Monte Carlo Tree Search Satellite Scheduling Under Cloud Cover Uncertainty

TL;DR

This paper presents a Monte Carlo Tree Search approach for satellite scheduling under cloud cover uncertainty, improving solution quality and efficiency in dynamic environments.

Contribution

It introduces two MCTS-based methods for multi-satellite scheduling under uncertainty and demonstrates their superior performance over existing algorithms.

Findings

MCTS outperforms traditional scheduling methods in solution quality.

Hyperparameter tuning enhances MCTS performance.

The approach is effective in dynamic, cloud-covered environments.

Abstract

Efficient utilization of satellite resources in dynamic environments remains a challenging problem in satellite scheduling. This paper addresses the multi-satellite collection scheduling problem (m-SatCSP), aiming to optimize task scheduling over a constellation of satellites under uncertain conditions such as cloud cover. Leveraging Monte Carlo Tree Search (MCTS), a stochastic search algorithm, two versions of MCTS are explored to schedule satellites effectively. Hyperparameter tuning is conducted to optimize the algorithm's performance. Experimental results demonstrate the effectiveness of the MCTS approach, outperforming existing methods in both solution quality and efficiency. Comparative analysis against other scheduling algorithms showcases competitive performance, positioning MCTS as a promising solution for satellite task scheduling in dynamic environments.