对比不同QP求解器与硬件组合，提升四足机器人动态行走效率。
Benchmarking Different QP Formulations and Solvers for Dynamic Quadrupedal Walking
- 比较稠密与稀疏QP公式在不同硬件上的表现
- 提出每瓦特求解频率（SFPW）评估跨硬件效率
- 为四足机器人控制提供硬件-求解器选型建议
二次规划（QP）广泛应用于步行机器人的控制，尤其在模型预测控制（MPC）和全身控制（WBC）中。控制器设计需构建QP问题并选择合适求解器，两者均需大量时间和专业知识。尽管已有QP求解器的计算性能基准，但缺乏对计算硬件（HW）、QP公式与求解器性能组合的系统比较。本文在基于MPC的四足机器人动态行走中，对比稠密与稀疏QP公式及多种求解方法在不同硬件架构上的计算效率。引入每瓦特求解频率（SFPW）作为跨硬件性能度量标准，并对用于轨迹稳定的WBC QP求解器进行基准测试。结果为不同硬件架构下QP公式与求解器的选择提供指导，并指明未来应重点投入的技术方向。
原文摘要 · Abstract (English)
Quadratic Programs (QPs) are widely used in the control of walking robots, especially in Model Predictive Control (MPC) and Whole-Body Control (WBC). In both cases, the controller design requires the formulation of a QP and the selection of a suitable QP solver, both requiring considerable time and expertise. While computational performance benchmarks exist for QP solvers, studies comparing optimal combinations of computational hardware (HW), QP formulation, and solver performance are lacking. In this work, we compare dense and sparse QP formulations, and multiple solving methods on different HW architectures, focusing on their computational efficiency in dynamic walking of four legged robots using MPC. We introduce the Solve Frequency per Watt (SFPW) as a performance measure to enable a cross hardware comparison of the efficiency of QP solvers. We also benchmark different QP solvers for WBC that we use for trajectory stabilization in quadrupedal walking. As a result, this paper provides recommendations for the selection of QP formulations and solvers for different HW architectures in walking robots and indicates which problems should be devoted the greater technical effort in this domain in future.
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