Optimal Trajectory Design for Innovative Low-thrust Spacecraft Missions

The objective of this proposal is to investigate innovative mission trajectories made possible by the application of low-thrust propulsion systems including solar electric propulsion (SEP), solar sails, and hybrid low-thrust propulsion (HLTP). Trajectories will be generated by pairing direct optimization methods, such as direct forward-backward multiple shooting (FBMS), with monotonic basin hopping (MBH), a global search algorithm. Using these methods, the feasibility of new exploration missions and the appropriate utilization of different state-of-the-art propulsion systems will be determined. By providing continuous thrust without consuming fuel, solar sails enable trajectories and orbits that are impossible with other forms of propulsion. One such example is the study of interstellar objects (ISOs) using statites. A statite is a satellite that uses a solar sail to counteract the gravitational attraction of the Sun to remain motionless in the heliocentric inertial frame. When an ISO is detected, the spacecraft reorients itself to remove the thrust of the solar sail and falls towards the Sun. In doing so, it converts the enormous potential energy of its stationary state into kinetic energy, allowing it to rendezvous with the fast-moving ISO. Without the concern of fuel depletion, the initial, static state may be maintained indefinitely, while the spacecraft waits for an appropriate target object to be discovered. Another mission concept is the interplanetary pole sitter, which, upon reaching a planet or moon, uses its solar sail to remain either directly, or within a narrow cone, above the pole of another planet, allowing for continuous scientific study. By eliminating the mass of onboard fuel, solar sails offer superior payload mass fractions than spacecraft using other forms of propulsion. However, this comes at the expense of time en route and with limitations on both the direction and magnitude of available thrust. This proposal therefore also seeks to investigate pairing a solar sail with electric propulsion, otherwise known as hybrid low-thrust propulsion, to better characterize the performance of such an arrangement as it pertains to trajectory design. Compared to either pure electric or solar sail propulsion, HLTP is a compromise between the greater speed of the former and the improved payload mass fraction of the latter. This study will utilize a nonidealized propulsion model compatible with direct optimization methods based on the present state-of-the-art of both propulsion systems. This will include important performance factors such as the areal mass of realistic sail materials and the service life of the electric propulsion system.

This technology could dramatically enhance the deep-space mission planning process. Advances in reinforcement learning (RL) and deep neural networks offer performance that can match or even surpass that of current state-of-the-art methods in complex tasks with large design spaces. This proposal seeks to apply this technology to the generation and control of energy efficient, low-thrust spacecraft trajectories. By applying RL to the design of interplanetary missions, future spacecraft will be able to more easily follow low-thrust trajectories and take advantage of multi-body dynamics, instead of the traditional Hohmann transfer. Non-standard orbits about libration points would also be made more available. Furthermore, by not requiring large computational resources, this technology could be applied to future manned missions for onboard mission planning. The increased freedom in mission design afforded by this technology would offer significant benefits to both NASA and other spaceflight programs.

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