Mind the Gaps: Multi-Robot Feedback-Driven Ergodic Coverage in Unknown Environments

TL;DR

This paper introduces an adaptive multi-robot coverage method that uses real-time feedback to improve exploration efficiency in unknown environments by updating target distributions online.

Contribution

It extends ergodic search by incorporating online environmental model updates, enabling robots to adaptively focus on high-interest regions in unknown or slowly changing environments.

Findings

Enhanced coverage efficiency demonstrated in simulations.

Improved resource allocation through adaptive targeting.

Effective trajectory synthesis for multi-robot systems.

Abstract

In this work, we address the problem of multi-robot adaptive coverage, where teams of robots perform dynamic sampling by continuously adjusting their positions to collect data in an environment. This task can be challenging, particularly when robots must be efficiently allocated to new sampling locations over time. Ergodic search methods optimize robot trajectories by ensuring that the robots' time-averaged spatial distribution aligns with the spatial distribution of environmental information. While these methods promote effective exploration provided a target distribution, they often fail to account for unknown prior distributions of the environment. To overcome this limitation, we propose an adaptive coverage strategy that utilizes real-time feedback from an environmental model to adjust robot sampling behavior in response to unknown conditions. Our approach enhances traditional ergodic trajectory optimization by constructing a target spatial information distribution based on parametric models of the environment, which are updated online. This strategy assumes that the environment is either static or changes slowly compared to the robot's motion. Our framework allows robots to dynamically prioritize regions of high interest, improving coverage efficiency, synthesizing effective control policies for individual agents, and optimizing resource use in settings with unknown prior distributions. We validate our approach through simulations, demonstrating its effectiveness in enhancing coverage and resource allocation.