Gas source localization (GSL) with an autonomous robot is a problem with many prospective applications, from finding pipe leaks to emergency-response scenarios. In this work we present a new method to perform GSL in realistic indoor environments, featuring obstacles and turbulent flow. Given the highly complex relationship between the source position and the measurements available to the robot (the single-point gas concentration, and the wind vector) we propose an observation model that derives from contrasting the online, real-time simulation of the gas dispersion from any candidate source localization against a gas concentration map built from sensor readings. To account for a convenient and grounded integration of both into a probabilistic estimation framework, we introduce the concept of probabilistic gas-hit maps, which provide a higher level of abstraction to model the time-dependent nature of gas dispersion. Results from both simulated and real experiments show the capabilities of our current proposal to deal with source localization in complex indoor environments. To the best of our knowledge, this is the first work in olfactory robotics that doesn't make simplistic assumptions about environmental conditions like operating in open spaces and/or having an unrealistic laminar flow wind.
翻译:气源定位(GSL)是自主机器人的问题,具有许多潜在的应用场景,从找到管道泄漏到应急响应情况。本文提出了一种在现实室内环境中进行GSL的新方法,其中包括障碍物和湍流流动。由于源位置与机器人可用测量之间的高度复杂关系(单点气体浓度和风向量),我们提出一种观察模型,该模型通过将任何候选源定位的在线实时气体扩散模拟与从传感器读数构建的气体浓度图进行对比而导出。为了考虑便于地、基于概率的集成两者到一个概率估计框架中,我们引入了概率气体命中图的概念,这提供了更高的抽象层次来建模气体扩散的时变性质。虽然尚存在着应对复杂室内环境中的源定位的挑战,但模拟和实际实验结果显示了我们目前提议的处理可行性。据我们所知,这是气味感知机器人领域中的首个作品,该作品不对环境条件做出简化假设(如在开放空间进行操作和/或具有不现实的层流风)。