Sequential Bayesian Optimization for Adaptive Informative Path Planning with Multimodal Sensing
This addresses the challenge of efficient environmental monitoring for autonomous agents with limited resources, representing an incremental advance over prior work by incorporating multimodal sensing.
The paper tackles the Adaptive Informative Path Planning with Multimodal Sensing (AIPPMS) problem, where an agent with multiple sensors must explore unknown environments under resource constraints, and it achieves more than double the average reward and reduces root-mean-square error by 50% compared to previous solutions.
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.