Aerial GRIPPER：一种基于梯度的实时逆博弈预测器与规划器
Aerial GRIPPER: A Gradient-based Real-time Inverse-game Predictor and Planner
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中文总结 AI 辅助
针对非合作目标捕获难题，提出集成GRIPPER框架的空中抓取系统，通过在线推断成本参数并迭代优化开环纳什均衡策略，结合抗扰动控制器，实现高频实时规划与精确抓取，仿真和实验验证了其高效性和鲁棒性。
中文摘要 AI 辅助
准确捕获非合作目标至关重要。为应对这一棘手挑战，提出了一种集成基于梯度的实时逆博弈预测与规划框架（GRIPPER）的空中抓取系统。该交互被建模为不完全信息下的一般和追逃博弈。具体而言，在线推断目标的潜在成本参数，并在滚动时域循环内迭代细化开环纳什均衡（OLNE）策略。为确保高频执行，开发了一种计算友好的基于梯度的逆博弈求解器。无需显式计算Hessian逆，优化解基于隐式微分和快速Hessian-向量乘积以超过50 Hz的频率更新。同时，开发了一种抗扰动控制器，以克服不确定载荷和抓取器作动的扰动，实现对规划轨迹的精确跟踪和对目标的准确抓取。仿真和真实实验展示了GRIPPER优越的计算效率和任务性能。完成了对非合作目标的捕获与运送任务，突显了该框架在高对抗场景下的鲁棒性、适应性和实时性能。
英文摘要
Accurate capture of non-cooperative targets is critical. In an attempt to tackle this intractable challenge, an aerial gripper system integrated with a Gradient-based Real-time Inverse-game Predictor and PlannER (GRIPPER) framework is proposed. The interaction is formulated as a general-sum pursuit-evasion game under incomplete information. Specifically, underlying cost parameters of the target are inferred online, and the open-loop Nash equilibrium (OLNE) strategy is iteratively refined within a receding-horizon loop. To ensure high-frequency execution, a computationally friendly gradient-based inverse-game solver is developed. Without explicit computation of the Hessian inverse, the optimized solution is updated (> 50 Hz) based on implicit differentiation and fast Hessian-vector products. Meanwhile, an anti-disturbance controller is developed to overcome disturbances of uncertain payload and gripper actuation, enabling precise tracking of the planned trajectory and accurate grasping of the target. Simulations and real-world experiments illustrate the superior computational efficiency and task performance of GRIPPER. The task of capturing and delivering a non-cooperative target is accomplished, highlighting the robustness, adaptability, and real-time performance of the framework in highly adversarial scenarios.
发表机构
- Beihang University（北京航空航天大学）
- Nanyang Technological University（南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。
