提出一种鲁棒时间最优运动规划方法，提升不确定环境下的安全与效率。
Robustified Time-optimal Point-to-point Motion Planning and Control under Uncertainty
- 分两阶段优化：固定网格阶段优化轨迹与反馈增益，变量网格阶段最小化总时长。
- 在不确定条件下实现轨迹不确定性最小化与总运动时间最短。
- 适合需要实时重规划的机器人系统，尤其适用于高动态场景。
本文提出一种新的时间最优点对点运动规划与控制方法，以应对不确定性。该方法构建了一个鲁棒化的两阶段最优控制问题（OCP）：第一阶段采用固定时间网格，优化名义轨迹、反馈增益及对应状态协方差，从而增强两阶段约束的鲁棒性；第二阶段采用可变时间网格，以最小化总运动时间。第一阶段降低不确定性，第二阶段缩短总时长，共同保障整体运动的时间最优性与安全性。采用及时重规划策略应对约束变化并保持可行性，同时设计专用迭代算法实现高效的实时OCP求解。
原文摘要 · Abstract (English)
This paper proposes a novel approach to formulate time-optimal point-to-point motion planning and control under uncertainty. The approach defines a robustified two-stage Optimal Control Problem (OCP), in which stage 1, with a fixed time grid, is seamlessly stitched with stage 2, which features a variable time grid. Stage 1 optimizes not only the nominal trajectory, but also feedback gains and corresponding state covariances, which robustify constraints in both stages. The outcome is a minimized uncertainty in stage 1 and a minimized total motion time for stage 2, both contributing to the time optimality and safety of the total motion. A timely replanning strategy is employed to handle changes in constraints and maintain feasibility, while a tailored iterative algorithm is proposed for efficient, real-time OCP execution.
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