This article presents a new decentralized multi-agent information-theoretic (DeMAIT) control algorithm for mobile sensors (agents). The algorithm leverages Bayesian estimation and information-theoretic motion planning for efficient and effective estimation and localization of a target, such as a chemical gas leak. The algorithm consists of: (1) a non-parametric Bayesian estimator, (2) an information-theoretic trajectory planner that generates “informative trajectories” for agents to follow, and (3) a controller and collision avoidance algorithm to ensure that each agent follows its trajectory as closely as possible in a safe manner. Advances include the use of a new information-gain metric and its analytical gradient, which do not depend on an infinite series like prior information metrics. Dynamic programming and multi-threading techniques are applied to efficiently compute the mutual information to minimize measurement uncertainty. The estimation and motion planning processes also take into account the dynamics of the sensors and agents. Extensive simulations are conducted to compare the performance between the DeMAIT algorithm to a traditional raster-scanning method and a clustering method with coordination. The main hypothesis that the DeMAIT algorithm outperforms the other two methods is validated, specifically where the average localization success rate for the DeMAIT algorithm is (a) higher and (b) more robust to changes in the source location, robot team size, and search area size than the raster-scanning and clustering methods. Finally, outdoor field experiments are conducted using a team of custom-built aerial robots equipped with gas concentration sensors to demonstrate efficacy of the DeMAIT algorithm to estimate and find the source of a propane gas leak.

The improper use of artificial light causing skyglow is detrimental to many types of wildlife and can potentially cause irregular human sleeping patterns. Studies have been performed to analyze light pollution on a global scale. However, light pollution data on a local scale is not of ten available and the effects at local scale have rarely been studied. Herein, a new custom-designed autonomous light assessment drone (ALAD) is described for evaluating light pollution at local scale. The ALAD is designed and equipped with a sky quality meter (SQM) to measure skyglow and a low-cost illuminance sensor to measure light from artificial sources. Outdoor field tests are performed at a remote site in central Utah and the measured results are validated against data from lightpollution-map.info. The SQM measurements are in agreement with the estimates from the light pollution map, and the initial results demonstrate feasibility of the ALAD for local-scale skyglow assessment.