Abstract
We consider chemical plume tracing (CPT) in time-varying airflow environments using multiple mobile robots. The purpose of CPT is to approach a gas source with a previously unknown location in a given area. Therefore, the CPT could be considered as a dynamic optimization problem in continuous domains. The traditional ant colony optimization (ACO) algorithm has been successfully used for combinatorial optimization problems in discrete domains. To adapt the ant colony metaphor to the multi-robot CPT problem, the two-dimension continuous search area is discretized into grids and the virtual pheromone is updated according to both the gas concentration and wind information. To prevent the adapted ACO algorithm from being prematurely trapped in a local optimum, the upwind surge behavior is adopted by the robots with relatively higher gas concentration in order to explore more areas. The spiral surge (SS) algorithm is also examined for comparison. Experimental results using multiple real robots in two indoor natural ventilated airflow environments show that the proposed CPT method performs better than the SS algorithm. The simulation results for large-scale advection-diffusion plume environments show that the proposed method could also work in outdoor meandering plume environments.
Highlights
Olfaction is widely used by many animals for searching for food, finding mates, exchanging information, and evading predators
The large-scale advection–diffusion simulated plume environment created by Farrell et al [18] was used to verify if the proposed AACO+US chemical plume tracing (CPT) algorithm could cope with the plume meander issue
The AACO+US CPT algorithm was demonstrated for three different plume environments, which we referred to as slightly wandering, medium-wandering and greatly wandering plumes
Summary
Olfaction is widely used by many animals for searching for food, finding mates, exchanging information, and evading predators. The results of real-robot experiments in ventilated indoor environments and simulations for large-scale advection–diffusion plume environments demonstrated the feasibility and advantage of the proposed P-PSO algorithm. Spears and her colleagues [22] proposed a multi-robot CPT algorithm called fluxotaxis that follows the gradient of the chemical mass flux to locate a chemical source emitter. Used a biologically-inspired algorithm called Searching Pollutant Iterative Rounding Algorithm (SPIRAL) with a Multi-robot for Odor Monitoring (MOMO) platform to localize a gas source in an indoor environment with no strong airflow. Meng et al [24] applied an improved ant colony optimization (ACO) algorithm to multi-robot odor-plume tracing in indoor airflow environments, and real robot experiments demonstrated its feasibility.
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