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

Engineering notes

This approach is applicable in scenarios where robots need to adapt their sampling strategies in real-time, such as in search and rescue missions or environmental monitoring tasks. It offers improvements over static coverage methods in dynamic settings.

Chinese explanation / 中文解读

本文探讨了多机器人适应性覆盖的问题，介绍了一种利用实时反馈的策略以应对未知环境条件。该方法提高了机器人在高关注区域的覆盖效率，从而优化资源使用。

Original 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.

Links and sources

• Official / arXiv page
• PDF from original source

Comments