State-transfer modeling collective behavior of multi-ball Bernoulli system based on local interaction forces

Abstract

Collective behavior observed in nature has been actively employed in swarm robotics. In order to better respond to external cues, the agents in such systems organize themselves in an ordered structure based on simple local rules. The central assumption, in swarm robotics, is that all agents in the system collaborate to fulfill a common goal. In nature, however, many multi-agent systems exhibit a more complex collective behavior involving a certain level of competition. One representative example of complex collective behavior is a multi-ball Bernoulli-ball system. In this paper, by extracting local force among the Bernoulli balls, we approximated the state-transfer model mapping interaction forces to observed behaviors. The results show that the collective Bernoulli-ball system spent 41% of its time on competitive behaviors, in which up to 84% of the interaction state is unorganized. The rest 59% of the time is spent on collaborative behavior. We believe that the novel proposed model opens new avenues in swarm robotics research.

Environmentally-Driven Morphogenesis of Reconfigurable Floating Robots using Markov Models

“…We propose that by utilizing a Markov decision process-inspired controller, guided by a Markov model, we can demonstrate and control different states of variable morphologies in the robotic system [20][21][22]. We hypothesize that by carefully manipulating the angular velocity of a bubble wall and leveraging the stochastic adhesive and de-adhesive behavior of the floating objects, we can achieve desired configurations and effectively control the system's emergent behaviors.…”