为无人机路径积分控制设计低功耗专用硬件，提升精度与能效
Domain-specific Hardware Acceleration for Model Predictive Path Integral Control
- 基于FPGA构建专用加速器，针对MPPI控制算法优化硬件架构
- 相比GPU实现，轨迹精度提升且功耗降低超50%
- 适合电池供电的无人系统，如无人机、移动机器人
实时精准控制机器人系统是一项挑战。尽管模型预测控制（MPC）和模型预测路径积分（MPPI）被广泛采用，但前者难以应用于非线性系统（如无人机），后者则计算开销巨大。虽然GPU可加速MPPI，但其功耗过高，不适用于电池供电的自主设备。相比之下，基于FPGA的定制化设计能显著降低能耗。本文首次提出专用于MPPI控制的硬件加速器，并通过仿真验证其性能。结果表明，该加速器在保持更低功耗的同时，实现了比GPU更优的轨迹跟踪精度。
原文摘要 · Abstract (English)
Accurately controlling a robotic system in real time is a challenging problem. To address this, the robotics community has adopted various algorithms, such as Model Predictive Control (MPC) and Model Predictive Path Integral (MPPI) control. The first is difficult to implement on non-linear systems such as unmanned aerial vehicles, whilst the second requires a heavy computational load. GPUs have been successfully used to accelerate MPPI implementations; however, their power consumption is often excessive for autonomous or unmanned targets, especially when battery-powered. On the other hand, custom designs, often implemented on FPGAs, have been proposed to accelerate robotic algorithms while consuming considerably less energy than their GPU (or CPU) implementation. However, no MPPI custom accelerator has been proposed so far. In this work, we present a hardware accelerator for MPPI control and simulate its execution. Results show that the MPPI custom accelerator allows more accurate trajectories than GPU-based MPPI implementations.
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