Legged Autonomous Surface Science In Analogue Environments (LASSIE): Making Every Robotic Step Count in Planetary Exploration

TL;DR

This paper introduces innovative legged robots and adaptive data algorithms for planetary exploration, enhancing terrain traversal and scientific data collection through in-situ measurements and human-inspired decision-making.

Contribution

It presents novel high-mobility legged robots and adaptive exploration algorithms that improve planetary surface exploration and scientific data acquisition.

Findings

Legged robots effectively traverse challenging terrains using soil mechanical measurements.

Soil property data helps understand geologic environment formation.

Human-inspired algorithms enable flexible, adaptive exploration decisions.

Abstract

The ability to efficiently and effectively explore planetary surfaces is currently limited by the capability of wheeled rovers to traverse challenging terrains, and by pre-programmed data acquisition plans with limited in-situ flexibility. In this paper, we present two novel approaches to address these limitations: (i) high-mobility legged robots that use direct surface interactions to collect rich information about the terrain's mechanics to guide exploration; (ii) human-inspired data acquisition algorithms that enable robots to reason about scientific hypotheses and adapt exploration priorities based on incoming ground-sensing measurements. We successfully verify our approach through lab work and field deployments in two planetary analog environments. The new capability for legged robots to measure soil mechanical properties is shown to enable effective traversal of challenging terrains. When coupled with other geologic properties (e.g., composition, thermal properties, and grain size data etc), soil mechanical measurements reveal key factors governing the formation and development of geologic environments. We then demonstrate how human-inspired algorithms turn terrain-sensing robots into teammates, by supporting more flexible and adaptive data collection decisions with human scientists. Our approach therefore enables exploration of a wider range of planetary environments and new substrate investigation opportunities through integrated human-robot systems that support maximum scientific return.