UAVによる環境モニタリングのためのPOMDPとしての逐次ベイズ最適化
Sequential Bayesian Optimisation as a POMDP for Environment Monitoring with UAVs
連続軌道でのベイズ最適化をPOMDPとして定式化し、MCTSで解くことで、UAVが物理的・軌道制約下で空間現象を効率的にモニタリングできる手法を提案した。
著者: Philippe Morere, Roman Marchant, Fabio Ramos
分類: cs.RO
原文アブストラクト
Bayesian Optimisation has gained much popularity lately, as a global optimisation technique for functions that are expensive to evaluate or unknown a priori. While classical BO focuses on where to gather an observation next, it does not take into account practical constraints for a robotic system such as where it is physically possible to gather samples from, nor the sequential nature of the problem while executing a trajectory. In field robotics and other real-life situations, physical and trajectory constraints are inherent problems. This paper addresses these issues by formulating Bayesian Optimisation for continuous trajectories within a Partially Observable Markov Decision Process (POMDP) framework. The resulting POMDP is solved using Monte-Carlo Tree Search (MCTS), which we adapt to using a reward function balancing exploration and exploitation. Experiments on monitoring a spatial phenomenon with a UAV illustrate how our BO-POMDP algorithm outperforms competing techniques.
