通过自适应缓存优化控制计算，让微型无人机实时抗风飞行
Robust and Efficient Embedded Convex Optimization through First-Order Adaptive Caching
- 预先缓存矩阵运算及其对参数变化的敏感度，支持在线动态调整
- 相比固定参数减少63.4%迭代次数，计算复杂度从O(n³)降至O(n²)
- 适用于资源受限的嵌入式机器人系统，尤其适合微小型无人机控制
基于一阶方法（如ADMM）与离线预计算缓存的模型预测控制（MPC）近期实现了在微控制器上的实时运行。然而，现有方法依赖固定超参数，限制了适应性和性能。本文提出一阶自适应缓存机制，不仅缓存关键矩阵运算，还预先计算其对超参数变化的敏感度，实现无需完整重算即可在线更新超参数。我们在多个四旋翼动态任务中验证该方法，相比最优固定超参数，最多减少63.4%的ADMM迭代次数，性能接近全量缓存重算的70%，同时将计算复杂度从O(n³)降低至O(n²)。该性能使27克微型四旋翼在风扰下完成8字轨迹飞行。代码已开源，供机器人社区使用。
原文摘要 · Abstract (English)
Recent advances in Model Predictive Control (MPC) leveraging a combination of first-order methods, such as the Alternating Direction Method of Multipliers (ADMM), and offline precomputation and caching of select operations, have excitingly enabled real-time MPC on microcontrollers. Unfortunately, these approaches require the use of fixed hyperparameters, limiting their adaptability and overall performance. In this work, we introduce First-Order Adaptive Caching, which precomputes not only select matrix operations but also their sensitivities to hyperparameter variations, enabling online hyperparameter updates without full recomputation of the cache. We demonstrate the effectiveness of our approach on a number of dynamic quadrotor tasks, achieving up to a 63.4% reduction in ADMM iterations over the use of optimized fixed hyperparameters and approaching 70% of the performance of a full cache recomputation, while reducing the computational cost from O(n^3) to O(n^2) complexity. This performance enables us to perform figure-eight trajectories on a 27g tiny quadrotor under wind disturbances. We release our implementation open-source for the benefit of the wider robotics community.
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