Reducing the Range of Perception in Multi-agent Patrolling Strategies

Multi-Agent Patrolling Problems consist in moving agents throughout a graph in order to optimize a collective performance metric. Some strategies from the literature tackle this problem by dispatching decentralized autonomous agents that coordinate themselves merely by sensing and writing information in the nodes. In this work, they are called k-range local strategies, were k indicates the range, in number of edges, of the agents’ sensing capabilities. The 1-range strategies (where agents can sense up to its neighbor nodes) are certainly the most common case in the literature. And only few 0-range strategies (where agents can only sense its current node) were found, although this type of strategy has the advantage of requiring simpler hardware, when applied in the design of real robots. In this work, we propose two higher-level procedures to reduce the perception range of 1-range strategies to 0: the Zr Method and the EZr Method. Applying both methods in 1-range strategies found in the literature, we created twenty new 0-range strategies, which were evaluated in a simulation experiment described and analyzed here. We also developed a prototype of a low-cost patrolling robot that is able to run the 0-range strategies proposed in this work.