Distributed Intelligent Systems and Algorithms Laboratory, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.
Sensors (Basel). 2019 Feb 5;19(3):656. doi: 10.3390/s19030656.
Finding sources of airborne chemicals with mobile sensing systems finds applications across safety, security, environmental monitoring, and medical domains. In this paper, we present an algorithm based on Source Term Estimation for odor source localization that is coupled with a navigation method based on partially observable Markov decision processes. We propose a novel strategy to balance exploration and exploitation in navigation. Moreover, we study two variants of the algorithm, one exploiting a global and the other one a local framework. The method was evaluated through high-fidelity simulations and in a wind tunnel emulating a quasi-laminar air flow in a controlled environment, in particular by systematically investigating the impact of multiple algorithmic and environmental parameters (wind speed and source release rate) on the overall performance. The outcome of the experiments showed that the algorithm is robust to different environmental conditions in the global framework, but, in the local framework, it is only successful in relatively high wind speeds. In the local framework, on the other hand, the algorithm is less demanding in terms of energy consumption as it does not require any absolute positioning information from the environment and the robot travels less distance compared to the global framework.
使用移动感测系统寻找空气中化学物质的来源在安全、保障、环境监测和医疗领域都有应用。在本文中，我们提出了一种基于源项估计的恶臭源定位算法，该算法与基于部分可观测马尔可夫决策过程的导航方法相结合。我们提出了一种新的策略来平衡导航中的探索和利用。此外，我们研究了该算法的两种变体，一种利用全局框架，另一种利用局部框架。该方法通过高保真模拟和在模拟受控环境下准层流空气流动的风洞中进行评估，特别是通过系统地研究多个算法和环境参数（风速和源释放速率）对整体性能的影响。实验结果表明，该算法在全局框架下对不同的环境条件具有鲁棒性，但在局部框架下，它仅在相对较高的风速下才成功。另一方面，在局部框架下，该算法对能量消耗的要求较低，因为它不需要环境的任何绝对定位信息，并且与全局框架相比，机器人行驶的距离较短。
