Eclares: Energy-Aware Clarity-Driven Ergodic Search

TL;DR

Eclares is a novel framework that combines clarity-driven ergodic search with energy-aware trajectory validation to enable persistent, information-rich coverage in stochastic environments while respecting robot energy constraints.

Contribution

The paper introduces a new method to construct target information distributions using clarity and an energy-aware filter for ergodic search, addressing stochastic environments and battery constraints.

Findings

Effective information decay modeling in stochastic environments

Energy-aware filter ensures robot can return to charging station

Simulation demonstrates improved persistent coverage

Abstract

Planning informative trajectories while considering the spatial distribution of the information over the environment, as well as constraints such as the robot's limited battery capacity, makes the long-time horizon persistent coverage problem complex. Ergodic search methods consider the spatial distribution of environmental information while optimizing robot trajectories; however, current methods lack the ability to construct the target information spatial distribution for environments that vary stochastically across space and time. Moreover, current coverage methods dealing with battery capacity constraints either assume simple robot and battery models, or are computationally expensive. To address these problems, we propose a framework called Eclares, in which our contribution is two-fold. 1) First, we propose a method to construct the target information spatial distribution for ergodic trajectory optimization using clarity, an information measure bounded between [0,1]. The clarity dynamics allows us to capture information decay due to lack of measurements and to quantify the maximum attainable information in stochastic spatiotemporal environments. 2) Second, instead of directly tracking the ergodic trajectory, we introduce the energy-aware (eware) filter, which iteratively validates the ergodic trajectory to ensure that the robot has enough energy to return to the charging station when needed. The proposed eware filter is applicable to nonlinear robot models and is computationally lightweight. We demonstrate the working of the framework through a simulation case study.