Spatio-Temporal Patterns Act as Computational Mechanisms Governing Emergent Behavior in Robotic Swarms

Abstract

Our goal is to control a robotic swarm without removing its swarm-like nature.In other words, we aim to intrinsically control a robotic swarms emergent behavior.Past attempts at governing robotic swarms or their self-coordinating emergent behavior, has proven ineffective, largely due to the swarms inherent randomness (making it difficult to predict) and utter simplicity (they lack a leader, any kind of centralized control, long-range communication, global knowledge, complex internal models and only operate on a couple of basic, reactive rules).The main problem is that emergent phenomena itself is not fully understood, despite being at the forefront of current research.Research into 1D and 2D Cellular Automata has uncovered a hidden computational layer which bridges the micro-macro gap (i.e.how individual behaviors at the micro-level influence the global behaviors on the macro-level).We hypothesize that there also lies embedded computational mechanisms at the heart of a robotic swarms emergent behavior.To test this theory, we proceeded to simulate robotic swarms (represented as both particles and