Proceedings Abstracts of the Twenty-Fourth International Joint Conference on Artificial Intelligence

Interplanetary Trajectory Planning with Monte Carlo Tree Search / 769
Daniel Hennes, Dario Izzo

Planning an interplanetary trajectory is a very complex task, traditionally accomplished by domain experts using computer-aided design tools. Recent advances in trajectory optimization allow automation of part of the trajectory design but have yet to provide an efficient way to select promising planetary encounter sequences. In this work, we present a heuristic-free approach to automated trajectory planning (including the encounter sequence planning) based on Monte Carlo Tree Search (MCTS). We discuss a number of modifications to traditional MCTS unique to the domain of interplanetary trajectory planning and provide results on the Rosetta and Cassini-Huygens interplanetary mission design problems. The resulting heuristic-free method is found to be orders of magnitude more efficient with respect to a standard tree search with heuristic-based pruning which is the current state-of-the art in this domain.