Autonomous mobile robots (AMRs), used for search-and-rescue and remote exploration, require fast and robust planning and control schemes. Sampling-based approaches for Model Predictive Control, especially approaches based on the Model Predictive Path Integral Control (MPPI) algorithm, have recently proven both to be highly effective for such applications and to map naturally to GPUs for hardware acceleration. However, both GPU and CPU implementations of such algorithms can struggle to meet tight energy and latency budgets on battery-constrained AMR platforms that leverage embedded compute. To address this issue, we present an FPGA-optimized MPPI design that exposes fine-grained parallelism and eliminates synchronization bottlenecks via deep pipelining and parallelism across algorithmic stages. This results in an average 3.1x to 7.5x speedup over optimized implementations on an embedded GPU and CPU, respectively, while simultaneously achieving a 2.5x to 5.4x reduction in energy usage. These results demonstrate that FPGA architectures are a promising direction for energy-efficient and high-performance edge robotics.

翻译：用于搜救和远程探索的自主移动机器人需要快速鲁棒的规划与控制方案。基于采样的模型预测控制方法，特别是基于模型预测路径积分控制算法的方案，近期已被证明不仅在此类应用中极为有效，而且能自然地映射到GPU以实现硬件加速。然而，在利用嵌入式计算的电池受限自主移动机器人平台上，此类算法的GPU和CPU实现往往难以满足严格的能耗与延迟约束。为解决这一问题，我们提出了一种面向FPGA优化的MPPI设计，该设计通过跨算法阶段的深度流水线与并行化，实现了细粒度并行并消除了同步瓶颈。相较于嵌入式GPU和CPU上的优化实现，该设计分别平均实现了3.1倍至7.5倍的加速，同时能耗降低了2.5倍至5.4倍。这些结果表明，FPGA架构是实现高能效高性能边缘机器人技术的一个有前景的方向。