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

Autonomous robots for gathering information on objects of interest has numerous real-world applications because of they improve efficiency, performance and safety. Realising 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 localisation problems using extensive simulated and field experiments with a multirotor aerial robot.