自由漂浮空间机械臂的高效贝塞尔速度优化
Efficient Bezier Velocity Optimization for Free-Floating Space Manipulators
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中文总结 AI 辅助
提出FAVOR规划器，利用贝塞尔曲线和递归灵敏度优化自由漂浮机械臂速度，实现高成功率与低计算时间，显著优于基线方法。
中文摘要 AI 辅助
我们提出了FAVOR（自由漂浮机械臂速度优化与递归灵敏度），一种用于非驱动航天器上无碰撞到达、跟踪和预定时间预抓取拦截的规划器。它通过线性速度、加速度和连续性约束优化贝塞尔关节速度曲线。决策维度与滚动分辨率无关。解析递归灵敏度通过耦合的基座-机械臂运动提供任务和间隙梯度。并行评估、缓存和增量碰撞发现减少了计算量。使用七自由度机械臂和五个模拟航天器模型，FAVOR实现了99.8%的点对点成功率，平均计算时间为1.779秒，而基于IK初始化的位置样条基线为62.9%和50.270秒。平均值包括失败和超时。FAVOR完成了36个跟踪案例中的30个，而单步QP完成了15个，并完成了所有36个拦截实例，而样条基线完成了22个。受控消融实验表明，有限差分使平均规划时间增加了4.7倍。
英文摘要
We present FAVOR (Free-floating Arm Velocity Optimization with Recursive Sensitivities), a planner for collision-free reaching, tracking, and prescribed-time pre-grasp interception on an unactuated spacecraft. It optimizes Bezier joint-velocity curves with linear velocity, acceleration, and continuity constraints. Decision dimension is independent of rollout resolution. Analytical recursive sensitivities provide task and clearance gradients through the coupled base-arm motion. Parallel evaluation, caching, and incremental collision discovery reduce computation. With a seven-DoF arm and five simulated spacecraft models, FAVOR achieves 99.8% point-to-point success with 1.779 s mean computation, versus 62.9% and 50.270 s for an IK-initialized position-spline baseline. Means include failures and timeouts. FAVOR completes 30 of 36 tracking cases, versus 15 for single-step QP, and all 36 interception instances, versus 22 for the spline baseline. A controlled ablation shows that finite differences increase mean planning time 4.7-fold.
