Machine Perception and Intelligent Robotics (MAPIR) Research Group, Malaga Institute for Mechatronics Engineering and Cyber-Physical Systems (IMECH.UMA), University of Malaga, 29071 Malaga, Spain.
Distributed Intelligent Systems and Algorithms Laboratory (DISAL), School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
Sensors (Basel). 2023 Jun 7;23(12):5387. doi: 10.3390/s23125387.
The ability to sense airborne pollutants with mobile robots provides a valuable asset for domains such as industrial safety and environmental monitoring. Oftentimes, this involves detecting how certain gases are spread out in the environment, commonly referred to as a gas distribution map, to subsequently take actions that depend on the collected information. Since the majority of gas transducers require physical contact with the analyte to sense it, the generation of such a map usually involves slow and laborious data collection from all key locations. In this regard, this paper proposes an efficient exploration algorithm for 2D gas distribution mapping with an autonomous mobile robot. Our proposal combines a Gaussian Markov random field estimator based on gas and wind flow measurements, devised for very sparse sample sizes and indoor environments, with a partially observable Markov decision process to close the robot's control loop. The advantage of this approach is that the gas map is not only continuously updated, but can also be leveraged to choose the next location based on how much information it provides. The exploration consequently adapts to how the gas is distributed during run time, leading to an efficient sampling path and, in turn, a complete gas map with a relatively low number of measurements. Furthermore, it also accounts for wind currents in the environment, which improves the reliability of the final gas map even in the presence of obstacles or when the gas distribution diverges from an ideal gas plume. Finally, we report various simulation experiments to evaluate our proposal against a computer-generated fluid dynamics ground truth, as well as physical experiments in a wind tunnel.
移动机器人感知空气中污染物的能力为工业安全和环境监测等领域提供了宝贵的资产。通常情况下，这涉及到检测特定气体在环境中的分布情况，通常称为气体分布图，然后根据收集到的信息采取相应的行动。由于大多数气体传感器需要与分析物物理接触才能进行感测，因此生成这样的地图通常需要从所有关键位置缓慢而费力地收集数据。在这方面，本文提出了一种用于二维气体分布映射的自主移动机器人高效探索算法。我们的提案将基于气体和风流动测量的高斯马尔可夫随机场估计器与部分可观察马尔可夫决策过程相结合，该过程针对非常稀疏的样本大小和室内环境设计，用于关闭机器人的控制回路。这种方法的优点是，不仅可以连续更新气体地图，还可以根据其提供的信息量来选择下一个位置。探索因此适应了运行时气体的分布方式，从而导致高效的采样路径，并最终在相对较少的测量次数下生成完整的气体地图。此外，它还考虑了环境中的风流，即使在存在障碍物或气体分布偏离理想气体羽流的情况下，也能提高最终气体地图的可靠性。最后，我们报告了各种模拟实验，以根据计算机生成的流体动力学地面实况以及在风洞中进行的物理实验来评估我们的提案。
