Collective Odor Source Estimation and Search in Time-Variant Airflow Environments Using Mobile Robots

Sensors

Qing-Hao Meng¹  Wei-Xing Yang²  Yang Wang² 

[1] Institute of Robotics and Autonomous Systems, School of Electrical Engineering and Automation, Tianjin University, No. 92, Weijin Rd., Tianjin 300072, China;

关键词: odor source localization;    multi-robot;    estimation;    search;    Bayesian rules;    fuzzy inference;    particle swarm optimization;   

DOI  :  10.3390/s111110415

来源: mdpi

【 摘 要 】

This paper addresses the collective odor source localization (OSL) problem in a time-varying airflow environment using mobile robots. A novel OSL methodology which combines odor-source probability estimation and multiple robots’ search is proposed. The estimation phase consists of two steps: firstly, the separate probability-distribution map of odor source is estimated via Bayesian rules and fuzzy inference based on a single robot’s detection events; secondly, the separate maps estimated by different robots at different times are fused into a combined map by way of distance based superposition. The multi-robot search behaviors are coordinated via a particle swarm optimization algorithm, where the estimated odor-source probability distribution is used to express the fitness functions. In the process of OSL, the estimation phase provides the prior knowledge for the searching while the searching verifies the estimation results, and both phases are implemented iteratively. The results of simulations for large-scale advection–diffusion plume environments and experiments using real robots in an indoor airflow environment validate the feasibility and robustness of the proposed OSL method.

【 授权许可】

CC BY   
© 2011 by the authors; licensee MDPI, Basel, Switzerland.