control, mobile unmanned robots/vehicles/aircrafts control, medical device (total artificial heart, ventricular assist device) design, optimization and control. His lecture courses include intelligent robotic system (undergrad), software engineering (undergrad), advanced robot modeling and control (grad).
Summary: Advanced medical robots and special operation robots are the key research fields of China's "Fourteenth Five Year Plan", which is vital in improving China's national medical care, resource exploration, disaster relief and rescue, and military exploration. Typical industrial robotic arms and AGV mobile robots have characteristics of paradigm forms and electric motor actuations. Yet, medical robots and special operation robots working under special environments require 1) specifically designed actuators and robot structure that meet the requirements of environmental constraints, 2) establishment of ad hoc kinematics/dynamics robot model, and 3) designs of control algorithms for joint actuators and the entire robot to meet the accuracy and dynamic characteristics requirements, and environmental constraints. Hence, the design, modeling and control of medical/mobile robots under special working environments brings more challenges and opportunities.
Keywords: Special working environment, robot design, actuation method, modeling method, control algorithm, simulation, prototype test.
Workshop 3 - Construction of High Quality Virtual Simulation Environment and Training of Intelligent Perception and Interactive Simulation for Intelligent Robots
Chair : Assoc. Prof. Xiaogang Wang, Southwest University, China
Bio: Xiaogang Wang is an associate professor in the School of Computer and Information Science, at Southwest University. He earned his Ph.D. in 2020 from Beihang University, advised by Professor Qinping Zhao. Prior to joining Beihang University, he got my bachelor and master degree from China Agricultural University. From 2019 to 2020, he worked as a visiting PhD in the GrUVi lab of Simon Fraser University. He is leading the Visual Computing and Intelligent Simulation Group. His recent research interests focus on Virtual reality, Digital Twinning, 2D/3D Computer Vision, Computer Graphics, 3D Geometry Understanding and Reconstruction, Machine Learning.
Summary: In 2017, The State Council issued the Development Plan for the New Generation of Artificial Intelligence, in which intelligent robots are one of the core directions. In addition to the advantages of traditional robots, intelligent robots can effectively avoid production accidents and work in harsh environments due to their intelligence, which can be widely used in intelligent manufacturing, logistics and transportation, automatic driving, medical rehabilitation, emergency rescue and other fields. In recent years, with the rapid development of artificial intelligence and virtual reality technologies, computer vision autonomous perception, multi-modal human computer interaction technologies greatly boosting the development of intelligent robots. At present, relevant intelligent frontier technologies mainly include: 1) According to specific application scenarios, build the high-quality virtual simulation environment to assist the rapid iterative verification of related intelligent algorithms; 2) Autonomous perception of intelligent robots, which can "observe" and "understand" the surrounding environment in real time, and improve intelligent decisions such as autonomous control of robots; 3) Multi-modal human computer interaction technology can realize intelligent information exchange between human and robot by using multi-modal information such as voice, image, text, eye movement and touch. Therefore, the construction of high-quality virtual simulation environment for intelligent robots and the training of intelligent perception and interactive simulation have great research and application value.
Chair: Assoc. Prof. Kui Qian, Southeast University, China
Bio: Dr. Kui Qian, senior engineer, associate professor at Nanjing Institute of Technology. In 2014, he graduated from the School of Instrument Science and Engineering in Southeast University. From 2012 to 2013, he was sent to the School of Computer Science and Engineering in Michigan State University for joint training. He has presided over the design, development and construction of new generation information systems at national and provincial levels, and served as deputy chief designer, chief designer, deputy chief software engineer, and deputy chief engineer for major projects at national and provincial levels. He has published more than 30 papers as the first author and authorized 10 national invention patents (including national defense patents).His research interests include intelligent robotics, environment perception and cognition, machine vision. His lecture courses include digital signal processing, intelligent instrumentation, digital image processing, etc.
Summary: Machine vision mainly uses computers to simulate human vision functions by extracting, processing and understanding image information, thus providing assisted decisions for robot control. With the rapid development of artificial intelligence technology, robot integration of machine vision technology to achieve intelligence, flexibility has become an inevitable trend. The robot integrates advanced sensing, environmental perception, human machine interaction and other functions, and cross-fertilizes with machine learning, deep learning, expert knowledge and other technologies to promote the intelligent empowerment of robot perception decision and execution. Therefore, the robot incorporates the new generation of machine vision, dynamic and complex environment perception, machine vision target detection, deep learning-based assisted decision-making and other core technologies, which play an important role in improving the intelligence level of the robot.
Workshop 5 - Task Assignment, Path Planning, and Control for Multi-robot Systems
Chair: Assist. Prof. Xiaoshan Bai, Shenzhen University, China
Bio: Xiaoshan Bai has been an Assistant Professor with the College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China, since September 2020. He received the Ph.D. degree in Systems Engineering from the University of Groningen, The Netherlands, in 2018. From January to July 2015, he was a Research Fellow with the Department of Electrical and Computer Engineering, National University of Singapore, Singapore. From November 2018 to July 2019, he was a Lecturer with the Faculty of Science and Engineering, University of Groningen. From August 2019 to July 2020, he was a Post-Doctoral Fellow with the Department of Cognitive Robotics, Delft University of Technology, The Netherlands. His main research interests include multi-vehicle/robot task assignment, path planning, logistic scheduling, and heuristic algorithms.
Summary: The increasing automation level of human being’s activities both in civil and military applications poses higher requirement on the intelligence of the various autonomous vehicles/robots, such as unmanned aerial vehicles (UAVs), unmanned marine vehicles, ground vehicles/robots and sensor networks. Among these applications, task assignment for one or multiple robots has been an important research area due to its high theoretical value and wide engineering applications in logistics, terrain mapping, and environmental monitoring. As researchers design, build, and employ cooperative multi-robot systems, they invariably encounter the fundamental question: which robot should execute which task in order to achieve the global goal cooperatively? This question must be answered, even for relatively simple multi-robot systems, and the importance of task allocation grows with the complexity, in size and capability, of the robot system under study. Furthermore, when a robot’s motion is affected by external disturbance such as winds/currents or static/dynamic obstacles, it is necessary to investigate how to optimally navigate/control a robot starting from one location to a target location, which is the robotic path planning problem.
Robot Design, Development and Control Space and Underwater Robotics Multi-robot coordination and cooperation Modeling, Simulation and Architecture Mathematical and computational methods in robotics Multi-robot task assignment Robotic path planning Robotic motion planning
Workshop 6 - Data Driven Aeroengine Health Management Technology
Chair: Prof. Bin Liu, Shenyang University of Technology, China
Bio: Bin Liu, Vice Dean, Professor, Doctoral Supervisor and Distinguished Professor of "Xiangyuan Scholars" of School of Information Science and Engineering of Shenyang University of Technology. "Hundred level" talents of the "Ten Thousand Project" in Liaoning Province, "Top Young Talents" of the "Liaoning Revitalizing Liaoning Talent Plan", the first batch of "excellent young" winners in Liaoning Province, innovative talents in Liaoning Province, high-level "leading talents" in Shenyang, excellent scientific and technological workers in Shenyang, and excellent postgraduate guidance teachers in Shenyang. He is now a senior member of China Mechanical Engineering Society, a member of China Electromagnetic Nondestructive Testing Society, and a member of the European Electromagnetic Nondestructive Testing Expert Group.
In the past five years, Professor Bin Liu has presided 37 national natural science foundation projects, provincial and ministerial key projects, international cooperation projects, and enterprise projects. Won 3 first prizes and 2 second prizes of provincial and ministerial "science and technology awards", 3 first prizes, 1 second prize and 1 third prize of "teaching achievements" of Liaoning Province; He has published more than 70 high-level academic papers and 4 books; 57 national and international invention patents, Software copyrights, etc. He has carried out extensive cooperation with Professor Xavier Maldague, an academician of the Canadian Academy of Engineering, Professor Juyang of Nagoya University, Professor Takagi of Tohoku University, Harbin Institute of Technology, Tianjin University, Dalian University of Technology, Northeast University, Aero Engine Corporation of China and other well-known universities and enterprises at home and abroad in aeroengine monitoring and testing technology, health management technology, etc.
Summary: Aeroengine is one of the most complex and multidisciplinary engineering mechanical systems nowadays. Due to environment is severe, and it is easy to cause performance degradation or various mechanical failures, which poses a great threat to flight safety. Aeroengine health management technology is not only an important technical means to ensure flight safety, but also a basic method for airlines and aeroengine manufacturers to reduce maintenance and support costs and scientifically manage spare parts. However, the modeling mechanism of aeroengine is complicated, the fault types are various, and the flight parameters have the characteristics of massive, high-dimensional and nonlinear, which brings great challenges to the health management of aeroengine. Data driven approach has become a research hotspot in the field of aeroengine health management because it does not require complex physical models. In recent years, AI technology based on big data has made significant progress and played an important role in aeroengine life assessment, digital modeling of key components, engine fault diagnosis, engine intelligent maintenance and other fields.
The purpose of this workshop is to collect the research results provided by academic and industrial researchers, so as to introduce, discuss and exchange ideas, results, work and experience of data-driven aeroengine health management technology. We look forward to your discussion on new theories, technologies and applications in relevant fields. Together, we can promote the development of intelligent aeroengine health management system.
Keywords: Data driven, aeroengine, health management, failure diagnosis, life assessment
Aeroengine health management technology
Aeroengine life assessment technology
Digital modeling technology of key components
Aeroengine failure diagnosis technology
Aviation composite material monitoring and testing technology
Workshop 7 - Software Quality Assurance for Robotics
Chair: Dr. Xingguang Yang, NingboTech University, China
Bio: Xingguang Yang received a bachelor's degree in engineering from the Department of Information Security of China University of Mining and Technology in 2016, and a doctor's degree in engineering from the Department of Computer Application Technology of East China University of Technology in 2021. He is now a teacher in the School of Computer and Data Engineering of NingboTech University. His research interests include software engineering, software analysis, software quality assurance, software defect prediction, software technology debt analysis, etc. He teaches the courses of "Mobile Platform Development" and "Computer Network" for undergraduates. He has published more than 20 papers in international journals or conferences, including 7 as the first author. He reviews manuscripts for journals/conferences (such as International Journal of Software Engineering and Knowledge Engineering, EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, etc.).
Summary: In recent years, the field of robots has received extensive attention from academia and industry. Robot oriented software system has the characteristics of complex program and large scale. Ensuring the software quality is the key to the smooth operation of the robot system. The conference aims to discuss how to use cutting edge AI, big data, blockchain and other technologies to improve the software quality in robot systems, such as 1) software defect prediction; 2) Software warehouse data mining bureau; 3) Code odor analysis, 4) software technology debt management and prediction, etc.
·Self-Admitted Technical Debt Analysis for Robotics
·Programming Analysis and Code Complement for Robotics
·Software security and vulnerabilities analysis for Robotics
Workshop 8 - Human-robot collaboration and interaction
Chair: Dr. Zoltan Dobra, Széchenyi István University, Hungary
Bio: Dr. Zoltan Dobra received technical manager qualification at the Széchenyi István Collage in 1998, MSc in Management and Leadership at Szent Istvan University, Hungary in 2010, MBA qualification in 2017 at the Széchenyi István University Hungary. He finished the Doctoral School of Regional Sciences and Business Administration in 2021.
He presented his topics at various conferences, such as the EDSI in Italy and United Kingdom and DSI conferences in the USA in 2018, 2019. He is the member of the Production and Operations Management Society and showed the progress at the POMS conference in Washington DC, USA in 2019. He is also the member of the IEEE organisation, which is the world's largest technical professional organization.
Since 2010 he works as a Production Manager at the Audi car manufacturing in Hungary. He moved to China in 2021 to take over a Manager position in car manufacturing of Audi models in a FAW-Volkswagen car plant in Changchun, Jilin, China. His research field is the human-robot collaboration in the manufacturing.
Summary: Industrial robots usually have physical separation such as cages, fences, barriers, and lasers, outlining their restricted space. Collaborative robots, also called “cobots” function together with their operators in an uncaged environment. Such design enables safe collaboration between humans and robots with a reduced risk of injuries and collisions. The wires and motors in cobots are internal. External surfaces and edges are rounded to reduce potential injuries. Cobots have built-in sensors to detect external forces and to stop or return to park position in case of contact or collision not pre-programmed. An advantage of deployment of a robot is that it is easy to teach the robot—the operator can show it the desired movement and it can then replicate that movement accurately. The advantage of the robot’s replicating accuracy, and agility and ability to produce repetitive work combined with human capabilities opens a new era in manufacturing.
The human-robot collaboration has several research field, such as: (1) process of working together, (speed, communication, position, feedback, task allocation), (2) human factors (adaptation, leadership, trust, workload, attitudes, framework, force), (3) complexity handling, programming (architecture, system/product/process complexity, scheduling), (4) robot system safety (injury, trust, collision detection, impact management, position following, collision avoidance), (5) instructing the robot system (gesture control, physical contact, role changing, teaching). Each of the topic has possibility for further deeper research.
Keywords: human robot interaction, collaborative robot, cobot, design for human-robot interaction
Robot Design, Development and Control
Industrial Networking and Automation
Workshop 9 - Hardware Engineering for Robotics
Chair: Deputy Director. Jianwei Zhao, Mine Robot Research Center, China
Bio: Jianwei Zhao, male, postdoctoral, Department of Precision Instruments and Mechanics, Tsinghua University. In 2013, he was the Deputy Director and Associate professor of the Department of Intelligent Control and Robotics, China University of Mining and Technology (Beijing), and he was the master's supervisor. He is the deputy director of Mine Robot Research Center. His courses for undergraduate or graduate students include "Fundamentals of Robot Technology", "Design and Application of Robot System", "Automatic Control System", "Electromechanical Control System", etc.
He became involved in robotics research in 2005 and has completed the development of 100 robots. The flexible two-wheel self-balancing bionic autonomous learning robot is the "world's first" robot with motion balancing self-learning and has won a series of awards; The CMP chemical polishing transmission robot developed for "National 02 major Project" has been put into production. China University of Mining and Technology (Beijing) self-made obstacle crossing robot, star wheeled robot, geological disaster detection robot, two-wheeled self-balancing robot, thin coal seam inspection robot, track reconnaissance robot, Mars robot, flying robot, underwater robot and other robots.
As the person in charge or project implementation personnel, he has participated in National Science and Technology 02 major project, National 863 project, National 12th Five-Year Key Research and Development Project sub-project, National 13th Five-Year Key research and development project sub-project, National Natural Science Foundation project, Beijing Natural Science Foundation project; It undertakes the research and research of National Natural Science Foundation of China, China Postdoctoral Science Foundation, Beijing Key Laboratory Project, Basic Research funds for central universities and several horizontal projects.
He has published 48 papers, including 54 SCI papers in journals such as Carbon and The International Journal of Advanced Manufacturing Technology. Published 21 EI papers on International Conference on Global Control (IFAC), IEEE International Conference on Robotics (ROBIO2009, ICIRA2009 and Robotics), and served as the Chinese representative's speech and chairman of the sub-conference at IFAC Conference. As a reviewer of SCI journal "Neural Computing and Applications" and a member of the Youth editorial board of "Industrial and Mining Automation".
He's the chief editor of Robot System Design and Application Technology,published by Tsinghua University Press.He is one of the lead authors of Research and Design of Two-Wheeled Self-Balancing Robots, published by Beijing's Science Press.His awards include: 1 authorized international patent, 8 national invention patents, 4 utility model patents, and China Industry-University-Research Cooperation Innovation and Promotion Award (individual award); He has won one provincial first prize (the first prize of Science and Technology Progress Award of Dezhou City, Shandong Province ranked second), Ten Outstanding Youth of Changzhou City and one second prize.
He also serves as a financial project evaluation expert of China Association for Science and Technology, a member of the Youth Robot Competition Committee of China Association for Science and Technology, a member of the Expert database of China Association for Science and Technology, a member of the expert database of Beijing Association for Science and Technology, a member of the expert database of China Science and Technology Museum, and the first group of expert members of the expert database of China University Innovation and Entrepreneurship Education Research Center. Academic Degree and Graduate Education Development Center, Ministry of Education; Member of Central Financial Project Evaluation Expert Database of China Association for Science and Technology; Judge of National Youth Innovation Competition of China Association for Science and Technology, member of proposition group and evaluation expert of National Youth Science Innovation Experiment and Work Competition of China Association for Science and Technology, evaluation expert of "Tomorrow's Little Inventor", evaluation expert of "Jin Peng"; Chief judge of Robot Competition in five provinces of North China; Chief Judge and member of Technical committee of Robocup and China Robot Competition and New Silk Road International Robot Competition.
Summary: In recent years, hardware engineering has made great progress. The special software requirements in robotics and other fields have brought new challenges to the current hardware engineering methods and techniques. Researchers and practitioners in the field of hardware engineering are trying to address these challenges in a variety of ways. Undoubtedly, hardware engineering for robots and other special fields needs to solve hardware problems unique to these fields, such as 1) the influence of hardware on robot control, decision and scheduling; 2) Robot control technology and application technology under open, dynamic and unpredictable operating environment; 3) Robots need to be more humanized, intelligent and diversified. This will be an important driving force to promote the research and practice of hardware engineering in requirements, design, testing, maintenance and other aspects of technology.
Keywords: Hardware engineering, robotics, control technology, application technology, intelligence, diversification
Hardware Analysis, Design and Inspection
Robot Hardware Architecture
Crowd-based Hardware Engineering
Hardware Maintenance and Development
Hardware Engineering Method
Hardware and System Testing methods
Safe of Robotics System
Security and Privacy of Robotics System
Workshop 10 - Modelling and Engineering in Robotic Human Cyber-Physical Systems
Chair: Assoc. Prof. Bo Liu, Southwest University, China
Bio: Bo Liu received his PhD degree at Chongqing University in 2012. He won the Excellent Teacher Award during his visit to Deakin University in Australia in 2016. He is now an associate professor of the School of Computer and Information Science (CIS) and the director assistant of the Center for Research and Innovation in Software Engineering (RISE) at Southwest University. His research interests include model-driven and AI-enabled software engineering, trustworthy microservices and blockchain systems, educational affective computing systems, etc. He teaches several courses including "Introduction to Software Engineering" (undergraduate) , "System Analysis and Design" (undergraduate) and "Software Modeling and Design" (postgraduate).
Summary: Human-cyber-physical system (HCPS) is one of the most hyped buzzwords in the computing science and technology, control engineering, communication, and information communication technology (ICT) application communities. HCPS can be essentially regarded as the emerging architecture style of engineering systems that are formed with cyber-systems, physical systems, and human systems (in the forms of individuals, organisational and social systems), which provides the enabling technology for solving many major challenges in sustainable development (e.g., smart city, industrial 4.0). However, the research on HCPS is in its infancy; especially, theories and methods in constructing HCPS with robotic systems as components still remain unknown. Issues demanded to be solved includes (not limited to): (1) abstractions and computational theory of Robotic HCPS, (2) theories and methods of Robotic HCPS architecture modelling, (3) specification and verification of the Robotic HCPS model, and (4) software-defined Robotic HCPS.
Topics Human-cyber-physical system Cyber-physical system Internet of Things Digital twins Hybrid system Model-based software engineering for HCPS Security and Privacy of HCPS Computational theory Architecture modelling Software-defined technology Formal verification of HCPS Artificial intelligence for HCPS The integration of HCPSs Simulation Implementation of HCPS components HCPS applications
Workshop 11 - Advanced Sensing for Robotics
Chair: Assoc. Prof. Jianxiong Zhu, Southeast University, China
Bio: Dr. Jianxiong Zhu obtained master's and doctor's degrees in 2010 and 2015 at the University of Science and Technology of China and the University of Missouri Columbia in the United States, respectively. After that, he engaged in scientific research at the Chinese Academy of Sciences, KAIST in Korea, and the National University of Singapore from 2016 to 2020. He is now an associate professor at the School of Mechanical Engineering at Southeast University. His research interests include advanced sensing, machine learning, human-computer interface, functional materials, taste and smell navigation, machine vision, etc. He teaches undergraduate courses in "control engineering" and "measurement and control engineering".
Summary: Robotics is hot research nowadays. However, the sensing technology around robotics is also a key component along with the trends of advanced robotics. Sensing in robotics contains machine vision, smell, touch, taste, and listening. The goal of sensing in robotics make the system to be stable and sustainable. Especially, with the technology of machine learning and virtual reality, robotics becomes more flexible and functional for human beings. Enabled by innovative machine learning algorithms and enhanced calculated performance, multi-sensor-based intelligent process control systems could eventually be realized, rendering a large variety of promising applications for robotics. We welcome contributions devoted to the design, fabrication, characterization, integration, and application of artificial intelligence and smart sensors around robotics, with a particular interest in multi-sensor fusion; machine vision technologies; digital twin technologies; human–machine interface; IoT; machine learning; and smell, touch, taste, and listening technologies for robotics.
Keywords: Smart Sensing, machine learning, virtual reality, human-machine interface, Five senses in robotics
Machine learning and multi-sensor in robotics Machine vision-based industrial defect detection for robotics
Sensitive materials in advanced sensing technology Machine vision, smell, touch, taste, and listening for robotics Functional sensing principle and coupling technologies for robotics Cloud computing assisted industrial advanced technology in robotics Edge computing assisted industrial advanced technology Multi-sensor fusion methods for industrial process technology
Workshop 12 - The code analysis, quality measurement, problem detection and refactoring techniques for complex software systems
Chair: Assoc. Prof. Wuxia Jin, Xi’an Jiaotong University, China
Bio: Wuxia Jin received her Ph.D degree in Computer Science from the Xi’an Jiaotong University in 2020, advised by Prof. Qinghua Zheng and Prof. Ting Liu. She was a visiting PhD student at the Drexel University from 2018-2019, advised by Prof. Yuanfang Cai. She is currently an Associate Professor at the Xi’an Jiaotong University. Her research focuses on software engineering, including code dependency analysis, software architecture, microservice, and software evolution and maintenance. Her research work has been published in international conferences and journals in the domain of software engineering, such as ICSE、ASE and TSE.
Summary: The rapid development of artificial intelligence, robots, and big data has posed new challenges for current software engineering methods and technologies. For a variety of complex software systems, it is urgent to conduct research from different perspectives such as code understanding, quality measurement, defect and security problem detection, code refactoring, architecture optimization, etc. For example, due to the characteristics of programming languages such as dynamic typing and functional programming, some software behaviors are implicit. Therefore, it is difficult to accurately resolve the implicit dependencies between code elements. For a software system, the source code, documents, execution logs are heterogeneous. How to combine heterogeneous feature for software quality assessment is challenging. Software systems in robot and big data fields contain the AI models as parts of the constructs; their evolution, defect detection, repair and refactoring techniques are also worth exploring.
Chair: Prof. Qi Chen, University of Shanghai for Science and Technology, China
Bio: Qi Chen received the B.S. (2006) in automation and the M.S. (2008) in control theory and control engineering from North-eastern University, Shenyang, China, and the Ph.D. (2017) in pattern recognition & intelligent systems from University of Chinese Academy of Sciences. He worked in the Shenyang Institute of Automation, Chinese Academy of Sciences (SIACAS) from 2008 to 2019. Currently, he is a Professor with the Mechanics institute at University of Shanghai for Science and Technology, Shanghai, China. His research interests include soft manipulators, marine engineering and underwater vehicles.
Summary：In recent years, a series of improvements have emerged in the control of soft manipulators. Soft manipulators have many advantages such as light weight, small moment of inertia and easy-to-realize variable stiffness control. However, it is challenging for soft manipulators to obtain accurate and smooth trajectory tracking due to their nonlinear response, parameters variation and non-rigid structures. At present, the control of soft manipulators is mainly studied from the following aspects: 1) structure design for more accurate soft manipulators, 2） more advanced control algorithms etc. It is believed that the progress of these aspects is beneficial for soft manipulators to obtain broader application fields and greater development prospects.
Keywords：soft manipulators, trajectory tracking, kinematic and dynamics models, control algorithms
Workshop 14 - Robot Grasping Based on Computer Vision
Chair: Assoc. Prof. Yifei Xu, Xi’an Jiaotong University, China
Bio: Yifei Xu received the Bachelor degree and PhD degree at the South China of South China University of Technology and Zhejiang University in 2011 and 2017. He was a visiting scholar at the University of Toronto from 2015-2017. He is currently a Associate Professor at the Xi’an Jiaotong University. His research interests include video understanding, computer vision, deep learning and multi-modal Learning. He teaches several courses including machine learning and deep learning (undergraduate), data mining (postgraduate).
Summary：In recent years, computer vision based on deep learning has made considerable progress. Computer vision mainly includes image classification, object detection, object tracking, semantic segmentation and instance segmentation, and is also widely used in the robot field. For vision-based robot grasping, there are three key tasks: object localization, object pose estimation and grasp estimation. The object localization task contains object localization without classification, object detection and object instance segmentation. This task provides the regions of the target object in the input data. The object pose estimation task mainly refers to estimating the 6D object pose and includes correspondence-based methods, template-based methods and voting-based methods, which affords the generation of grasp poses for known objects. The grasp estimation task includes 2D planar grasp methods and 6DoF grasp methods, where the former is constrained to grasp from one direction. These three tasks could accomplish the robotic grasping with different combinations. Although so many intelligent algorithms are proposed to assist the robotic grasping tasks, there are several challenges in practical applications, such as the insufficient information in data acquisition, the insufficient amounts of training data, the generalities in grasping novel objects and the difficulties in grasping transparent objects.
Topics Object Localization based on deep learning models
Deep Learning Object Pose Estimation
Robot Application Based on Computer Vision Application of Robot Grassing Robot Grasping Based on Computer Vision Challenges in Practical Application of Robot Grasping Security of Robot Grassing System
Chair: Assoc. Prof. Li Hengyu, Shanghai University,
Bio: Li Hengyu, male, born in November 1983, is an associate professor/doctoral advisor, and received a doctor's degree in mechanical and electronic engineering from Shanghai University in 2012. Main research fields: robot intelligent perception and control, multi robot cooperative control, etc. In recent years, in J Field. Rob.， IEEE Trans. Intell. Veh.，Nonlinear Dyn, Robot Auton Sys. and other journals published more than 100 academic papers, of which more than 40 were included in SCI (including more than 30 first and corresponding authors), and more than 80 were included in EI. 150 national invention patents have been applied, 85 of which have been authorized, including 13 patents transferred. As the project leader, he has completed more than 10 projects, including 2 projects from the National Natural Science Foundation of China and 3 projects from the Shanghai Science and Technology Commission. As the deputy project leader or technical director, he participated in and completed more than 20 provincial and ministerial level projects such as National Natural Science Key Fund, National Key R&D Plan, National 863 Plan, etc. Research achievements have successively won 2 first prizes of Shanghai Technological Invention Award (2011, 2017), 1 first prize of Shanghai Scientific and Technological Progress Award (2015), 1 first prize of China Automation Society for Scientific and Technological Progress Award (2022), 3 second prizes of Shanghai Technological Invention Award (2009, 2013, 2022), 1 second prize of Henan Provincial Scientific and Technological Progress Award (2021), and 1 first prize of Henan Provincial Department of Education for scientific and technological achievements (2021); The doctoral dissertation won the Third National Shangyin Excellent Mechanical Doctoral Dissertation Award (2013), Cai Guanshen, an outstanding young teacher of Shanghai University (2014), one hundred excellent scientific and technological papers of the Fourth China Association for Science and Technology (corresponding author, 2019), one Best paper of IEEE International Academic Conference (corresponding author, 2022), etc..
Summary: Scene understanding capabilities are critical for mobile robots. Once the scene is understood, the robot can obtain the semantics and location of objects in the scene to assist in other tasks. One of the technical routes to achieve scene understanding is to equip robots with vision-based scene parsing capabilities, which relies on fast and accurate semantic segmentation algorithms oriented to scene parsing. Semantic segmentation algorithms based on deep learning have been improving in accuracy in recent years, but inference speed is generally slow. To speed up inference, engineering tools such as quantization and ASIC are used. But these methods will reduce model accuracy. Thus, mobile robots urgently need a semantic segmentation algorithm for scene parsing with high accuracy and high speed, so as to achieve fast and accurate scene understanding.
In this workshop, we will discuss the following issues: 1) robot visual perception; 2) Complex scene segmentation; 3) Multi sensor fusion technology.
Visual Perception, Mobile Robot; Scene Parsing; Semantic Segmentation; Multi sensor fusion
Robot visual perception
Object and obstacle segmentation
Multi sensor fusion
Workshop 16 - Overview, design and application of spherical motion manipulators
Chair: Prof. Guanglei Wu, Dalian University of Technology, China
Bio: Dr. Guanglei Wu received his PhD in robotics from Aalborg University, Denmark, 2013, and worked as an industrial Postdoc fellow in Aalborg University from 2014 to 2016. He was a visiting scholar in the Research Institute in Communications and Cybernetics of Nantes (IRCCyN, currently reorganized as Laboratory of Digital Sciences of Nantes-LS2N) in June-July 2012, in McGill University in Aug. 2015, and in Aarhus University in 2020. Currently, he is a professor in School of Mechanical Engineering, Dalian University of Technology (DUT). His research interests include robotic technology, conceptual design and performance evaluation of robots, robot dynamics and control, industrial robots and their applications. He has published one monography by Springer, 9 Chinese patents, and over 80 peer-reviewed articles in international journals and conferences. He was the awardee of AIS Expert of Year 2021, DUT Xinghai 1000 Youth Talent program (2018-2020), Best paper award from IFToMM Asian MMS & CCMMS 2016, Academic Awards from Dept. Sci. Tech. Liaoning province, Longcheng Talent program by Changzhou Municipality. He has given over 10 keynote speeches for the international conferences. He is the referee for over 50 international journals and conferences in the fields of mechanisms and robots.
Summary: Spherical motion widely exists in robot system and its application, and has a very broad application prospect, which is one of the important research directions in the field of robot research at present. The research and development of the spherical manipulator is very important for the performance analysis and design of the new system to meet the new challenges brought by emerging markets. The research of spherical motion manipulator spans a large number of research fields, involving many topics. New parallel robots and actuators have been conceived to solve engineering problems, and future research problems, together with other problems that have not yet appeared, need to be solved to continuously improve the technical level of spherical motion generators. This report takes the spherical robot as the object, and makes an in-depth study on its type, structure, application background, design and prospect.
Workshop 17 - Application of Knowledge Graph in Software Engineering
Chair:Assoc. Prof. Wei Zheng, Northwestern Polytechnical University, China
Bio: Wei Zheng received the M.Sc and Ph.D degree in Computer Software and Theory from University of Northwestern Polytechnical, China. He is currently an associate professor with the Department of School of Software, University of Northwestern Polytechnical. His research focus is on knowledge graph assisted software security and intelligent software quality assurance. He is the author or co-author of more than 80 high quality research publications, which includes international journals of high impact or prestigious international conferences. Moreover, he has been a reviewer of several international journals and conferences.
Chair:Assist. Prof. Xiaoxue Wu, Yang zhou University, China
Bio: Xiaoxue Wu received the M.Sc and Ph.D degree from University of Northwestern Polytechnical, China. She is currently an assistant Professor of Yangzhou University. Her current research focus is software vulnerability analysis with machine learning and knowledge graph. She has published over 20 articles in high quality journals (TSE, TOSEM, ESEM, IST) and conferences (ICSE, ISSRE).
Summary: Software is the core of intelligent development such as robotics. How to quickly develop high-quality software products is a severe challenge of current software engineering. In the recent years, many researchers and practitioners have focused on AI assisted software engineering. However, existing research on intelligent software development is still at a relatively low level. Knowledge graph is an important cornerstone of artificial intelligence, and the existing research on knowledge graph is mainly oriented to open domain knowledge graph, which cannot be directly applied to the field of software engineering. In order to promote intelligent software development, knowledge graph construction and optimization related techniques and methods for software engineering problems need to be studied urgently. Including but not limited to: Software related knowledge representation, reason and use a knowledge base to solvecomplex software problems, such as software vulnerability detection, code clone detection, and software supply chain security, etc.
Workshop 18 - Simulation, control, and optimization towards intelligent bionic robots
Chair: Prof. Mingguo Zhao, Tsinghua University, China
Bio: Mingguo Zhao received the E., M.Sc., and Ph.D. degrees from the Harbin Institute of Technology in 1995, 1997, and 2001, respectively. From 2001 to 2003, he held a post-doctoral position with the Department of Precision Instruments at Tsinghua University, where he is currently a Professor with the Department of Automation. His current research interests include locomotion, whole-body control, and neuromorphic robotics of humanoid robots.
Summary: Nature's creatures have inspired and helped develop many contemporary human technologies. The real advancement of the world is only possible with the inspiration gained through animal models. In recent years, intelligent bionic robots have made rapid progress. They are developed to play a more and more important role in hard-working environments, rescue missions, space exploration, and other fields. However, many problems still need to be solved for intelligent bionic robots. Especially in unknown environments, robots' sensing data is heavily affected by light, moving objects, and measurement uncertainties. Furthermore, multiple sensing data must be fused, and the complexity of many algorithms is prohibitively high for real-world applications. As a result, more approaches to simulation, control, and optimization need to be proposed and studied for intelligent bionic robots.
Sensing, Planning, and Control of Intelligent Bionic Robots
Large-Scale Robots Intelligence
Workshop 19 - Novel sensors and actuators for robotics
Chair: Assoc. Prof. Taogang Hou, Beijing Jiaotong University, China
Bio: Taogang Hou received the B.E. degree and Ph.D. degree in mechanical engineering from Beihang University, Beijing, China, in 2016 and 2020. He is currently an associate professor with the School of Electronic and Information Engineering, Beijing Jiaotong University, China. He has been selected to join “Youth Talent Support Project” of the Chinese Association for Science and Technology. His research interests include intelligent robotics, visual perception under high-speed movement and smart transportation system. He is now a member of the Intelligent Robot Technical Committee of the China Computer Federation (CCF), a member of the Professional Committee of Cognitive Systems and Information Processing of the Chinese Association for Artificial Intelligence (CAAI), and a member of the Medical-Industrial Integration Professional Committee of the China Health Culture Association (CHCA). He has presided several projects supported by National Natural Science Foundation of China, Beijing Natural Science Foundation and China Postdoctoral Science Foundation. He has published more than 20 academic papers in journals such as Mech. Mach. Theory and IEEE International Conference on Robotics (ICRA). He has also guided students to win the Gold Prize of the“Challenge Cup” National College Student Entrepreneurship Competition and the First Prize of the"Challenge Cup" National Undergraduate Extracurricular Academic Science and Technology Contest.
Summary: This workshop is dedicated to new sensors and actuators for robotics, and algorithms for their processing data and control. Extensive research in robotics has spawned many new sensors and actuators, including event cameras (bio-inspired vision sensors with the key advantages of microsecond temporal resolution, low latency, and high dynamic range), biosensors, and soft actuators (shape memory alloys (SMAs), dielectric elastomer actuators (DEA), liquid metals, hydrogels, etc.). The applications of these new sensors and actuators in different scenarios of robotics have brought new challenges to their performance and control: 1) Algorithms for new vision sensors in traditional vision applications (visual odometry, SLAM, 3D reconstruction, optical flow estimation, etc.), 2) Promotion and application of novel sensors on robots (ground mobile robots, soft robots, UAV, etc.) 3) Structure design and control methods for more accurate soft actuators.
Workshop20 - Advanced Technologies and Intelligent Applicationsfor Unmanned Systems
Chair: Assoc. Prof. Jinchao Chen, Northwestern Polytechnical University, China
Bio: Jinchao Chen is an associate professor in School of Computer Science at Northwestern Polytechnical University, Xi'an, China. He received his M.S. and Ph.D. degree in Computer Science from the same institution in 2012 and 2016. He has several publications in top journals and conferences, including IEEE ITS, IEEE TIE, IEEE TII, IEEE VT, IEEE IoTJ, ESWA, SWARM EVOL COMPUT, RTSS, etc. He also serves as an Editor of some international Journals, such as International Journal of Aerospace Engineering, ASP transaction on Computers. He focuses on the multi-processor scheduling, embedded and real-time systems, decision-making and intelligent control of unmanned aerial vehicles. He is a member of IEEE and CCF.
Chair: Assist. Prof. Zhihua Chen, Nanchang Hangkong University, China
Bio: Zhihua Chen received the M.Sc. degree in mechatronics engineering at Beijing Information Science and Technology University, Beijing, China, in 2018. He received the Ph.D. degree of control science and engineering as a member of State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, China, in 2022. He is currently an assistant professor at School of Information Engineering, Nanchang Hangkong University, China. His current research interests are robot legged motion control, wheel-legged motion planning, obstacle avoidance. Dr. Chen has been awarded the Best Conference Paper Finalist of IEEE International Conference on Advanced Robotics & Mechatronics in 2021. He also serves as a Reviewer for some scientific journals, such as IEEE Transactions on Industrial Electronics, IEEE/ASME Transactions on Mechatronics, ISA Transactions, ASME Journal of Dynamic Systems, Measurement and Control, and so on.
Summary: In recent years, unmanned systems have attracted more and more research interests, especially in industrial robots, autonomous mobile robots, unmanned surface vehicle, and unmanned aerial vehicle, which can realize industrial intelligence and future war intelligence. The advanced technologies and intelligent applications for unmanned systems is a highly enlightening and promising topic. Meanwhile, with the integration with artificial intelligence, machine learning, data mining, signal processing and other technologies, many intelligent applications of unmanned systems are fast growing and widely applied. This invited workshop aims to bring world-class researchers to present state-of-the-art research achievements and advances that contribute to unmanned systems in terms of control theory, environment perception, sensor fusion, software engineering, and their intelligent applications.
Keywords: Unmanned systems,control technique, information processing, artificial intelligence
Advanced technique of unmanned systems
Integrated perception, information processing and autonomous control
Deep learning techniques for autonomous robots in perception and control
Cooperation and cluster control for unmanned systems
Human-robot interaction for autonomous manipulators
Artificial intelligence applications for autonomous vehicles
Data science in autonomous vehicle systems
Vehicle localization, mapping and connection
Collaborative perception and control of vehicle swarms
High safety and reliability communication networks
Simulation and verification of unmanned systems
Workshop 21 - Security and Privacy of Robotics System
Chair: Prof. Guotian He, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, China
Bio: Guotian He received the Master degree and PhD degreefrom Southwest University and the Graduate School of the Chinese Academy of Sciences in 1998 and 2006 respectively. He is now a talent of National“Ten Thousand Talents Program”and a professor, doctoral supervisor of Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences. He is mainly engaged in the researches such as robot intelligence evaluation, intelligent software development, force sensing technology and control technology. He is now the expert of the International ISO Service Robot Standards Expert Group, the expert of the National Robot Standards Overall Group, and the chief expert of Chongqing Robot Experts Studio.
Summary: In recent years, information security and privacy protection technologies have made considerable progress. The special requirements of security protection in the robotic field have brought new challenges to the current methods and technologies of information security and privacy protection. Researchers in the field of information security are trying to address these challenges in a variety of ways. The security and privacy protection for robotics system need sto solve the following problems, such as 1) data security and privacy protection for robotic system; 2) algorithm security detection and protection for robotics system; 3) application environment security detection and protection for robotics system; 4) robotic intelligent development security, etc. This will be an important driving force to promote the research and practice of information security and privacy protection technologies in the requirements, design, testing, maintenance and other aspects of robotics system.
Keywords: Information safety, security and privacy protection, robotics system, security detection
Hardware Analysis, Design and Inspection
Robot Hardware Architecture
Crowd-based Hardware Engineering
Hardware Maintenance and Development
Hardware Engineering Method
Hardware and System Testing methods
Safe of Robotics System
Security and Privacy of Robotics System
Workshop 22 - Bio-inspired Soft Robotics
Chair: Assoc. Prof. Xinghao Hu, Jiangsu University, China
Bio: Xinghao Hu, Ph.D., associate professor, graduated from the Department of Electrical Engineering of Xi'an Jiaotong University in 2019. He joined the School of Mechanical Engineering, Jiangsu University in August 2019, and worked as a postdoctoral fellow at the University of Texas at Dallas from March 2019 to July 2020. He is a senior member of Chinese Mechanical Engineering Society, Chinese Instrumentation Society, and International Society for Bionic Engineering. He won the Excellent Doctoral Dissertation of Shaanxi Province in 2021, and the Ninth "Top Ten Young Faculty and Staff" of Jiangsu University in 2022. He won the 2022 "MINE" Outstanding Young Scientist.
At present, he is engaged in research on the driving principle and application of artificial muscles, and has achieved systematic and original achievements in electrochemical artificial muscles and carbon nanotube fiber artificial muscles. He published over 16 SCI papers in internationally renowned academic journals such as Science, Advanced Functional Materials, he authorized 7 Chinese invention patents as the first inventor, and jointly applied for PCT and US invention patents.
Summary: The field of soft robotics has experienced tremendous growth over the past decade, drawing interest due to the utility of soft robots in applications involving the handling of fragile objects or close collaboration with humans, their relatively low cost and lightweight, and their resistance to impact and harsh conditions. To fully realize these advantages, researchers are now investigating methods to simplify or replace the existing hard, bulky control infrastructure ubiquitous to soft robots with a combination of onboard control methods and advances in external software control. Research in this area has been fast-paced, with many concurrent and complementary developments in just the past three years, but opportunities for further advances remain. This workshop will bring together a diverse group of researchers investigating the simplified control of soft robots to identify promising future directions.
This workshop intends to broaden the view of attendees by discussing the concept of soft robotics, fabrication of necessary hardware for implementing such systems, control of soft robot systems and how they can be used in applications such as exoskeleton systems, VR systems and industrial robotics.
Workshop 23 - Biomimetic robot and Biohybrid Systems
Chair: Assoc. Prof. Yunian Shen, Nanjing University of Science and Technology, China
Bio: Yunian Shen, an associate professor, is currently the director of the Department of Mechanics and Engineering Science, School of Science, Nanjing University of Science and Technology, the PI of Robotics and Intelligent Machine Laboratory, and the national outstanding teacher of Xu Zhilun Mechanics. He is a doctoral student jointly trained by Cambridge University from 2009 to 2010. He used to be a visiting scholar of Cambridge University in 2013 and Assistant Researcher of University of California, Berkeley in 2016-2017. His cooperative supervisor was Professor Bogy, an academician of the American Academy of Engineering. He served as the young editorial board member of the Journal of Applied Mechanics (in Chinese), the director of Jiangsu Mechanics Society, the member of the Solid Mechanics Committee and the Education Committee of Jiangsu Mechanics Society, the certified expert of high-tech enterprises in Jiangsu Province, the member of the National Science and Technology Expert Pool of the Ministry of Science and Technology, the member of the Technical Committee of the ACIRS International Conference, and the reviewer of various journals such as Nonlinear Dynamics, Mechanical and Machine Theory, Engineering Mechanics and Robotics (in Chinese). Mainly engaged in the mechanics+ intersection research of robot and intelligent machine, friction contact dynamics of walking robot, nanoscale wear of microstructure. He presided over nearly 20 projects, including the General Program and Youth Fund of the National Natural Science Foundation of China, the Doctoral Program Fund of the Ministry of Education, the General Program of the Natural Science Foundation of Jiangsu Province and the Innovation Project of the National Defense Science and Technology Zone. More than 40 high-quality papers were published. The guide won the first prize of national Challenge Cup and the Silver award of national Internet + Competition National.
Summary: The development of future real-world technologies will depend strongly on our understanding and harnessing of the principles underlying living systems and the flow of communication signals between living and artificial systems.
Biomimetics is emerged as the times required, which is the development of novel technologies through the distillation of principles from the study of biological systems. The investigation of biomimetic robot or systems can serve two complementary goals. First, a suitably designed and configured biomimetic artefact can be used to test theories about the natural system of interest. Second, biomimetic technologies can provide useful, elegant and efficient solutions to unsolved challenges in science and engineering. Biohybrid systems are formed by combining at least one biological component—an existing living system—and at least one artificial, newly-engineered component. By passing information in one or both directions, such a system forms a new hybrid bio-artificial entity.
Multi-Modal Robots: Bio-inspired robots that can transition between various kinds of locomotion, from swimming or jumping to flying to climbing vertical walls or branch.
Adhesion and Applications: Grasping and climbing vertical surfaces.
Soft robot: various kinds of bio-inspired soft robot.
Nature-inspired designs and manufacturing processes.
Active biomimetic materials and structures that self-organize and self-repair.
Organism-level biohybrids such as robot-animal or robot-human systems.
Workshop 24 - Quality Engineering for Robotics
Chair: Prof. Shaomin Zhu, Tongji University, China
Bio: Shaomin Zhu, Professor of Tongji University, Senior Member of CCF, Head of Standard Evaluation Group of Software Green Alliance. He has been engaged in software testing and quality management for nearly 30 years, and has won a few the Science and Technology Progress Awards in the provincial or ministerial level. He has published more than 20 books and 4 translations, and his representative works include Software Testing Methods and Techniques, Software Quality Assurance and Management, Full-lifecycle Software Testing, Agile Testing, etc. He was the Senior QA Director of the Cisco (China) Research & Development Center. He was the Chairman of IEEE ICST 2019 Industry Forum, a member of IEEE ICST, QRS and DSA, NASAC program, and a reviewer of Journal of Software, etc.
In recent years, the application of robots has become more and more common, not only in automotive welding, warehousing and logistics, indoor distribution, production line assembly and other fields, but also in surgery, urban emergency security, complex environment detection, etc. It is required that the robots are operated accurately, interact friendly, and with high performance, safety, and reliability in these application areas, which puts a serial of demands on the high quality of robots, thus the development and maintenance of robots is facing a great challenges. Therefore, we set up the "Quality Engineering for Robotics" workshop to bring together researchers and industry practitioners in this field to discuss performance engineering, safety engineering, reliability engineering, user experience (UX) engineering for robotics. It also includes design, analysis and testing to improve the quality of this area.
Workshop 25 -
Intelligent sensing, learning and control of multi-robot systems
Chair：Prof. Jiangping Hu, University of Electronic Science and Technology of China, China
Bio: Jiangping Hu received the B.S. degree in applied mathematics and the M.S. degree in computational mathematics from Lanzhou University, Lanzhou, China, in 2000 and 2004, respectively, and the Ph.D. degree in complex systems and control from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China, in 2007. He is currently a Professor with the School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China. His current research interests include distributed reinforcement learning, multi-robot control, and so on. He has published around 140 peer-reviewed papers receiving more than 6000 citation times. Dr. Hu has been IEEE Senior Member and served as members of Technical Committee on Control Theory, Chinese Association of Automation; also served as an Associate Editor for KYBERNETIKA and an Associate Editor for Journal of Systems Science and Complexity.
Compared with single robot system, multi-robot system has better cooperative positioning and navigation accuracy, better fault tolerance and robustness, can adapt to more complex environment, and can complete more diverse cooperative tasks. However, multi-robot system also faces a series of challenges, such as task space scheduling, motion space planning, task-oriented cooperation mechanism, optimization and control in dynamic environment, etc. The workshop includes but is not limited to the following
Keywords: Multi-robot system, collaborative sensing, distributed learning and control, planning and scheduling
Collaborative positioning and navigation
Task scheduling and assignment
Multi-unmanned autonomous system cooperation
Special robot applications
Workshop 26 - Robot interactive control in complex scenes
Chair: Prof. Yuancan Huang, Beijing Institute of Technology, China
Bio: Yuancan Huang the Bachelor degree, Master degree and PhD degree at the Harbin Institute of Technology in 1985, 1992 and 1995 respectively. He was a visiting scholar at the University of Geneva from 2002-2003. He is currently a Professor at the Beijing Institute of Technology. His research interests include medical rehabilitation robots, physical human-robot interaction control and continuum robot design and control, etc. He teaches "robotics" courses for undergraduates.
Summary: In recent years, robots have gradually stepped out of the isolated operating environment and accelerated their integration into human living and working space, playing an important role in life services, medical care, education and entertainment, helping the elderly and disabled, smart home and other fields. In the process of getting together with people for a long time and completing various interactive tasks, it can realize the "integration" with the surrounding environment and users. The physical interaction of direct contact between robots and humans or the environment has become an important way for robots to perform tasks, which puts forward higher requirements for the interaction ability between robots and humans. There is no doubt that the new human-robot interactive control needs to be solved in complex scenes, such as 1) the design of the universal framework of robot interactive control; 2) Passivity of interactive control of human-robot coupling system; 3) Stably and effectively present a wider and more real virtual environment for human beings. This will serve as an important driving force to promote the extensive application of human-computer interaction technology in key fields such as aviation, aerospace and navigation, as well as the improvement of basic human-robot interaction capabilities in key fields of robotics.
Topics: Interaction dynamics
Estimating the human user's intent
Human–robot interaction control
Human intent estimation
Robot control adaptation
Interactive system passivity
Apparent interactive dynamic performance
Workshop 27 - Intelligent Software Engineering
Chair: Prof. Zhenyan Ji, Beijing Jiaotong University, China
Bio: Zhenyan Ji, Professor, PhD supervisor of Beijing Jiaotong Univeristy. She received her Ph.D. degree from Institute
of Software, Chinese Academy of Sciences in 1999. She had worked for Norwegian University of Science and Technology from November 1999 to June 2000, and Mid Sweden University from July 2000 to September 2008. Her main research areas: software engineering, computer vision, and distributed systems. She has published more than 60 academic papers and authored 6 books in these areas. She teaches courses, System Analysis and Design, Software Architecture Design, and etc.
With the rapid development of artificial intelligence, artificial intelligence has been more and more widely used in the field of
software engineering. The related research fields include: intelligent software IP, software artifact knowledge base, intelligent
question and answering system, intelligent code generation and completion, intelligent document generation and completion, intelligent
software system integration, intelligent malware detection, intelligent testing, and so on. The application of artificial intelligence in software
engineering will reduce the costs of software development and maintenance, and improve the efficiency and quality of software development.
Design and implementation of intelligent software IP
Design and construction of Software artifact knowledge base
Intelligent question and answering system related to software engineering
Intelligent recommendation related to software engineering
Intelligent code generation and completion
Intelligent document generation and completion
Intelligent software system integration
Intelligent malware detection
Intelligent software testing and defect prediction
Workshop 28 -
Industrial Robot Digital Twins and Intelligent Applications
Chair: Assoc. Prof. Yongkui Liu, Xidian University, China
Bio: Yongkui Liu is an associate professor, doctoral supervisor, deputy director of the department of automatic control, and director of the research center of intelligent manufacturing systems and robotics at Xidian University. His research interests lie in robotic intelligent manufacturing, including human-robot collaboration, cloud-edge collaboration, digital twin, etc. He received his PhD degree in pattern recognition and intelligent systems from Xidian University in 2010. From 2011 to 2018, he successively did postdoctoral research at Beihang University, The University of Auckland, New Zealand, and KTH Royal Institute of Technology, Sweden, respectively. He serves as a member of professional committees of a number of academic societies, and an editor (or guest editor) of a number of international journals. He has published more than 60 academic papers and co-published 1 monograph in English.
Summary: In recent years the concept of digital twin has attracted much attention from both academia and industry. The core of the concept is to build a digital entity of a physical object in combination with the specific application requirement, which is able to accurately reflect, characterize and simulate the property, characteristic and behavior of the physical object, and finally monitor, control and optimize the physical object based on the two-way real-time interaction and feedback between them. Industrial robots are an important manufacturing equipment, which have been widely used in many manufacturing industries. The construction of digital twins of industrial robots is of significant research and application value. Digital twin provides an effective means for accurately describing geometry, physics, behavior, and rules of industrial robots, and can provide a powerful technique for the whole life-cycle applications, including robot optimization design, performance simulation, real-time monitoring, intelligent control, and fault diagnosis, etc. However, the current research on industrial robot digital twins and their applications is still at the very early stage, and there are still a lot of research and application problems to be solved.
Keywords:Industrial robot, digital twin, intelligent application
Topics: (include, but are not limited to)
High-fidelity digital twin model construction for industrial robots
Development of digital twin software systems for industrial robots
Finite element analysis of industrial robots
Multi-physics field analysis of industrial robots
Optimal design of industrial robots with digital twin
Simulation of industrial robots based on digital twin
Intelligent monitoring of industrial robots based on digital twin
Intelligent control of industrial robots based on digital twin
Human-machine collaboration based on digital twin
Cloud-edge-end collaborative control of industrial robots based on digital twin
Workshop 29 - Target detection, recognition, and tracking technology for robot vision
Chair: Prof. Luping Ji, University of Electronic Science and Technology, China
Bio: Luping Ji graduated from Beijing University of Technology in June 1999 with a bachelor's degree in mechanical and electronic engineering. Graduated from the University of Electronic Science and Technology in June 2005 and July 2008, respectively, with a master's degree in computer application technology and a doctor's degree in computer software and theory. In 2008, he graduated and stayed in the university. From September 2016 to September 2017, he was publicly funded to visit scholars at the University of Houston (USA). The main research directions are artificial intelligence, neural network, pattern recognition, image processing and data analysis. Presided over 2 general programs of the National Natural Science Foundation of China, a number of provincial and ministerial-level programs such as the Central University Scientific Research Fund and the Doctoral Program Fund of the Ministry of Education, and published more than 30 papers in journals and international conferences such as IEEE Transactions on Cybernetics, Pattern Recognition, IEEE Transactions on Systems, Man and Cybernetics and Neurocomputing, and served as reviewers of multiple journals for a long time, and authorized 6 Chinese invention patents.
Summary: Visual sensor is one of the main tools for robots to obtain the information of the surrounding environment. The main task of computer vision is to simulate the human visual system and realize the perception of the environment by processing the information obtained by the visual sensor. As one of the important research directions in the field of computer vision, target detection and tracking has been widely used in the fields of deep space detection, intelligent transportation, safety monitoring and intelligent robot environment perception. Therefore, the research on target detection, recognition and tracking has important theoretical significance and application value. The research topic of object detection for robot vision is currently mainly focused on single-frame object detection and multi-frame (video sequence) object detection. The key problems to be solved are: 1) new method of feature modeling of target image, 2) perception of target motion feature, 3) effective fusion of image feature and motion feature, 4) construction of new object detection framework and design of learning algorithm. The effective solution of these problems can further promote the development and engineering application of robotic vision technology.
Keywords: Robotic vision, Object detection, Feature modeling，Framework construction and learning algorithm
Object detection with Deep Learning
Infrared Dim-small Target Detection
Feature Modelling of Object Images
Motion Information Capturing & Modelling of Object Detection
Feature Fusion of Object Image and Motion
New Framework Design for Object Detection
Machine Learning Algorithms for Object Detection
Engineering Applications of Object Detection in intelligent Robotics
Workshop 30 - Machining Planning and Control of Industrial Robots
Chair: Prof. Jianwei Ma, Dalian University of Technology, China
Bio: Jianwei Ma, is currently a professor and a PhD candidate supervisor in School of Mechanical Engineering, Dalian University of Technology, China. The research team focuses on the direction of mechanical manufacturing, mainly engaged in the research work of surface machining and process control of difficult-to-machine materials, laser precision machining and process control, robot-assisted machining planning and control. He has successively undertaken more than 10 scientific research projects including the National Natural Science Foundation of China, the National Science and Technology Major Project of China, the National Key Research and Development Program of China, et al.
Summary: The structure of key parts for high-end equipment is becoming gradually complicated and large-scale, which poses challenges to high-precision machining and detection technology. Industrial robots represented by multi-axis serial robots are characterized by multiple degrees of freedom, strong flexibility and wide spatial accessibility. With corresponding machining and detection devices, industrial robots provide an effective solution for the in-situ integrated high-precision machining and detection of local complex features of key parts of high-end equipment. Due to the complex structure of key parts and the limited operating space of robots, some problems need to be solved in the process of robot machining and detection, including 1) difficult feature recognition of key parts, 2) robot end vibration caused by rapid changes of robot joint acceleration, 3) serious cumulative effect of robot end trajectory error, 4) increasing flexible effect of robot joints and links etc. The above technical problems have promoted the research of industrial robots in the fields of visual tracking and guidance, complex motion trajectory planning, end error compensation and accuracy improvement, and stiffness performance improvement etc. Nowadays, the machining planning and control of industrial robots has become one of the key technologies to realize high-precision machining and detection of key parts of high-end equipment.
Keywords:Industrial robot, integration of machining and detection, robot machining planning, robot control, machine vision