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.

翻译：自主机器人采集感兴趣目标信息具有众多实际应用，因其能提升效率、性能与安全性。实现自主性需要在线规划算法来解决不确定性下的序列决策问题，因为感兴趣目标通常是动态的，其状态（如位置）无法直接观测，需从含噪测量中获取。此类规划问题由于需要预测未来以做出最优决策的组合特性而极为困难。针对信息论规划算法，我们提出了一种计算高效且有效的近似方法，用于解决从不确相信状态预测可能传感器测量值这一难题。该方法能更准确地预测信息采集动作带来的信息增益。理论分析证明，所提公式的预测误差低于现有高效方法。我们通过多旋翼飞行机器人的大量仿真与现场实验，验证了该方法在无线电源追踪与定位问题中的性能提升。