Shun-ichi Azuma – författare

This book presents the state of the art in distributed autonomous systems composed of multiple robots, robotic modules, or robotic agents. Swarms in nature can not only adapt to their environments, but can also construct suitable habitats to their own advantage. Distributed autonomous robotic systems can do many things that its individuals cannot do alone. As the global pandemic was still ongoing, the 15th International Symposium on Distributed Autonomous Robotic Systems (DARS2021) was held on June 1–4, 2021, as an online meeting. The scope of DARS201 was to create a bridge between biologists and engineers interested in the distributed intelligence of living things and to establish a new academic field by integrating knowledge from both disciplines. Topics of DARS2021 were swarm intelligence, swarm robotics, multi-agent system, modular robotics, decentralized control, distributed system, etc. The papers in this book provide a very good overview of the state of the art in distributedautonomous robotic systems (DARS). They reflect current research themes in DARS with important contributions. We hope that this book helps to sustain the interest in DARS and triggers new research.

This textbook teaches control theory for multi-agent systems. Readers will learn the basics of linear algebra and graph theory, which are then developed to describe and solve multi-agent control problems. The authors address important and fundamental problems including:

• consensus control;

• coverage control;

• formation control;

• distributed optimization; and

• the viral spreading phenomenon.

Students'' understanding of the core theory for multi-agent control is enhanced through worked examples and programs in the popular Python language. End-of-chapter exercises are provided to help assess learning progress. Instructors who adopt the book for their courses can download a solutions manual and the figures in the book for lecture slides. Additionally, the Python programs are available for download and can be used for experiments by students in advanced undergraduate or graduate courses based on this text.

The broad spectrum of applications relevant to this material includes the Internet of Things, cyber-physical systems, robot swarms, communications networks, smart grids, and truck platooning. Additionally, in the spheres of social science and public health, it applies to opinion dynamics and the spreading of viruses in social networks. Students interested in learning about such applications, or in pursuing further research in multi-agent systems from a theoretical perspective, will find much to gain from Control of Multi-agent Systems. Instructors wishing to teach the subject will also find it beneficial.