Abstract
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.
Highlights
The olfactory sense is crucial to the survival for many creatures, and has long played a fundamental role in human development and biosocial interaction
The gas-source localization algorithm is demonstrated for two different plume environments, which we refer to as slightly wandering and medium-wandering
The robots started from the right side and the lower right corner of the search area
Summary
The olfactory sense is crucial to the survival for many creatures, and has long played a fundamental role in human development and biosocial interaction. Simulation results in ventilated indoor environments demonstrated the feasibility and advantage of the P-PSO algorithm Spears and her colleagues [32] proposed a multi-robot CPT algorithm called fluxotaxis that follows the gradient of the chemical mass flux to locate a chemical source emitter. Ferri and his colleagues [15] used a biologically-inspired SPIRAL (Searching Pollutant Iterative Rounding ALgorithm) with MOMO (Multi-robot for Odor Monitoring) platform to localize a gas source in an indoor environment with no strong airflow. A novel collective OSL strategy which combines multi-robot search with gas source probability estimation is proposed in this paper.
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