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軌道最適化
Trajectory Optimization for Adaptive Informative Path Planning with Multimodal Sensing
複数のセンサを持つ自律エージェントが、資源制約下で未知環境を探索し情報を収集する問題を軌道最適化として定式化し、ガウス過程の信念分散を低減する投影ベース手法で解いた。NASA VIPERミッションのローバ経路計画を支援する。
著者: Joshua Ott, Edward Balaban, Mykel Kochenderfer
分類: cs.RO
原文アブストラクト
We consider the problem of an autonomous agent equipped with multiple sensors, each with different sensing precision 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. The challenge lies in reasoning about the effects of sensing and movement while respecting the agent's resource and dynamic constraints. We formulate the problem as a trajectory optimization problem and solve it using a projection-based trajectory optimization approach where the objective is to reduce the variance of the Gaussian process world belief. Our approach outperforms previous approaches in long horizon trajectories by achieving an overall variance reduction of up to 85% and reducing the root-mean square error in the environment belief by 50%. This approach was developed in support of rover path planning for the NASA VIPER Mission.
