Autonomy in the Real-World: Autonomous Trajectory Planning for Asteroid Reconnaissance via Stochastic Optimization

TL;DR

The paper addresses autonomous trajectory planning for the reconnaissance phase of small-body missions under significant uncertainties. It introduces a stochastic optimal control problem, then reformulates it deterministically via Gaussian uncertainty propagation and polyhedral approximations to enable efficient solving with off-the-shelf nonlinear optimizers. Key contributions include MuSCAT-based validation of modeling assumptions, a detailed uncertainty-propagation framework, and a demonstrated performance improvement over current practice (e.g., OREX) in Bennu-like recon scenarios. The work advances real-time onboard autonomy for small-body exploration, with potential to reduce ground intervention and accelerate mission science return.

Abstract

This paper presents the development and evaluation of an optimization-based autonomous trajectory planning algorithm for the asteroid reconnaissance phase of a deep-space exploration mission. The reconnaissance phase is a low-altitude flyby to collect detailed information around a potential landing site. Although such autonomous deep-space exploration missions have garnered considerable interest recently, state-of-the-practice in trajectory design involves a time-intensive ground-based open-loop process that forward propagates multiple trajectories with a range of initial conditions and parameters to account for uncertainties in spacecraft knowledge and actuation. In this work, we introduce a stochastic trajectory optimization-based approach to generate trajectories that satisfy both the mission and spacecraft safety constraints during the reconnaissance phase of the Deep-space Autonomous Robotic Explorer (DARE) mission concept, which seeks to travel to and explore a near-Earth object autonomously, with minimal ground intervention. We first use the Multi-Spacecraft Concept and Autonomy Tool (MuSCAT) simulation framework to rigorously validate the underlying modeling assumptions for our trajectory planner and then propose a method to transform this stochastic optimal control problem into a deterministic one tailored for use with an off-the-shelf nonlinear solver. Finally, we demonstrate the efficacy of our proposed algorithmic approach through extensive numerical experiments and show that it outperforms the state-of-the-practice benchmark used for representative missions.

Figures (16)

• Figure 1: Overview: The proposed framework provides a trajectory for the reconnaissance phase. The realistic mission concept considered in this paper includes pre-scheduled $4$ times maneuvers ($t_0, t_1, t_2$, and $t_f$) and observations throughout the pre-scheduled observation time window (from $t_1^o$ to $t_2^o$). The proposed planner uses a stochastic optimization-based approach to efficiently deal with uncertainties and all realistic mission constraints. As a result, mission planners enjoy the efficiency, flexibility, and robustness provided by the proposed trajectory planner.
• Figure 2: Overview of observation: The spacecraft must meet all observation constraints continuously throughout the designated observation time window. Red regions indicate keep-out zones, whereas blue regions represent keep-in zones. Intersections of the blue and red zones are also considered keep-out zones. Those zones may move differently depending on different factors, such as the relative position of the spacecraft, the normal vector of a sample site, and the vector from the site to the Sun.
• Figure 3: Overview of the Deep-space Autonomous Robotic Explorer (DARE) mission concept that seeks use autonomy across all mission phases from approach through proximity operations, landing and surface operations. In this work, we focus on the reconnaissance phase for science mapping and landing site identification.
• Figure 4: The design of a conceptual spacecraft for the DARE project.
• Figure 5: Overview of Multi-Spacecraft Concept and Autonomy Tool (MuSCAT). Figure shows the different components of the spacecraft that MuSCAT considers. MuSCAT serves as a test bed for understanding which mission process may benefit from autonomy.