An informative path planning framework for UAV-based terrain monitoring
This addresses the challenge of efficient data acquisition for UAV-based monitoring in complex environments, representing an incremental improvement over existing methods.
The paper tackles the problem of planning efficient UAV missions for terrain monitoring by introducing an informative path planning framework that learns and focuses on regions of interest, showing in simulations that it is more efficient than existing methods and demonstrating real-time application on photorealistic mapping and agricultural monitoring tasks.
Unmanned Aerial Vehicles (UAVs) represent a new frontier in a wide range of monitoring and research applications. To fully leverage their potential, a key challenge is planning missions for efficient data acquisition in complex environments. To address this issue, this article introduces a general Informative Path Planning (IPP) framework for monitoring scenarios using an aerial robot, focusing on problems in which the value of sensor information is unevenly distributed in a target area and unknown a priori . The approach is capable of learning and focusing on regions of interest via adaptation to map either discrete or continuous variables on the terrain using variable-resolution data received from probabilistic sensors. During a mission, the terrain maps built online are used to plan information-rich trajectories in continuous 3-D space by optimizing initial solutions obtained by a coarse grid search. Extensive simulations show that our approach is more efficient than existing methods. We also demonstrate its real-time application on a photorealistic mapping scenario using a publicly available dataset and demonstrate a proof of concept for an agricultural monitoring task.