軌道プランナー：衛星エージェントの軌道上障害物回避のための潜在世界モデル
Orbit-Planner: Towards Latent World Models for On-Orbit Obstacle Avoidance of Satellite Agents
軌道上の衛星ナビゲーション向けに、潜在空間で将来状態を予測する2段階の世界モデルを提案し、物理状態を復元するプローブを導入。シミュレーションで91.7%の成功率を達成。
著者: Zhijian Li, Chao Ren, Peijin Wang, Xian Sun
分類: cs.RO, cs.AI
原文アブストラクト
Satellite agents for on-orbit navigation tasks need to predict collision risks using limited onboard observations. However, conventional planners often rely on predefined maps and fixed environmental assumptions, limiting their adaptability in dynamic on-orbit scenarios. In this paper, we propose Orbit-Planner, a two-stage latent world model for on-orbit obstacle avoidance. Orbit-Planner learns action-conditioned spacecraft dynamics to perform future-state rollouts in latent space, and introduces a Physics Probe to decode physical state changes from imagined latent trajectories. Experiments demonstrate that Orbit-Planner can perform long-horizon latent rollouts and recover physical states from imagined trajectories. In closed-loop obstacle-avoidance navigation in Isaac Sim, it attains a success rate of 91.7%. Code is available at https://github.com/ZhijianLi2003/Orbit_Planner.