Legged Autonomous Surface Science In Analogue Environments (LASSIE): Making Every Robotic Step Count in Planetary Exploration
🤖 AI Summary
This work proposes a highly mobile legged robotic system to overcome the limitations of conventional wheeled rovers in traversing complex planetary terrains and conducting flexible scientific investigations. For the first time, the robot is equipped with in situ soil mechanical property measurement capabilities, integrating foot–terrain interaction sensing with multimodal geological data—including composition, thermal properties, and grain size. By incorporating a human-like scientific hypothesis reasoning mechanism, the system dynamically optimizes its exploration strategy based on real-time perception, enabling adaptive, collaborative exploration with human scientists. Validated in two planetary analog environments, the platform not only demonstrated efficient traversal across challenging terrain but also uncovered critical geological evolution clues through the fusion of soil mechanics and multimodal geological information, substantially enhancing scientific return.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
planetary exploration
legged robots
terrain traversal
adaptive data acquisition
soil mechanics
Innovation
Methods, ideas, or system contributions that make the work stand out.
legged robots
soil mechanics sensing
adaptive exploration
human-inspired algorithms
planetary analog environments
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