IEEE Aerospace Conference. IEEE Aerospace Conference最新文献

More than any other known planet, Venus is essential to our understanding of the evolution and habitability of Earth-size planets throughout the galaxy. We address two critical questions for planetary science: 1) How, if at all, did Venus evolve through a habitable phase? 2) What circumstances affect how volatiles shape habitable worlds? Volatile elements have a strong influence on the evolutionary paths of rocky bodies and are critical to understanding solar system evolution. It is clear that Venus experienced a different volatile element history from the Earth and provides the only accessible example of one end-state of habitable Earth-size planets. Venus will allow us to identify the mechanisms that operate together to produce and maintain habitable worlds like our own. The (VFM) concept architecture relies on five collaborative platforms: an Orbiter, Lander, variable-altitude Aerobot and two Small Satellites (SmallSats) delivered via a single launch on a Falcon 9 heavy expendable. The platforms would use multiple instruments to measure the exosphere, atmosphere and surface at multiple scales with high precision and over time. VFM would provide the first measurements of mineralogy and geochemistry of tessera terrain to examine rocks considered to be among the most likely to have formed in a habitable climate regime. Landed, descent, aerial and orbital platforms would work synergistically to measure the chemical composition of the atmosphere including the Aerobot operating for 60 days in the Venus clouds. Loss mechanisms would be constrained by the SmallSats in two key orbits. The baseline payload for VFM includes instruments to make the first measurements of seismicity and remanent magnetism, the first long-lived (60 day) surface platform and the first life detection instrument at Venus to interrogate what could be an inhabited world. The VFM concept directly addresses each of the three Venus Exploration Analysis Group (VEXAG) goals as well as several of the strategic objectives of the 2020 NASA Science Plan, Planetary Science Division, Heliophysics and Astrophysics. The simultaneous, synergistic measurements of the solid body, surface, atmosphere and space environment provided by the VFM would allow us to target the most accessible Earth-size planet in our galaxy, and gain a profound new understanding of the evolution of our solar system and habitable worlds.

Multi-Reward Proximal Policy Optimization, a multi-objective deep reinforcement learning algorithm, is used to examine the design space of low-thrust trajectories for a SmallSat transferring between two libration point orbits in the Earth-Moon system. Using Multi-Reward Proximal Policy Optimization, multiple policies are simultaneously and efficiently trained on three distinct trajectory design scenarios. Each policy is trained to create a unique control scheme based on the trajectory design scenario and assigned reward function. Each reward function is defined using a set of objectives that are scaled via a unique combination of weights to balance guiding the spacecraft to the target mission orbit, incentivizing faster flight times, and penalizing propellant mass usage. Then, the policies are evaluated on the same set of perturbed initial conditions in each scenario to generate the propellant mass usage, flight time, and state discontinuities from a reference trajectory for each control scheme. The resulting low-thrust trajectories are used to examine a subset of the multi-objective trade space for the SmallSat trajectory design scenario. By autonomously constructing the solution space, insights into the required propellant mass, flight time, and transfer geometry are rapidly achieved.