AI 中文总结
该研究针对机器人视觉触觉感知中主机处理的延迟与功耗问题，实现含谱泊松求解器的近传感器计算框架，可快速、准确且节能地重建接触几何，显著缩短机器人保护反射回路的响应时间。
AI 中文摘要
视觉触觉传感器可从测量的表面梯度重建密集接触几何结构，但基于主机的处理会增加功耗，并引入数据传输延迟和可变调度延迟，限制了机器人系统的感知和响应速度。为解决这些局限，我们实现了一种近传感器计算框架，其中包含作为全流硬件流水线的谱泊松求解器。计算核心逻辑的估计功耗为347 mW，且无需依赖数据的分支或迭代收敛即可实现高吞吐量，从而提供确定性延迟。该流水线工作频率为166 MHz，在接收到第一个输入像素后，经过35107个周期生成每帧128×128的第一个深度值，对应固定延迟为0.211 ms。在15种接触几何结构上，重建深度与双精度参考值的差异为峰值接触深度的0.17%。基于这些重建结果的片上决策可在28.3±4.9 ms内闭合机器人保护反射回路，而使用相同执行器的等效基于主机的回路则需169.9±27.8 ms。这些结果表明，近传感器重建可在适合机器人快速接触响应的时间尺度上提供准确、节能且确定性的触觉几何结构。
英文摘要
Visuotactile sensors reconstruct dense contact geometry from measured surface gradients, but host-based processing increases power consumption and introduces data-transfer delays and variable scheduling latency, limiting the sensing and response speed of robotic systems. To address these limitations, we implement a near-sensor computing framework that includes a spectral Poisson solver as a fully streaming hardware pipeline. The computational core logic has an estimated power consumption of 347 mW and achieves high throughput without data-dependent branching or iterative convergence, thereby providing deterministic latency. Operating at 166 MHz, the pipeline produces the first depth value of each 128x128 frame 35,107 cycles after receiving the first input pixel, corresponding to a fixed latency of 0.211 ms. Across 15 contact geometries, the reconstructed depths differ from a double-precision reference by 0.17 % of the peak contact depth. On-chip decisions based on these reconstructions close a robot protective reflex loop in 28.3 +/- 4.9 ms, compared with 169.9 +/- 27.8 ms for an equivalent host-based loop using the same actuator. These results demonstrate that near-sensor reconstruction can provide accurate, energy-efficient, and deterministic tactile geometry on timescales suitable for rapid robotic contact responses.
Comments14 pages, 4 figures
