机器人群体覆盖率的定量评估
Quantitative Assessment of Robotic Swarm Coverage
摘要：
本文研究了一种普遍适用的，敏感的，直观的误差度量机器人群体密度控制器的性能评估。受涡滴数值方法的启发，它克服了基于离散化的常用策略的缺点，并统一了其他连续覆盖的概念。我们提出了两个基准，对一个给定的群配置的误差度量值进行比较：非平凡的边界上的误差度量，和密度函数的误差度量时，机器人的位置是随机抽样的目标群分布。我们给出严格的结果，这密度函数的误差度量服从一个中心极限定理，允许更有效的数值逼近。对于这两个基准，我们提出了支持理论，计算方法，例子，和MATLAB实现代码。
英文摘要：
This paper studies a generally applicable, sensitive, and intuitive error metric for the assessment of robotic swarm density controller performance. Inspired by vortex blob numerical methods, it overcomes the shortcomings of a common strategy based on discretization, and unifies other continuous notions of coverage. We present two benchmarks against which to compare the error metric value of a given swarm configuration: non-trivial bounds on the error metric, and the probability density function of the error metric when robot positions are sampled at random from the target swarm distribution. We give rigorous results that this probability density function of the error metric obeys a central limit theorem, allowing for more efficient numerical approximation. For both of these benchmarks, we present supporting theory, computation methodology, examples, and MATLAB implementation code.
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