MEXGEN: An Effective and Efficient Information Gain Approximation for Information Gathering Path Planning
This work addresses the challenge of online planning for information gathering in robotics, offering incremental improvements in efficiency and accuracy for applications like aerial surveillance.
The paper tackles the problem of predicting information gain for autonomous robots in uncertain environments by developing an efficient approximation method, achieving lower prediction error and improved performance in radio-source tracking and localization experiments.
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.