This book constructs a model of the knowledge value chain in the university and analyzes the university knowledge value-added mechanism in the process of Industry-University Collaborative Innovation. The efficiency of university knowledge value-added of Provinces in China is measured. The book illustrates the operating mechanism between enterprise subsystems and college subsystems in the collaborative innovation system, and establishes a Data Envelopment Analysis (DEA) model with parallel decision making units to assess the performance of Industry-University Collaboration Innovation in China by considering the complex internal structure of the collaborative innovation system. The book also addresses various behaviors of knowledge agents in the knowledge sharing process. The research findings of this book will provide some policy implications to help policy makers to establish a more effective collaborative and interactive innovation system. The focus on China offers a unique contribution, because the form that university-industry collaborations take differs widely from country to country. The United States, the United Kingdom, Japan, and China differ vastly in the way that they implement their respective R&D policies. Some of these differences stem from national culture, others from the historical evolution of the institutions that support innovation efforts, and some from the extent of available resources.

This book introduces readers to the principles and practical applications of intelligent robot system with robot operating system (ROS), pursuing a task-oriented and hands-on approach. Taking the conception, design, implementation, and operation of robot application systems as a typical project, and through “learning-by-doing, practicing-while-learning” approach, it familiarizes readers with ROS-based intelligent robot system design and development step by step. The topics covered include ROS principles, mobile robot control, Lidar, simultaneous localization and mapping (SLAM), navigation, manipulator control, image recognition, vision calibration, object grasping, vision SALM, etc., with typical practical application tasks throughout the book, which are essential to mastering development methods for intelligent robot system. Easy to follow and rich in content, the book can be used at colleges and universities as learning material and a teaching reference book for “intelligent robot,” “autonomous intelligent system,” “robotics principles,” and “robot system application development with ROS” in connection with automation, robotics engineering, artificial intelligence (AI), mechatronics, and other related majors. The book can assist in mastering the development and design of robot systems and provide the necessary theoretical and practical references to cultivate robot system development capabilities and can be used as teaching material for engineering training and competitions, or for reference, self-study, and training by engineering and technical personnel, teachers, and anyone who wants to engage in intelligent robot system development and design.

This book constructs a model of the knowledge value chain in the university and analyzes the university knowledge value-added mechanism in the process of Industry-University Collaborative Innovation. The efficiency of university knowledge value-added of Provinces in China is measured. The book illustrates the operating mechanism between enterprise subsystems and college subsystems in the collaborative innovation system, and establishes a Data Envelopment Analysis (DEA) model with parallel decision making units to assess the performance of Industry-University Collaboration Innovation in China by considering the complex internal structure of the collaborative innovation system. The book also addresses various behaviors of knowledge agents in the knowledge sharing process. The research findings of this book will provide some policy implications to help policy makers to establish a more effective collaborative and interactive innovation system. The focus on China offers a unique contribution, because the form that university-industry collaborations take differs widely from country to country. The United States, the United Kingdom, Japan, and China differ vastly in the way that they implement their respective R&D policies. Some of these differences stem from national culture, others from the historical evolution of the institutions that support innovation efforts, and some from the extent of available resources.