Ergodic Control Strategy for Multi-Agent Environment Exploration
This addresses the challenge of coordinating multi-agent timing for ergodic exploration, but it appears incremental as it builds on existing ergodic control concepts without major breakthroughs.
The study tackled the problem of achieving ergodic environment exploration for a centralized multi-agent system by matching the time-averaged robot distribution to a given Mixture of Gaussian reference distribution, with the proposed control strategy providing relatively reasonable performance as validated through simulations.
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.