用能耗数据评估工业机器人程序性能，更真实反映代码实际影响。
Evaluating Robot Program Performance with Power Consumption Driven Metrics in Lightweight Industrial Robots
- 从能耗角度分析代码执行效率，替代传统CPU指标。
- 提出三类新指标，量化能量利用、转换与可靠性表现。
- 适合关注能效与设备寿命的智能制造研发人员。
工业机器人代码性能通常通过CPU指标分析，但忽略了代码对机器人物理行为的影响。本研究提出一种基于实体化视角的新框架，通过分析机器人电能消耗特征来评估程序性能。该方法摒弃传统CPU依赖，采用归一化指标——能量利用率系数、能量转换度与可靠性系数，捕捉任务执行过程中能量使用的效率与可靠性。此外，建立机器人磨损度指标，进一步揭示长期运行可靠性。实验以UR5e机器人在上下料场景中对比四种不同策略的程序，结果表明所提指标可不依赖具体任务，直接比较并分类不同程序优劣，揭示各策略的优缺点，为优化编程实践提供可操作洞察。该基于实体化的评估方式不仅提升单机性能，还助力可持续制造与降本增效等工业目标。
原文摘要 · Abstract (English)
The code performance of industrial robots is typically analyzed through CPU metrics, which overlook the physical impact of code on robot behavior. This study introduces a novel framework for assessing robot program performance from an embodiment perspective by analyzing the robot's electrical power profile. Our approach diverges from conventional CPU based evaluations and instead leverages a suite of normalized metrics, namely, the energy utilization coefficient, the energy conversion metric, and the reliability coefficient, to capture how efficiently and reliably energy is used during task execution. Complementing these metrics, the established robot wear metric provides further insight into long term reliability. Our approach is demonstrated through an experimental case study in machine tending, comparing four programs with diverse strategies using a UR5e robot. The proposed metrics directly compare and categorize different robot programs, regardless of the specific task, by linking code performance to its physical manifestation through power consumption patterns. Our results reveal the strengths and weaknesses of each strategy, offering actionable insights for optimizing robot programming practices. Enhancing energy efficiency and reliability through this embodiment centric approach not only improves individual robot performance but also supports broader industrial objectives such as sustainable manufacturing and cost reduction.
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