Safe Autonomy with Control Barrier Functions

This book presents the concept of Control Barrier Function (CBF), which captures the evolution of safety requirements during the execution of a system and can be used to enforce safety. Safety is formalized using an emerging state-of-the-art approach based on CBFs, and many illustrative examples from autonomous driving, traffic control, and robot control are provided. Safety is central to autonomous systems since they are intended to operate with minimal or no human supervision, and a single failure could result in catastrophic results. The authors discuss how safety can be guaranteed via both theoretical and application perspectives. This presented method is computationally efficient and can be easily implemented in real-time systems that require high-frequency reactive control. In addition, the CBF approach can easily deal with nonlinear models and complex constraints used in a wide spectrum of applications, including autonomous driving, robotics, and traffic control. Withthe proliferation of autonomous systems, such as self-driving cars, mobile robots, and unmanned air vehicles, safety plays a crucial role in ensuring their widespread adoption. This book considers the integration of safety guarantees into the operation of such systems including typical safety requirements that involve collision avoidance, technological system limitations, and bounds on real-time executions. Adaptive approaches for safety are also proposed for time-varying execution bounds and noisy dynamics.

Presents safe autonomy including the theoretical development, solutions, and analysis of feasibility guarantees Discusses how the CBF approach can be used for most autonomous systems and proposes safety guarantees Establishes the basic concepts, definitions, and algorithms required to understand the CBF approach

Autorentext

Wei Xiao, Ph.D., is a Postdoctoral Associate at Massachusetts Institute of Technology. He received a B.Sc. from the University of Science and Technology Beijing, a M.Sc. degree from the Chinese Academy of Sciences (Institute of Automation), and a Ph.D. from Boston University. His research interests include control theory and machine learning with particular emphasis on robotics and traffic control. He received an Outstanding Student Paper Award at the 2020 IEEE Conference on Decision and Control. Christos G. Cassandras, Ph.D., is a Distinguished Professor of Engineering at Boston University. He is Head of the Division of Systems Engineering, Professor of Electrical and Computer Engineering, and co-founder of Boston University's Center for Information and Systems Engineering (CISE). He received a B.S. from Yale University, a M.S.E.E from Stanford University, and S.M. and Ph.D. degrees from Harvard University. He specializes in the areas of discrete event and hybrid systems, cooperative control, stochastic optimization, and computer simulation, with applications to computer and sensor networks, manufacturing systems, and transportation systems. He has published over 450 refereed papers in these areas and six books. He has guest-edited several technical journal issues and serves on several journal editorial boards. In addition to his academic activities, he has worked extensively with industrial organizations on various systems integration projects and the development of decision-support software. He has most recently collaborated with MathWorks, Inc. in the development of the discrete event and hybrid system simulator SimEvents. Calin Belta, Ph.D, is a Professor in the Department of Mechanical Engineering at Boston University, where he holds the Tegan family Distinguished Faculty Fellowship. He is also the Director of the BU Robotics Lab. He received B.Sc. and M.Sc. degrees from the Technical University of Iasi and M.Sc. and Ph.D. degrees from the University of Pennsylvania. His research interests include dynamics and control theory, with particular emphasis on hybrid and cyber-physical systems, formal synthesis and verification, and applications in robotics and systems biology. He has received the Air Force Office of Scientific Research Young Investigator Award and the National Science Foundation CAREER Award. He is a Fellow and Distinguished Lecturer of IEEE.

Klappentext

This book presents the concept of Control Barrier Function (CBF), which captures the evolution of safety requirements during the execution of a system and can be used to enforce safety. Safety is formalized using an emerging state-of-the-art approach based on CBFs, and many illustrative examples from autonomous driving, traffic control, and robot control are provided. Safety is central to autonomous systems since they are intended to operate with minimal or no human supervision, and a single failure could result in catastrophic results. The authors discuss how safety can be guaranteed via both theoretical and application perspectives. This presented method is computationally efficient and can be easily implemented in real-time systems that require high-frequency reactive control. In addition, the CBF approach can easily deal with nonlinear models and complex constraints used in a wide spectrum of applications, including autonomous driving, robotics, and traffic control. Withthe proliferation of autonomous systems, such as self-driving cars, mobile robots, and unmanned air vehicles, safety plays a crucial role in ensuring their widespread adoption. This book considers the integration of safety guarantees into the operation of such systems including typical safety requirements that involve collision avoidance, technological system limitations, and bounds on real-time executions. Adaptive approaches for safety are also proposed for time-varying execution bounds and noisy dynamics.

Inhalt
Introduction to Autonomy and Safety.- Control Barrier Functions.- High Order Control Barrier Functions.- Feasibility Guarantees for CBFs.- Adaptive CBFs.- Safety Guarantees for Systems with Unknown Dynamics.- Applications to Traffic Networks.- Applications to Robotics.- Conclusions and Future Directions.