Learning Heterogeneous Multi-Agent Allocations for Ergodic Search

Information-based coverage directs robots to move over an area to optimize a pre-defined objective function based on some measure of information. Our prior work determined that the spectral decomposition of an information map can be used to guide a set of heterogeneous agents, each with different sensor and motion models, to optimize coverage in a target region, based on a measure called ergodicity. In this paper, we build on this insight to construct a reinforcement learning formulation of the problem of allocating heterogeneous agents to different search regions in the frequency domain. We relate the spectral coefficients of the search map to each other in three different ways. The first method maps agents to predefined sets of spectral coefficients. In the second method, each agent learns a weight distribution over all spectral coefficients. Finally, in the third method, each agent learns weight distributions as parameterized curves over coefficients. Our numerical results demonstrate that distributing and assigning coverage responsibilities to agents depending on their sensing and motion models leads to 40%, 51%, and 46% improvement in coverage performance as measured by the ergodic metric, and 15%, 22%, and 20% improvement in time to find all targets in the search region, for the three methods respectively.