动态四足行走中不同QP公式与求解器的基准测试
Benchmarking Different QP Formulations and Solvers for Dynamic Quadrupedal Walking
AI总结：
本研究比较了不同硬件架构下稠密与稀疏QP公式及多种求解器在四足机器人MPC与WBC中的计算效率，引入SFPW指标，为步行机器人QP求解方案选择提供指导建议。
摘要：
二次规划（QP）广泛应用于步行机器人的控制中，尤其是在模型预测控制（MPC）和全身控制（WBC）中。在这两种情况下，控制器的设计都需要对QP进行公式化并选择合适的QP求解器，这两者都需要耗费大量的时间和专业知识。虽然存在针对QP求解器的计算性能基准测试，但缺乏比较计算硬件（HW）、QP公式和求解器性能最优组合的研究。在本研究中，我们比较了稠密和稀疏QP公式，以及在不同HW架构上的多种求解方法，重点关注使用MPC的四足机器人动态行走中的计算效率。我们引入了每瓦求解频率（SFPW）作为性能指标，以实现QP求解器效率的跨硬件比较。我们还对用于四足行走轨迹稳定的WBC的不同QP求解器进行了基准测试。因此，本文为步行机器人中不同HW架构的QP公式和求解器选择提供了建议，并指出了未来该领域应投入更多技术努力的问题。
英文摘要：
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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