Planning for an Uncertain Future: Tree-Based Methods for Real-Time Fault Estimation, Collision Avoidance, and Multi-Agent Reconfiguration

Citation

Ragan, James Francis, III (2025) Planning for an Uncertain Future: Tree-Based Methods for Real-Time Fault Estimation, Collision Avoidance, and Multi-Agent Reconfiguration. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/ptpk-d504. https://resolver.caltech.edu/CaltechTHESIS:02252025-021704066

Abstract

Autonomous spacecraft making independent high-level decisions present the promise of dramatically increased productivity in space for both exploration and economic activity. While autonomy has seen limited use in space to date owing to a lack of flight heritage, limited computational resources, and a traditionally risk adverse industry, the growing numbers of spacecraft and increasingly ambitious missions will soon render the current ground-intensive mode of space operation untenable.

In this thesis, we develop two critical capabilities for an autonomous future in space. The first is proactive fault estimation, which seeks to rapidly and safely identify the root causes of onboard anomalies by planning sequences of test actions to gather information while probabilistically ensuring safety. The second is real-time reconfiguration to enable formations of spacecraft to respond quickly and effectively to changing environments or mission objectives.

We achieve both goals using various forms of Monte-Carlo Tree Search planning. By formalizing each capability as sequential decision-making problems, and developing algorithms well suited to information gathering, we show that our algorithms provably converge to optimal solutions while maintaining the ability to run in real-time on robotic spacecraft simulators. We present several algorithmic innovations, including marginalized filtering, sampling-based chance constraint evaluation, and an array-based implementation of Monte-Carlo Tree Search. Through and numerical simulations and hardware experiments, we demonstrate that these modifications enable our algorithms to outperform existing tree search methods and achieve better scaling across system complexity, noise, and simulation depth.

Thesis Files