论文标题
蒙特卡罗取样的硬件加速度，以进行节能强大的机器人操纵
Hardware Acceleration of Monte-Carlo Sampling for Energy Efficient Robust Robot Manipulation
论文作者
论文摘要
由于其性能鲁棒性，基于蒙特卡洛采样的算法已广泛适应机器人技术和其他工程领域。但是，这些基于抽样的方法具有很高的计算要求，这使得它们不适合具有严格限制的实时应用。在本文中，我们使用此方法研究了6个自由度（6DOF）姿势估计机器人操纵，该方法使用渲染与顺序的蒙特卡罗取样相结合。尽管潜在的非常准确，但算法的显着计算复杂性使其对移动机器人的吸引力降低，而在运行时和能源消耗受到严格限制的情况下。为了应对这些挑战，我们在FPGA上开发了一种新颖的硬件实现，具有较低的计算复杂性和记忆使用情况，同时实现了高平行性和模块化。我们的结果表明，对于低功率和高端GPU实施，能源效率的提高了12x-21x。此外，我们在不损害准确性的情况下实现实时性能。
Algorithms based on Monte-Carlo sampling have been widely adapted in robotics and other areas of engineering due to their performance robustness. However, these sampling-based approaches have high computational requirements, making them unsuitable for real-time applications with tight energy constraints. In this paper, we investigate 6 degree-of-freedom (6DoF) pose estimation for robot manipulation using this method, which uses rendering combined with sequential Monte-Carlo sampling. While potentially very accurate, the significant computational complexity of the algorithm makes it less attractive for mobile robots, where runtime and energy consumption are tightly constrained. To address these challenges, we develop a novel hardware implementation of Monte-Carlo sampling on an FPGA with lower computational complexity and memory usage, while achieving high parallelism and modularization. Our results show 12X-21X improvements in energy efficiency over low-power and high-end GPU implementations, respectively. Moreover, we achieve real time performance without compromising accuracy.
