Multi-Robot Systems: Design, Control and Applications

Dear Colleagues,

Multi-robot systems are a field of study that deals with the design, control and application of multiple robots working together to achieve a common goal. In recent years, researchers working in the field of robotics have conducted much research on multi-robot systems with the aim of performing complex tasks that would be impossible with a single robot, and can also provide redundancy and increased robustness in the face of system failures. In addition, the system has the ability to observe, in a broad sense, an event from multiple viewpoints simultaneously for a better understanding of the context, presenting itself to the human operator as a single creature to be governed. The fusion of sensors and actuators physically distributed in space also makes it possible to perform tasks that would be impossible for the single robot. Indeed, this implies the presence of an additional layer of control, to share work between machines and a methodology of cooperation between them. The design of multi-robot systems includes the selection of appropriate robot platforms, the development of communication and coordination methods, and the implementation of control algorithms that enable robots to work together effectively. The challenge of designing and developing a control system on this topic has, therefore, gained interest. There are many definitions of multi-robot systems; here, we will stick to the more general meaning of multi-agents. Research in this field is interdisciplinary and draws on techniques and concepts from fields such as robotics, control theory, computer science and artificial intelligence. The study of multi-robot systems is still relatively new and ongoing, and many interesting developments and opportunities are expected in the future.

Despite significant research developments in this field, many technical challenges remain to be overcome in order to create technically efficient and cost-effective multi-robot systems. These challenges include communications, relative position sensing, the fusion of distributed sensors or actuators, human–machine interfaces and the creation of appropriate control paradigms. The economic aspect also needs to be studied.

Research on multi-robot systems is an active and rapidly evolving field, with new developments and advances occurring regularly. Some recent trends and new developments in the field include the following:

• Development of swarm intelligence algorithms: Researchers are developing new algorithms based on the concept of swarm intelligence, which allow multiple robots to work together in a decentralised and adaptive manner to achieve a common goal. These algorithms can be applied to a wide range of tasks, such as formation control, scouting and target tracking.
• Use of deep learning techniques: The integration of deep learning techniques into multi-robot systems is an area of active research. This allows robots to learn from data and adapt to new situations, improving their performance and autonomy.
• Multi-robot systems for aerial and underwater tasks. These environments pose unique challenges, such as limited communication ranges, and researchers are developing new methods to address them.
• Multi-robot systems for search, rescue and inspection. These systems can be equipped with cameras, sensors and other devices to explore, inspect and map hard-to-reach areas.
• Human–robot interaction: The study of how humans and robots can work together effectively. This includes the development of interfaces and methods that allow humans to easily control and interact with multiple robots.

Overall, there are many new and exciting developments in the field of multi-robot systems and many opportunities for further research.

This Special Issue aims to discuss the state of the art in this field and provide an overview of the technology and developments needed to exploit the full potential of multi-robot systems.

Dr. Ramiro Dell’Erba
Guest Editor

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• multi-robot
• swarm robotics
• intelligence
• cooperative robot
• deep learning