Energy-aware algorithms for multi-robot systems require accurate power consumption models, yet existing approaches rely on kinematic approximations that fail to capture the complex dynamics of real hardware. We present a lightweight autoregressive predictor for the GTernal mobile robot platform deployed in the Georgia Tech Robotarium. Through analysis of 48,000 samples collected across six motion trials, we discover that power consumption exhibits strong temporal autocorrelation ($ρ_1 = 0.95$) that dominates kinematic effects. A 7,041-parameter multi-layer perceptron (MLP) achieves $R^2 = 0.90$ on held-out motion patterns by conditioning on recent power history, reaching the theoretical prediction ceiling imposed by measurement noise. Physical validation across seven robots in a collision avoidance scenario yields mean $R^2 = 0.87$, demonstrating zero-shot transfer to unseen robots and behaviors. The predictor runs in 224 $μ$s per inference, enabling real-time deployment at 150$\times$ the platform's 30 Hz control rate. We release the trained model and dataset to support energy-aware multi-robot algorithm development.

翻译：多机器人系统的能量感知算法需要精确的功耗模型，然而现有方法依赖于运动学近似，无法捕捉真实硬件的复杂动态。我们为部署在佐治亚理工学院Robotarium中的GTernal移动机器人平台提出了一种轻量级自回归预测器。通过对六组运动试验中收集的48,000个样本进行分析，我们发现功耗表现出强烈的时间自相关性（$ρ_1 = 0.95$），其影响超过了运动学效应。一个包含7,041个参数的多层感知机（MLP）通过以近期功耗历史为条件，在预留运动模式上实现了$R^2 = 0.90$的预测精度，达到了由测量噪声所决定的理论预测上限。在七台机器人参与的避障场景中进行物理验证，平均$R^2 = 0.87$，证明了该预测器对未见过的机器人和行为具有零样本迁移能力。该预测器单次推理耗时224 $μ$s，能以平台30 Hz控制频率的150倍速率实时运行。我们发布了训练好的模型和数据集，以支持能量感知多机器人算法的开发。