为传感器建模以提高其效率
Modeling a Sensor to Improve its Efficacy
摘要：
机器人依靠传感器为它们提供周围环境的信息。然而，高质量的传感器可能非常昂贵，成本过高。因此，许多机器人系统必须使用低质量的传感器。在这里，我们通过一个案例研究如何建模传感器可以提高其效率时，采用贝叶斯推理框架。作为一个测试平台，我们使用了一个机械臂，该机械臂被设计为使用廉价的乐高光传感器自动进行测量，以估计黑色场地上白色圆圈的位置和半径。光传感器对从其视场内的空间分布区域到达的光进行积分，所述光由其空间灵敏度函数（SSF）加权。我们证明，通过将一个准确的模型的光传感器SSF的似然函数的贝叶斯推断引擎，一个自治系统可以使其周围的改进的推断。这里介绍的方法是基于数据的，相当通用，并考虑到即插即用，以便它可以在类似的问题中实现。
英文摘要：
Robots rely on sensors to provide them with information about their surroundings. However, high-quality sensors can be extremely expensive and cost-prohibitive. Thus many robotic systems must make due with lower-quality sensors. Here we demonstrate via a case study how modeling a sensor can improve its efficacy when employed within a Bayesian inferential framework. As a test bed we employ a robotic arm that is designed to autonomously take its own measurements using an inexpensive LEGO light sensor to estimate the position and radius of a white circle on a black field. The light sensor integrates the light arriving from a spatially distributed region within its field of view weighted by its Spatial Sensitivity Function (SSF). We demonstrate that by incorporating an accurate model of the light sensor SSF into the likelihood function of a Bayesian inference engine, an autonomous system can make improved inferences about its surroundings. The method presented here is data-based, fairly general, and made with plug-and play in mind so that it could be implemented in similar problems.
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