通过分阶段优化，实现移动机械臂高精度路径跟踪
Two-Stage Path Following for Mobile Manipulators via Dimensionality-Reduced Graph Search and Numerical Optimization
- 将8自由度问题分解为2自由度基座优化，降低计算复杂度
- 仿真中实现亚毫米级运动精度，物理实验验证实际可用性
- 适合对轨迹平滑性和可达性要求高的移动机器人应用
移动机械臂的高效路径跟踪常受限于高维配置空间和运动学约束。本文提出一种稳健的两阶段配置规划框架，将8自由度规划问题在偏航固定假设下解耦为可处理的2自由度基座优化。第一阶段采用IRM将任务空间路径离散化为多层图，基于Dijkstra的动态规划方法提取初始可行路径，确保离散图内的计算效率与全局最优性。第二阶段通过将可行基座区域转换为凸包，利用L-BFGS算法进行连续优化，以最大化轨迹平滑性并严格满足可达性约束。仿真结果表明该方法具有理论精度，实现亚毫米级运动精度；在全向移动机械臂上的物理实验进一步验证了该框架的鲁棒性与实际适用性。
原文摘要 · Abstract (English)
Efficient path following for mobile manipulators is often hindered by high-dimensional configuration spaces and kinematic constraints. This paper presents a robust two-stage configuration planning framework that decouples the 8-DoF planning problem into a tractable 2-DoF base optimization under a yaw-fixed base planning assumption. In the first stage, the proposed approach utilizes IRM to discretize the task-space path into a multi-layer graph, where an initial feasible path is extracted via a Dijkstra-based dynamic programming approach to ensure computational efficiency and global optimality within the discretized graph. In the second stage, to overcome discrete search quantization, feasible base regions are transformed into convex hulls, enabling subsequent continuous refinement via the L-BFGS algorithm to maximize trajectory smoothness while strictly enforcing reachability constraints. Simulation results demonstrate the theoretical precision of the proposed method by achieving sub-millimeter kinematic accuracy in simulation, and physical experiments on an omnidirectional mobile manipulator further validate the framework's robustness and practical applicability.
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