MacroSwarm: A Field-based Compositional Framework for Swarm Programming

TL;DR

MacroSwarm introduces a novel field-based, compositional framework for programming swarm behaviors, enabling flexible, reusable, and resilient control of collective agent actions through aggregate computing principles.

Contribution

It presents MacroSwarm, a new macroprogramming approach for swarm behavior design using functional blocks and collective computation, enhancing expressiveness and modularity.

Findings

Successfully demonstrated common swarm patterns like flocking and pattern formation.

Showed MacroSwarm's compositionality and expressiveness through simulations.

Discussed self-stabilisation properties that ensure resilience in swarm behaviors.

Abstract

Swarm behaviour engineering is an area of research that seeks to investigate methods and techniques for coordinating computation and action within groups of simple agents to achieve complex global goals like pattern formation, collective movement, clustering, and distributed sensing. Despite recent progress in the analysis and engineering of swarms (of drones, robots, vehicles), there is still a need for general design and implementation methods and tools that can be used to define complex swarm behaviour in a principled way. To contribute to this quest, this article proposes a new field-based coordination approach, called MacroSwarm, to design and program swarm behaviour in terms of reusable and fully composable functional blocks embedding collective computation and coordination. Based on the macroprogramming paradigm of aggregate computing, MacroSwarm builds on the idea of expressing each swarm behaviour block as a pure function, mapping sensing fields into actuation goal fields, e.g., including movement vectors. In order to demonstrate the expressiveness, compositionality, and practicality of MacroSwarm as a framework for swarm programming, we perform a variety of simulations covering common patterns of flocking, pattern formation, and collective decision-making. The implications of the inherent self-stabilisation properties of field-based computations in MacroSwarm are discussed, which formally guarantee some resilience properties and guided the design of the library.