We present the proposed strategic study, ‘Integrated elements for Martian life signature exploration’, to support the sampling and identification of any potential biosignatures in compliance with the engineering constraints of the Tianwen-3 mission.

Microwave emission of the Moon, measured by the Chang’E-2 Microwave Radiometer (MRM), provides an effective way to understand the physical properties of lunar near-surface materials. The observed microwave brightness temperature is affected by near-surface temperatures, which are controlled by the surface albedo, roughness, regolith thermophysical properties, and the high thermal inertia and permittivity of both surface and buried rocks. In this study, we propose a rock model using thermal infrared measurements from the Lunar Reconnaissance Orbiter's (LRO) Diviner as surface temperature constraints. We then retrieve the volumetric rock abundance (RA) from nighttime MRM data at several rocky areas. Although our retrieved MRM RA cannot be compared to the rock concentration measured with LRO Camera images directly, there is a good agreement with Diviner-derived RA and radar observations. The extent of several geological units, including rocky craters, hummocky regions, and impact melts, agree well with the distribution of elevated rock concentration. Based on seven large craters with published model ages, we present an inverse correlation between rock concentration and crater age. The result shows that the rock concentration decreases with crater age rapidly within 1 Ga but declines slowly after that. These data are consistent with a short survival time for exposed rocks and a long lifetime for buried rocks that are shielded from lunar surface processes.

Path-planning algorithms for planetary rovers are critical for autonomous robotic exploration, enabling the efficient and safe traversal of complex and dynamic extraterrestrial terrains. Unlike terrestrial mobile robots, planetary rovers must navigate highly unpredictable environments influenced by diverse factors such as terrain variability, obstacles, illumination conditions, and temperature fluctuations, necessitating advanced path-planning strategies to ensure mission success. This review comprehensively synthesizes recent advancements in planetary rover path-planning algorithms. First, we categorize these algorithms from a constraint-oriented perspective, distinguishing between internal rover state constraints and external environmental constraints. Next, we examine rule-based path-planning approaches, including graph search-based methods, potential field methods, sampling-based techniques, and dynamic window approaches, analyzing representative algorithms in each category. Subsequently, we explore bio-inspired path-planning methods, such as evolutionary algorithms, fuzzy computing, and machine learning-based approaches, with a particular emphasis on the latest developments and prospects of machine learning techniques in planetary rover navigation. Finally, we synthesize key insights from existing algorithms and discuss future research directions, highlighting their potential applications in planetary exploration missions.