发表机构
Arizona State University; Virginia Tech（亚利桑那州立大学; 弗吉尼亚理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究以气动软机器人为对象，探究囊拓扑、刚度及传感数量对PRC弯曲角度估计性能的影响，得出三条设计准则，为软机器人PRC的协同设计提供依据。
AI 中文摘要
物理储备池计算（PRC）指将物理动力系统用作计算资源，用于状态估计、控制等任务，但目前缺乏针对更高效物理储备池设计规则的正式研究。本研究采用带有五囊传感柱的气动软机械臂，探究囊间互联拓扑、机器人刚度及传感数量对弯曲角度估计性能的影响。在覆盖波形、传感柱基线压力、驱动范围的36组匹配试验中，所有设计均采用0.2秒压力历史和固定脊估计器，在同步弯曲角度估计基准下评估。实验分析得出三条设计准则：第一，独立密封囊比共享流形保留更丰富的可观测状态；第二，增加传感柱基线压力会使囊响应更冗余，且在耦合拓扑中估计误差增幅最显著；第三，密封拓扑中，2个位置合理的传感器即可实现大部分可获增益，3个传感器可捕获几乎全部增益，额外传感器几乎无附加价值。综上，研究表明需协同设计拓扑、刚度及传感数量，以实现软机器人状态的准确PRC；仅靠更强激励无法弥补设计不当导致的多样性缺失。
英文摘要
Physical reservoir computing (PRC) refers to the use of a physical dynamical system as a computational resource for tasks such as state estimation and control, but there has been a lack of formal study of design rules towards more effective design of such physical reservoirs. Using a pneumatic soft arm with a five-pouch sensing column, this work studies how the pouch interconnection topology, robot stiffness, and the number of instrumented sensors affect bending-angle estimation performance. Across 36 matched trials spanning waveform, baseline pressure of the sensing column, and actuation range, all designs are evaluated under the same-time bending-angle estimation benchmark using 0.2 s of pressure history and a fixed ridge estimator. Our analysis of the experimental results leads to three design guidelines. First, independently sealed pouches preserve a much richer observable state than a shared manifold. Second, increasing the baseline pressure of the sensing column makes the pouch responses more redundant and increases estimation error most strongly in the coupled topology. Third, in the sealed topology, two strategically placed sensors already recover most of the attainable benefit, three capture essentially all of it, and additional sensors provide little or no additional value. In summary, the results suggest that topology, stiffness, and number of instrumented sensors should be co-designed for accurate PRC of soft robot states; stronger excitation alone cannot recover the diversity that poor design choices have already removed.
Comments6 pages; 5 figures, accepted for 2026 Modeling, Estimation, and Control Conference (MECC)
