Using Boolean Networks for Consensus in Multi-Robot Environmental Monitoring Tasks

Robotic systems have been shown to be effective for environmental monitoring tasks, in which one or more robots survey an environment for a particular event. Multi-robot systems, consisting of several interacting robots, have been successful in a variety of applications where their ability to accomplish tasks as a team surpasses the abilities and capacities of a single robot. However, multi-robot systems can generate a large degree of complexity due to the required coordination of movement, communication, and the tolerance for incorrect sensor readings. In this paper, we present a novel approach to multi-robot environmental monitoring based on dynamical systems, in which a robot team overcomes data misinterpretation and aggregation difficulties through an effort of collaboration between all members of the team. Our approach makes use of Boolean networks, which allow for a non-complex method of corroboration, while still retaining meaningful information regarding the dynamics of the robotic system. Using our Boolean network model we apply mathematical tools from dynamical systems and chaos theory to analyze the overall behavior of the robotic dynamic over time. Here we observe how different parameters affect the behaviors of the system. We also empirically and experimentally show that, despite the simplification of the robots' states into Boolean states, our Boolean network model produces accurate results when compared to real events of the environment.