マルチモーダルセンシングを用いた適応的情報経路計画のための逐次ベイズ最適化
Sequential Bayesian Optimization for Adaptive Informative Path Planning with Multimodal Sensing
複数のセンサを持つエージェントが、限られた資源の中で未知環境を探索し情報を集める問題を、逐次ベイズ最適化とオンライン計画で解き、従来手法より平均報酬を2倍以上に改善した研究。
著者: Joshua Ott, Edward Balaban, Mykel J. Kochenderfer
分類: cs.AI, cs.RO
原文アブストラクト
Adaptive Informative Path Planning with Multimodal Sensing (AIPPMS) considers the problem of an agent equipped with multiple sensors, each with different sensing accuracy and energy costs. The agent's goal is to explore the environment and gather information subject to its resource constraints in unknown, partially observable environments. Previous work has focused on the less general Adaptive Informative Path Planning (AIPP) problem, which considers only the effect of the agent's movement on received observations. The AIPPMS problem adds additional complexity by requiring that the agent reasons jointly about the effects of sensing and movement while balancing resource constraints with information objectives. We formulate the AIPPMS problem as a belief Markov decision process with Gaussian process beliefs and solve it using a sequential Bayesian optimization approach with online planning. Our approach consistently outperforms previous AIPPMS solutions by more than doubling the average reward received in almost every experiment while also reducing the root-mean-square error in the environment belief by 50%. We completely open-source our implementation to aid in further development and comparison.
