The ARTEMIS docking system demonstrates autonomous docking capability applicable to robotic exploration of sub‐ice oceans and sub‐glacial lakes on planetary bodies, as well as here on Earth. In these applications, melted or drilled vertical access shafts restrict vehicle geometry as well as the in‐water infrastructure that may be deployed. The ability of the vehicle to return reliably and precisely to the access point is critical for data return, battery charging, and/or vehicle recovery. This paper presents the mechanical, sensor, and software components that make up the ARTEMIS docking system, as well as results from field deployment of the system to McMurdo Sound, Antarctica in the austral spring of 2015. The mechanical design of the system allows the vehicle to approach the dock from any direction and to pitch up after docking for recovery through a vertical access shaft. It uses only a small volume of in‐water equipment and may be deployed through a narrow vertical access shaft. The software of the system reduces position estimation error with a hierarchical combination of dead reckoning, acoustic aiding, and machine vision. The system provides critical operational robustness, enabling the vehicle to return autonomously and precisely to the access shaft and latch to the dock with no operator input.

The development of algorithms for agile science and autonomous exploration has been pursued in contexts ranging from spacecraft to planetary rovers to unmanned aerial vehicles to autonomous underwater vehicles. In situations where time, mission resources and communications are limited and the future state of the operating environment is unknown, the capability of a vehicle to dynamically respond to changing circumstances without human guidance can substantially improve science return. Such capabilities are difficult to achieve in practice, however, because they require intelligent reasoning to utilize limited resources in an inherently uncertain environment. Here we discuss the development, characterization and field performance of two algorithms for autonomously collecting water samples on VALKYRIE (Very deep Autonomous Laser-powered Kilowatt-class Yo-yoing Robotic Ice Explorer), a glacier-penetrating cryobot deployed to the Matanuska Glacier, Alaska (Mission Control location: 61°42′09.3″N 147°37′23.2″W). We show performance on par with human performance across a wide range of mission morphologies using simulated mission data, and demonstrate the effectiveness of the algorithms at autonomously collecting samples with high relative cell concentration during field operation. The development of such algorithms will help enable autonomous science operations in environments where constant real-time human supervision is impractical, such as penetration of ice sheets on Earth and high-priority planetary science targets like Europa.

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A Rapid Thermal Annealing (RTA) system was used to anneal sputtered and MOVPEgrown Aluminum Nitride (AlN) thin films at temperatures up to 1000°C in ambient and controlled environments. According to Energy Dispersive X-Ray Analysis (EDAX), the films annealed in an ambient environment rapidly oxidize after five minutes at 1000°C. Below 1000°C the films oxidized linearly as a function of annealing temperature which is consistent with what has been reported in literature [1]. Laser Doppler Vibrometry (LDV) was used to measure the piezoelectric coefficient, d 33 , of these films. Films annealed in an ambient environment had a weak piezoelectric response indicating that oxidation on the surface of the film reduces the value of d 33 . A high temperature furnace has been built that is capable of taking in-situ measurements of the piezoelectric response of AlN films. In-situ d 33 measurements are recorded up to 300°C for both sputtered and MOVPE-grown AlN thin films. The measured piezoelectric response appears to increase with temperature up to 300°C possibly due to stress in the film.

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