适用于机会约束六自由度交会的SE(3)上的内在随机逐次凸化方法
Intrinsic Stochastic Successive Convexification on SE(3) for Chance Constrained 6-DOF Rendezvous
浏览论文内容
中文总结 AI 辅助
该研究提出基于SE(3)的内在随机逐次凸化方法，用于航天器六自由度交会轨迹优化，可捕捉运动不确定性耦合，联合优化轨迹等提升概率约束满足度。
中文摘要 AI 辅助
本研究提出一种基于特殊欧几里得群SE(3)构建的内在随机逐次凸化方法，用于六自由度航天器交会轨迹优化。该方法将最初针对欧几里得状态空间开发的随机逐次凸化方法扩展到SE(3)的非线性流形上，从而实现刚体位姿轨迹的一致协方差制导与机会约束优化。传统轨迹优化方法通常单独处理位置与姿态，或仅在生成确定性参考轨迹后才考虑随机分散性，而所提出的基于SE(3)的公式化方法能够捕捉平移与旋转运动不确定性之间的内在耦合。这种耦合对于依赖全相对位姿的安全约束交会问题尤为重要，包括避障、对接走廊、相机视场以及概率力和力矩边界。数值仿真表明，与采用反馈线性化控制器跟踪确定性参考轨迹相比，联合优化标称轨迹、协方差和反馈律可塑造闭环分散性，并提高概率约束满足度。
英文摘要
This work presents an intrinsic stochastic successive convexification method formulated on the Special Euclidean group SE(3) for six degrees of freedom spacecraft rendezvous trajectory optimization. The proposed approach extends stochastic successive convexification, originally developed for Euclidean state spaces, to the nonlinear manifold of SE(3), thereby enabling a consistent covariance steering and chance constrained optimization of rigid body pose trajectories. While conventional trajectory optimization methods often treat position and attitude separately, or account for stochastic dispersion only after a deterministic reference trajectory has been generated, the proposed SE(3)-based formulation captures the intrinsic coupling between translational and rotational motion uncertainty. This coupling is especially important for rendezvous problems with safety constraints that depend on the full relative pose, including collision avoidance, docking corridor, camera field of view, and probabilistic force and torque bounds. Numerical simulations show that jointly optimizing the nominal trajectory, covariance, and feedback law shapes the closed loop dispersion and improves probabilistic constraint satisfaction relative to tracking a deterministic reference with a feedback linearization controller.
