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.

通过开发计算上高效且有效的算法，我们为在不确定信念状态下预测可能的传感器测量结果这一困难问题提供了一个更精确的近似解。实验结果表明，在多旋翼飞行器的广泛模拟和实地实验中，我们的方法在无线电源跟踪和定位问题中取得了改进的性能增益。