# Sequential Bayesian Optimisation as a POMDP for Environment Monitoring with UAVs

arXiv: https://arxiv.org/abs/1703.04211 · HTML: https://mlsanity.com/paper/1703.04211/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2017.

## Built on by (most cited first, 7 of 7)

- [The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning](https://mlsanity.com/paper/2405.03164/) (2024)
- [Generalized Mission Planning for Heterogeneous Multi-Robot Teams via LLM-constructed Hierarchical Trees](https://mlsanity.com/paper/2501.16539/) (2025)
- [Bayesian Optimisation for Active Monitoring of Air Pollution](https://mlsanity.com/paper/2202.07595/) (2022)
- [Sequential Bayesian Optimization for Adaptive Informative Path Planning with Multimodal Sensing](https://mlsanity.com/paper/2209.07660/) (2022)
- [Bayesian Optimisation Against Climate Change: Applications and Benchmarks](https://mlsanity.com/paper/2306.04343/) (2023)
- [Domain Curiosity: Learning Efficient Data Collection Strategies for Domain Adaptation](https://mlsanity.com/paper/2103.07223/) (2021)
- [Distributed Mission Planning of Complex Tasks for Heterogeneous Multi-Robot Teams](https://mlsanity.com/paper/2109.10106/) (2021)

## Abstract (arXiv)

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.