This works deals with the problem of long-term autonomy in the context of multi-day field deployments of Micro Aerial Vehicle (MAV) systems. To truly depart from the necessity for human intervention for the crucial task of providing battery recharging, and to liberate from the need to operate in a confined range around specially installed infrastructure such as recharging pods, the MAV robot is required to harvest power on its own, but equally importantly also sustain prolonged periods of ambient power scarcity. This implies being able to sustain the battery charge overnight when using solar recharging, or even during multiple days of illumination inadequacy (e.g., due to degraded atmospheric lucidity and heavy overcast). We address this by presenting a Self-Sustainable Autonomous System architecture for MAVs centered around a specially tailored Power Management Stack, which is capable of achieving deep system hibernation, a feature that facilitates the aforementioned functionalities. We present a) continuous, b) multi-day successive, and c) externally-powered recharging that uses a legged robot-mounted Mobile Recharging Station. We conclude by demonstrating a challenging zero-intervention multi-day field deployment mission in the N.Nevada region.

Nested relational query languages have long been seen as an attractive tool for scenarios involving large hierarchical datasets. There has been a resurgence of interest in nested relational languages. One driver has been the affinity of these languages for large-scale processing platforms such as Spark and Flink. This demonstration gives a tour of TraNCE, a new system for processing nested data on top of distributed processing systems. The core innovation of the system is a compiler that processes nested relational queries in a series of transformations; these include variants of two prior techniques, shredding and unnesting, as well as a materialization transformation that customizes the way levels of the nested output are generated. The TraNCE platform builds on these techniques by adding components for users to create and visualize queries, as well as data exploration and notebook execution targets to facilitate the construction of large-scale data science applications. The demonstration will both showcase the system from the viewpoint of usability by data scientists and illustrate the data management techniques employed.