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We present methods for autonomous collaborative surveying of volcanic CO2 emissions using aerial robots. CO2 is a useful predictor of volcanic eruptions and an influential greenhouse gas. However, current CO2 mapping methods are hazardous and inefficient, as a result, only a small fraction of CO2 emitting volcanoes have been surveyed. We develop algorithms and a platform to measure volcanic CO2 emissions. The Dragonfly Unpiloted Aerial Vehicle (UAV) platform is capable of long-duration CO2 collection flights in harsh environments. We implement two survey algorithms on teams of Dragonfly robots and demonstrate that they effectively map gas emissions and locate the highest gas concentrations. Our experiments culminate in a successful field test of collaborative rasterization and gradient descent algorithms in a challenging real-world environment at the edge of the Valles Caldera supervolcano. Both algorithms treat multiple flocking UAVs as a distributed flexible instrument. Simultaneous sensing in multiple UAVs gives scientists greater confidence in estimates of gas concentrations and the locations of sources of those emissions. These methods are also applicable to a range of other airborne concentration mapping tasks, such as pipeline leak detection and contaminant localization. [ABSTRACT FROM AUTHOR]

Denis Diderot, in 'Salon de 1767' and the 'Lecons de clavecin' of 1771, avoids both purely linear explanatory logic and the more systematic approach to knowledge. Some have seen this dichotomy as characteristic of the Enlightenment. In these works by Diderot, however, the logic and the flow of information is far from systematic, as seemingly incongruous approaches are given side by side. The approach is also non-linear, studying the topics at hand in a manner that is charged with immediacy while remaining reflexive and suggesting a progressive accumulation towards a single end, which never comes.