Ergodic Control Strategy for Multi-Agent Environment Exploration

TL;DR

This paper introduces an ergodic control strategy for multi-agent systems to explore environments by matching the agents' visitation distribution to a reference distribution modeled as a Mixture of Gaussians, ensuring comprehensive coverage.

Contribution

It proposes a novel centralized control algorithm that achieves ergodic exploration by timing agents' visits to Gaussian components, validated through simulations.

Findings

The control strategy effectively achieves ergodicity in simulated environments.

The algorithm ensures agents visit all Gaussian components proportionally.

Simulation results validate the proposed method's performance.

Abstract

In this study, an ergodic environment exploration problem is introduced for a centralized multi-agent system. Given the reference distribution represented by the Mixture of Gaussian (MoG), the ergodicity is achieved when the time-averaged robot distribution is identical to the given reference distribution. The major challenge associated with this problem is to determine proper timing for a team of agents (robots) to visit each Gaussian component in the reference MoG for ergodicity. The ergodic function is defined as a measure of ergodicity and the condition for convergence is derived based on timing analysis. The proposed control strategy provides relatively reasonable performance to achieve the ergodicity. We provide the formal algorithm for centralized multi-agent control to achieve the ergodicity and simulation results are presented for the validation of the proposed algorithm.