Sequential Bayesian Optimisation as a POMDP for Environment Monitoring with UAVs

Philippe Morere; Roman Marchant; Fabio Ramos

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arXiv · 1703.04211

Sequential Bayesian Optimisation as a POMDP for Environment Monitoring with UAVs

Philippe Morere·Roman Marchant·Fabio Ramos

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Abstract

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.

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BibTeX RIS

Philippe Morere, Roman Marchant, Fabio Ramos. 2017-03-13. Sequential Bayesian Optimisation as a POMDP for Environment Monitoring with UAVs. https://arxiv.org/abs/1703.04211

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