MEXGEN: An Effective and Efficient Information Gain Approximation for Information Gathering Path Planning

TL;DR

This paper introduces MEXGEN, a novel approximation method for information gain in autonomous robot planning, improving prediction accuracy and efficiency in dynamic, uncertain environments, validated through simulations and real-world experiments.

Contribution

MEXGEN provides a new computationally efficient approximation for predicting sensor measurement information gain, outperforming existing methods in accuracy and effectiveness.

Findings

Improved information gain prediction accuracy.

Enhanced performance in radio-source tracking and localization.

Validated through extensive simulations and real-world tests.

Abstract

Autonomous robots for gathering information on objects of interest has numerous real-world applications because of they improve efficiency, performance and safety. Realizing autonomy demands online planning algorithms to solve sequential decision making problems under uncertainty; because, objects of interest are often dynamic, object state, such as location is not directly observable and are obtained from noisy measurements. Such planning problems are notoriously difficult due to the combinatorial nature of predicting the future to make optimal decisions. For information theoretic planning algorithms, we develop a computationally efficient and effective approximation for the difficult problem of predicting the likely sensor measurements from uncertain belief states}. The approach more accurately predicts information gain from information gathering actions. Our theoretical analysis proves the proposed formulation achieves a lower prediction error than the current efficient-method. We demonstrate improved performance gains in radio-source tracking and localization problems using extensive simulated and field experiments with a multirotor aerial robot.