Bayesian optimisation for Intelligent Environmental Monitoring

Environmental Monitoring (EM) is typically performed using sensor networks that collect measurements in predefined static locations. The possibility of having one or more autonomous robots to perform this task increases versatility and reduces the number of necessary sensor nodes to cover the same area. However, several problems arise when making use of autonomous moving robots for EM. The main challenges are how to build an accurate spatial-temporal model while choosing locations for measuring the phenomenon. This paper addresses the problem by using Bayesian Optimisation for choosing sensing locations, and presents a new utility function that takes into account the distance travelled by a moving robot. The proposed methodology is tested in simulation and in a real environment. Compared to existing strategies, our approach exhibits slightly better accuracy in terms of RMSE error and considerably reduces the total distance travelled by the robot.