Prospects of the µHAPS Miniaturised Stratospheric Platform for Remote Sensing and Environmental Monitoring in Light of Recent Flight Demonstrations

The Space Systems Laboratory of the Department of Civil and Industrial Engineering at the University of Pisa (UniPi) has been active in the field of stratospheric flight platform design, construction and operation since 2018. A total of 34 autonomous near-space missions were carried out in 2019-24, many of them in support of scientific investigations and technology testing by academic and industrial entities. Development efforts are now focused on the advancement of µHAPS, a small platform intended to support a payload of up to 15 kg in the stratosphere for several days. The platform relies on the use of low-cost latex meteorological balloons for lift and a number of mission-enabling subsystems specifically developed at UniPi, including long range telemetry and telecommand, photovoltaic power generation, and guided payload re-entry and recovery. In addition to potential uses for technology demonstration and validation in the near-space environment (e.g., solar cells, sensors) and for science (e.g., astronomy, physics of the atmosphere), µHAPS is particularly well suited for Earth Observation. Optical multispectral sensors, or possibly a small synthetic aperture radar, derived from current microsatellite technology can be operated from the platform, thanks to the availability of substantial electric power onboard (25 W to the payload). Two critical functions of the µHAPS platform have recently been successfully demonstrated in flight: azimuth pointing of the payload gondola and long duration permanence in the stratosphere. None of these functions are normally provided by standard weather balloon flight trains, while are found only on large zero- or super-pressure balloon platforms, with costs and complexity orders of magnitude larger. Attitude stabilization allows optical payloads to point in the desired direction, high-gain antennas to remain in view of a ground station, the solar array to remain pointed toward the Sun, etc. Long-term stay (three to five days) within a fixed-range area around a point of interest is achieved through altitude control maneuvers guided by a dedicated trajectory controller based on reinforcement learning. In this work, we present the results of our recent flight activities, including successful demonstration of both technologies in real missions, and discuss the expected performance and potential use cases of the µHAPS platform for remote sensing and environmental monitoring applications.