Real-time chemical gas concentration map building of unknown environment by multiple mobile robots using bio-inspired algorithms

In this thesis implementations of various bio-inspired algorithms for obtaining high concentration regions of the chemical gas and real time chemical gas concentration mapping of an environment filled with a contaminant by using mobile robots are described. Khepera~III miniature mobile robots equipped with the \emph{``kheNose''} transducer are used to search and determine the high concentration regions of the chemical gas contaminant and to build $3D$ map of the chemical gas concentration in the environment. The search and mapping process is achieved by using the Particle Swarm Optimization (PSO), Bacterial Foraging Optimization (BFO), and Ant Colony Optimization (ACO) algorithms. Moreover, we perform Sweeping algorithm as base case to compare with implemented algorithms. During the experiments at each step the robots share their sensor readings and position data through a wireless network among each other and also send to a remote computer where the data are combined, filtered, and interpolated to form the chemical concentration 3D map of the environment in real time. In order to obtain smoother chemical concentration map the Kriging method is used and an extrapolation and an interpolation algorithms are applied. The performance of the implemented algorithms are also compared in terms of the quality of the maps obtained and success of locating the contaminating gas sources.