arXiv subjects

Kaichang Di

Publications and source records attributed to Kaichang Di.

2 recordsLinked to original sources

arXiv2026

Mining beyond Earth with Space Robots: Exploration, Sampling, and Extraction

Space resource acquisition and utilization, commonly referred to as Space Mining, represent critical pathways for enabling sustained human exploration and unlocking commercial opportunities in space. These resources mainly include helium-3, water, mineral resources on the Moon and Mars, and abundant mineral deposits on asteroids. Due to the harsh conditions of space, communication delays, and high launch costs, the development of autonomous robotic systems is critical to achieving efficient, cost-effective space mining. This paper provides a comprehensive overview of space mining robotics and associated technologies. First, we review the background of space mining, including international policies, commercial entities, and recent advancements. We define a systematic six-stage architecture for space mining: Exploration is initiated by (1) remote sensing for target identification and (2) precise in situ robotic detection; Sampling progresses from (3) single-robot small-scale sampling to (4) multi-robot large-scale excavation; and Extraction integrates (5) autonomous resource extraction and (6) final integration into in situ construction or terrestrial transport. Additionally, we review and curate existing resources for space mining research, including real-world mission data, terrestrial analog datasets, and high-fidelity simulation environments. Finally, we identify critical open challenges in autonomous space mining and delineate a strategic research roadmap to bridge current technological gaps, fostering the transition toward a sustainable off-world economy. To track ongoing developments in space mining, we maintain an updated project page: https://github.com/OpenSpace-Lab/Space-Mining-with-Robotics-List.

cs.RO↗

arXiv2025

History-Augmented Contrastive Learning With Soft Mixture of Experts for Blind Super-Resolution of Planetary Remote Sensing Images

Blind Super-Resolution (BSR) in planetary remote sensing constitutes a highly ill-posed inverse problem, characterized by unknown degradation patterns and a complete absence of ground-truth supervision. Existing unsupervised approaches often struggle with optimization instability and distribution shifts, relying on greedy strategies or generic priors that fail to preserve distinct morphological semantics. To address these challenges, we propose History-Augmented Contrastive Mixture of Experts (HAC-MoE), a novel unsupervised framework that decouples kernel estimation from image reconstruction without external kernel priors. The framework is founded on three key innovations: (1) A Contrastive Kernel Sampling mechanism that mitigates the distribution bias inherent in random Gaussian sampling, ensuring the generation of plausible kernel priors via similarity constraints; (2) A History-Augmented Contrastive Learning strategy that leverages historical model states as negative self-priors. We provide a theoretical analysis demonstrating that this mechanism induces strong convexity in the feature space, thereby stabilizing the unsupervised optimization trajectory and preventing overfitting; and (3) A Morphology-Aware Soft Mixture-of-Experts (MA-MoE) estimator that dynamically modulates spectral-spatial features to adaptively reconstruct diverse planetary topographies. To facilitate rigorous evaluation, we introduce Ceres-50, a benchmark dataset encapsulating diverse geological features under realistic degradation simulations. Extensive experiments demonstrate that HAC-MoE achieves state-of-the-art performance in reconstruction quality and kernel estimation accuracy, offering a solution for scientific observation in data-sparse extraterrestrial environments.

cs.CV↗