Swarm robotics is a discipline that studies fully decentralized approaches for the coordination of large-scale teams of robots (swarms). Research in this field is ambitious: robot swarms are envisioned for scenarios for which solutions are today impractical, too dangerous, or inexistent.

From drones to self-driving cars, robot swarms will become pervasive thanks to the development of the Internet-of-Things, and will be used in many applications. Examples of such applications are search and rescue operations, industrial and agricultural inspection, coordinated vehicle platooning, space exploration, and medical or surgical activities. We envision a world where a designer can specify the behaviour of heterogeneous groups of robots, and package this behaviour in an application that can be installed on multiple robotic systems. Swarm-based solutions will likely form the backbone for the upcoming self-driving car infrastructure, and will act as an enabling technology to make widespread robotics a reality.

While it seems natural to deal with robot swarms as yet another instance of a classical distributed system, important aspects set the former apart from the latter. The dynamics of robot swarms are characterized by an inseparable mixture of spatial and network aspects. Spatial aspects include the fact that robots move, and modify their surrounding environment, while network aspects include a communication modality based on range-limited, gossip-based message passing, and an ever changing topology due to robot navigation across the environment. As a result, the mapping between swarm-level requirements and individual actions is a problem whose solution exceeds current approaches to distributed system design. Designing and developing swarm behaviors is achieved today through a slow trial-and-error process, in which the expertise of the designer and his or her ability to encode complex behaviors are the main factors for success.