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We present methods for autonomous collaborative surveying of volcanic CO 2 emissions using aerial robots. CO 2 is a useful predictor of volcanic eruptions and an influential greenhouse gas. However, current CO 2 mapping methods are hazardous and inefficient, as a result, only a small fraction of CO 2 emitting volcanoes have been surveyed. We develop algorithms and a platform to measure volcanic CO 2 emissions. The Dragonfly Unpiloted Aerial Vehicle (UAV) platform is capable of long-duration CO 2 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. 

In the search for life beyond Earth, distinguishing the living from the non-living is paramount. However, this distinction is often elusive, as the origin of life is likely a stepwise evolutionary process, not a singular event. Regardless of the favored origin of life model, an inherent “grayness” blurs the theorized threshold defining life. Here, we explore the ambiguities between the biotic and the abiotic at the origin of life. The role of grayness extends into later transitions as well. By recognizing the limitations posed by grayness, life detection researchers will be better able to develop methods sensitive to prebiotic chemical systems and life with alternative biochemistries.