Risk-Aware Coverage Path Planning for Lunar Micro-Rovers Leveraging Global and Local Environmental Data

TL;DR

This paper introduces a risk-aware 3D coverage path planning algorithm for lunar micro-rovers that effectively balances exploration, energy efficiency, and obstacle avoidance using global and local environmental data.

Contribution

It presents a novel 3D myopic coverage path planning algorithm that integrates global and local data for lunar terrain exploration, improving efficiency and safety.

Findings

High coverage achieved with low energy consumption

Effective obstacle avoidance in complex terrain

Validated in both simulation and outdoor field tests

Abstract

This paper presents a novel 3D myopic coverage path planning algorithm for lunar micro-rovers that can explore unknown environments with limited sensing and computational capabilities. The algorithm expands upon traditional non-graph path planning methods to accommodate the complexities of lunar terrain, utilizing global data with local topographic features into motion cost calculations. The algorithm also integrates localization and mapping to update the rover's pose and map the environment. The resulting environment map's accuracy is evaluated and tested in a 3D simulator. Outdoor field tests were conducted to validate the algorithm's efficacy in sim-to-real scenarios. The results showed that the algorithm could achieve high coverage with low energy consumption and computational cost, while incrementally exploring the terrain and avoiding obstacles. This study contributes to the advancement of path planning methodologies for space exploration, paving the way for efficient, scalable and autonomous exploration of lunar environments by small rovers.