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

arXiv: https://arxiv.org/abs/2412.06816 · HTML: https://mlsanity.com/paper/2412.06816/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2024.

## Built on by (most cited first, 1 of 1)

- [Planetary Exploration 3.0: A Roadmap for Software-Defined, Radically Adaptive Space Systems](https://mlsanity.com/paper/2604.20910/) (2026)

## Abstract (arXiv)

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.