# 智能主动粒子感知和致动的非平衡能量学

> 原文链接：https://robotics.hellobike.com/papers/2026-10-19-2603-15602
> 频道：papers | 更新日期：2026-10-19 | 标签：arxiv, cs.RO
> 核心摘要：该文针对智能主动体能量分配问题，提出自转向粒子最小模型，并在不同任务几何中揭示热力学权衡。

# 智能主动粒子感知和致动的非平衡能量学

::: v-pre
> 原文链接：[Nonequilibrium energetics of sensing and actuation by a smart active particle](https://arxiv.org/abs/2603.15602)（arXiv 2603.15602 · 发布日期：2026-10-19）

**Nonequilibrium energetics of sensing and actuation by a smart active particle**

该文针对智能主动体能量分配问题，提出自转向粒子最小模型，并在不同任务几何中揭示热力学权衡。

## 核心要点

- 动机：智能主动体需在多种功能间分配有限能量，相关热力学权衡尚不清楚。
- 方法：构建带内部极性线索传感器并耦合外部环境场的自转向粒子最小模型。
- 分解稳态熵产生率为运动、致动与感知成本，形成具身导航的能量记账结构。
- 结果：Pareto前沿连接能耗、定位精度和路径跟随性能，约束在不同任务几何中保持。

**验证范围与局限**：验证范围为该最小自转向粒子模型及不同任务几何。

## 英文摘要 (Abstract)

Abstract Smart active agents must allocate finite energetic resources across distinct functions, yet the underlying thermodynamic trade-offs remain poorly understood. Here, we introduce a minimal model of a self-steering particle with an internal polarity-cue sensor coupled to an external environmental field, decomposing its steady-state entropy production rate into locomotion, actuation, and sensing costs. This separation exposes an energetic bookkeeping structure underlying even the simplest form of embodied navigation. The emergence of Pareto fronts linking energetic expenditure to localisation precision and path-following performance shows that feedback-controlled active motion is constrained by quantitative thermodynamic bounds that persist across distinct task geometries.

作者：Luca Cocconi, Benoît Mahault, Lorenzo Piro
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## 原始参考来源 (Sources)

- [arXiv 2603.15602：Nonequilibrium energetics of sensing and actuation by a smart active particle](https://arxiv.org/abs/2603.15602)