外观
论文arXiv 2609.168801 个月前
人工智能赋能的空间机器人操作：技术、挑战与前景
原文链接：Artificial Intelligence-Enabled Space Robot Operations: Technologies, Challenges and Prospects（arXiv 2609.16880 · 发布日期：2026-09-15）
Artificial Intelligence-Enabled Space Robot Operations: Technologies, Challenges and Prospects
本文综述AI赋能空间机器人操作，构建三层技术框架，并围绕场景、约束、能力形成与部署演化展开分析。
核心要点
- 面向长时程、富接触、多阶段且人工干预有限的空间机器人操作，自主性与适应性需求提升。
- 文章从能力建设视角建立三层框架，涵盖能力基础、能力形成，以及能力部署与演化。
- 综述仿真环境、数据集与基准，以及理解、感知、决策规划、动作执行等关键技术。
- 讨论任务数据稀缺、空间动力学与感知条件、星载资源和安全要求等约束，并提出未来方向。
英文摘要 (Abstract)
Space robots are increasingly expected to perform long-duration, contact-rich, and multi-stage operations with limited human intervention. Recent advances in artificial intelligence (AI), robot learning, and embodied foundation models provide new opportunities to improve the autonomy and adaptability of such systems, but their transfer to space is constrained by scarce mission data, space-specific dynamics and sensing conditions, limited onboard resources, and stringent safety requirements. This article reviews artificial intelligence-enabled space robot operations (AI-SRO) from a capability-building perspective. We first summarize representative operational scenarios, autonomy trends, and space-specific constraints. We then establish a three-layer technical framework comprising capability foundations, capability formation, and capability deployment/evolution. Within this framework, we review simulation environments, datasets and benchmarks; task and environment understanding, state perception, decision-making and planning, and action execution; and onboard deployment, ground-to-space adaptation, continual learning, and capability transfer. Finally, we propose key research directions toward trustworthy simulation and data, open-world multimodal cognition, long-horizon safe decision-making, physically constrained policy learning, and space computing infrastructures.
作者：Zeyuan Huang, Gang Chen, Zixuan Hao, Guoqin Tang, Junyi Zong, Guoyou Ban, Jiale Wang, Haoyang Lv, Chaoqian Ren, Sitong Liu
来源
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