Domain-specific Hardware Acceleration for Model Predictive Path Integral Control

TL;DR

This paper introduces a custom hardware accelerator for Model Predictive Path Integral (MPPI) control, significantly improving trajectory accuracy and energy efficiency over GPU-based implementations for real-time robotic applications.

Contribution

The paper presents the first hardware accelerator specifically designed for MPPI control, enabling more accurate and energy-efficient robotic control.

Findings

The custom MPPI accelerator achieves higher trajectory accuracy.

It consumes less power compared to GPU implementations.

Simulation results demonstrate improved control performance.

Abstract

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.