Flocking-Segregative Swarming Behaviors using Gibbs Random Fields
This addresses the challenge of executing complex tasks like surveillance and search-and-rescue with swarms, though it is incremental as it builds on existing segregative behavior research.
The paper tackles the problem of enabling a heterogeneous robot swarm to simultaneously exhibit segregative and flocking behaviors using only local sensing, achieving performance comparable to state-of-the-art methods in simulations and real-robot experiments.
This paper presents a novel approach that allows a swarm of heterogeneous robots to produce simultaneously segregative and flocking behaviors using only local sensing. These behaviors have been widely studied in swarm robotics and their combination allows the execution of several complex tasks, ranging from surveillance and reconnaissance, to search and rescue, to transport, and to foraging. Although there are several works in the literature proposing different strategies to achieve these behaviors, to the best of our knowledge, this paper is the first to propose an algorithm that emerges simultaneously behaviors and do not rely on global information or communication. Our approach consists of modeling the swarm as a Gibbs Random Field (GRF) and using appropriate potential functions to reach segregation, cohesion and consensus on the velocity of the swarm. Simulations and proof-of-concept experiments using real robots are presented to evaluate the performance of our methodology in comparison to some of the state-of-the-art works that tackle segregative behaviors.