# Sequential Bayesian Optimization for Adaptive Informative Path Planning with Multimodal Sensing

arXiv: https://arxiv.org/abs/2209.07660 · HTML: https://mlsanity.com/paper/2209.07660/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 16 Sep 2022.

## Builds on (most cited first, 10 of 10)

- [Online algorithms for POMDPs with continuous state, action, and observation spaces](https://mlsanity.com/paper/1709.06196/) (2017)
- [Scalable Exact Inference in Multi-Output Gaussian Processes](https://mlsanity.com/paper/1911.06287/) (2019)
- [An informative path planning framework for UAV-based terrain monitoring](https://mlsanity.com/paper/1809.03870/) (2018)
- [Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing](https://mlsanity.com/paper/2109.13570/) (2021)
- [Adaptive Informative Path Planning with Multimodal Sensing](https://mlsanity.com/paper/2003.09746/) (2020)
- [Sequential Bayesian Optimisation as a POMDP for Environment Monitoring with UAVs](https://mlsanity.com/paper/1703.04211/) (2017)
- [Efficient Planning under Uncertainty with Macro-actions](https://mlsanity.com/paper/1401.3827/) (2014)
- [Modeling Curiosity in a Mobile Robot for Long-Term Autonomous Exploration and Monitoring](https://mlsanity.com/paper/1509.07975/) (2015)
- [Informative Path Planning to Estimate Quantiles for Environmental Analysis](https://mlsanity.com/paper/2201.10633/) (2022)
- [Risk-aware Meta-level Decision Making for Exploration Under Uncertainty](https://mlsanity.com/paper/2209.05580/) (2022)

## Built on by (most cited first, 4 of 4)

- [Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning](https://mlsanity.com/paper/2402.04894/) (2024)
- [Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning](https://mlsanity.com/paper/2312.04402/) (2023)
- [Scalable Multi-Robot Informative Path Planning for Target Mapping via Deep Reinforcement Learning](https://mlsanity.com/paper/2409.16967/) (2024)
- [MEXGEN: An Effective and Efficient Information Gain Approximation for Information Gathering Path Planning](https://mlsanity.com/paper/2405.02605/) (2024)

## Abstract (arXiv)

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.