B*高效求解机械臂基座最优位置,兼顾精度与速度。
B*: Efficient and Optimal Base Placement for Fixed-Base Manipulators
- 分层优化框架,外层约束渐进收紧,内层线性化求解
- 比采样法优度高5个数量级,成功率100%且计算开销更低
- 适合需要精确轨迹规划的工业机器人部署场景
B*是一种新型优化框架,解决固定基座机械臂中的基座最优放置问题。现有方法依赖预采样的运动学数据库进行搜索,但采样分辨率在解的最优性与计算效率间存在固有权衡。为克服此局限,B*不依赖数据库,统一多目标优化。该框架采用两层分层结构:外层通过逐步收紧终端约束来管理基座移动性,实现可行初始化并拓展解空间;内层通过顺序局部线性化处理各子问题中的非凸性,将原问题转化为可解的序列线性规划(SLP)。在多个机器人平台上的测试表明,B*相较采样方法在解的最优性上提升五个数量级,同时保持100%成功率且计算开销更低。直接在配置空间运行,支持与可定制优化目标同步的路径规划。B*作为关键初始化工具,弥合了理论运动规划与实际部署间的鸿沟,确保可行轨迹的存在性。
原文摘要 · Abstract (English)
B* is a novel optimization framework that addresses a critical challenge in fixed-base manipulator robotics: optimal base placement. Current methods rely on pre-computed kinematics databases generated through sampling to search for solutions. However, they face an inherent trade-off between solution optimality and computational efficiency when determining sampling resolution. To address these limitations, B* unifies multiple objectives without database dependence. The framework employs a two-layer hierarchical approach. The outer layer systematically manages terminal constraints through progressive tightening, particularly for base mobility, enabling feasible initialization and broad solution exploration. The inner layer addresses non-convexities in each outer-layer subproblem through sequential local linearization, converting the original problem into tractable sequential linear programming (SLP). Testing across multiple robot platforms demonstrates B*'s effectiveness. The framework achieves solution optimality five orders of magnitude better than sampling-based approaches while maintaining perfect success rates and reduced computational overhead. Operating directly in configuration space, B* enables simultaneous path planning with customizable optimization criteria. B* serves as a crucial initialization tool that bridges the gap between theoretical motion planning and practical deployment, where feasible trajectory existence is fundamental.
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