发表机构
University of Colorado Boulder（科罗拉多大学博尔德分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出WOLF算法，将滚动时域模型预测控制与基于序贯凸化的开环微分博弈求解器结合，通过动态鲁棒性管协同优化轨迹与约束收紧，解决长时间范围受扰动的非凸博弈运动规划问题，并在两个在轨对抗博弈中验证可行性。
AI 中文摘要
本工作针对长时间范围内受扰动影响的非凸、博弈论运动规划问题提出了一种解决方案。该问题被建模为部分解耦的广义纳什均衡问题，其中每个智能体的动力学仅依赖于其自身状态和控制，从而允许对竞争性多智能体运动规划采用快速求解方法。开发了一种名为WOLF的算法，该算法将基于序贯凸化的开环微分博弈求解器应用于滚动时域模型预测控制。与离线固定不确定性描述的鲁棒公式不同，这里的鲁棒性管本身是一个动态状态，与轨迹协同优化，其厚度直接决定共享耦合约束的收紧程度。推导了一个充分条件，在该条件下，满足针对所有智能体误差界收紧约束的名义轨迹对于每个可容许的扰动实现仍然可行。该方法在两个具有耦合平移-旋转动力学的对抗性在轨博弈中得到了验证：一个是在主动探测概率约束下的隐蔽共轨干扰博弈，另一个是太阳遮挡博弈，其中对手通过降低太阳能功率来使逃避者失效。
英文摘要
This work presents a solution to nonconvex, game-theoretic motion planning problems subject to disturbances over long time horizons. The problem is posed as a partially-decoupled generalized Nash equilibrium problem, in which each agent's dynamics depend only on its own state and control, admitting fast solution methods for competitive multi-agent motion planning. An algorithm, WOLF, is developed that applies receding-horizon model predictive control to an open-loop differential games solver based on sequential convexification. In contrast to robust formulations that fix the uncertainty description offline, the robustness tube here is itself a dynamic state, co-optimized with the trajectory, and its thickness directly sets the tightening of the shared coupling constraints. A sufficient condition is derived under which a nominal trajectory satisfying constraints tightened against all agents' error bounds remains feasible for every admissible disturbance realization. The method is demonstrated on two adversarial on-orbit games with coupled translational-attitude dynamics: a stealthy co-orbital jamming game under an active detection-probability bound, and a sun-blocking game in which an adversary disables an evader by decreasing solar power
