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.

翻译：实时精确控制机器人系统是一个具有挑战性的问题。为此，机器人学界已采用多种算法，例如模型预测控制（MPC）与模型预测路径积分（MPPI）控制。前者在诸如无人机等非线性系统上难以实现，而后者则需承担沉重的计算负荷。GPU已被成功用于加速MPPI实现；然而，其功耗对于自主或无人目标（尤其是电池供电系统）往往过高。另一方面，已有研究提出通常基于FPGA实现的自定义设计，用以加速机器人算法，同时其能耗显著低于基于GPU（或CPU）的实现。然而，迄今为止尚未有MPPI专用加速器被提出。本工作中，我们提出了一种用于MPPI控制的硬件加速器并对其执行进行了仿真。结果表明，与基于GPU的MPPI实现相比，该MPPI专用加速器能够实现更精确的轨迹跟踪。