Real-Time, Energy-Efficient, Sampling-Based Optimal Control via FPGA Acceleration

2026-01-23

AI Summary

Autonomous mobile robots struggle to simultaneously achieve the low latency and low power consumption required for real-time control on embedded platforms. This work proposes an FPGA-oriented optimized architecture for Model Predictive Path Integral (MPPI) control, achieving the first deep hardware specialization of the MPPI algorithm. By leveraging deep pipelining, cross-stage parallelization, and customized data paths, the design eliminates synchronization bottlenecks and fully exploits fine-grained parallelism. Compared to optimized implementations on embedded CPUs and GPUs, the proposed architecture delivers 3.1–7.5× average speedup while reducing energy consumption by 2.5–5.4×, all without compromising control accuracy—significantly enhancing both energy efficiency and real-time performance.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Machine Learning: Hardware-aware ML
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

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.

Problem

• autonomous mobile robots
• real-time control
• energy efficiency
• sampling-based control
• embedded systems

Innovation

• FPGA acceleration
• Model Predictive Path Integral Control
• energy-efficient robotics
• real-time optimal control
• hardware-software co-design