Adaptivity: a path towards general swarm intelligence?

Hian Lee Kwa, Jabez Leong Kit, Nikolaj Horsevad, Julien Philippot, Mohammad Savari, Roland Bouffanais

Abstract: The field of multi-robot systems (MRS) has recently been gaining increasing popularity among various research groups, practitioners, and a wide range of industries. Compared to single-robot systems, MRS are able to perform tasks more efficiently or accomplish objectives that are simply not feasible with a single unit. This makes such MRS ideal candidates for carrying out distributed tasks in large environments-e.g., performing object retrieval, mapping or surveillance. However, the traditional approach to MRS using global planning and centralized operation is in general ill-suited for fulfilling tasks in unstructured and dynamic environments. Swarming MRS have been proposed to deal with such steep challenges, primarily owing to its adaptivity. These qualities are expressed by the system's ability to learn or change its behavior in response to new and/or evolving operating conditions. Given its importance, in this perspective, we focus on the critical importance of adaptivity for effective MRS swarming and use it as the basis for defining, and potentially quantifying, swarm intelligence. In addition, we highlight the importance of establishing a suite of benchmark tests to measure a swarm's level of adaptivity. We believe that a focus on achieving increased levels of swarm intelligence through the focus on adaptivity, will be able to further elevate the field of swarm robotics.

External IDs:dblp:journals/firai/KwaKHPSB23