![]() To address this, our work introduces a general framework for surveying terrain characteristics using an aerial robot. Despite recent advances in this field, most real robot systems today still perform data acquisition in a passive manner, e.g., using coverage-based planning (Galceran and Carreras 2013), as current IPP solutions tend to be limited to specific platforms or application domains. This is known as the informative path planning (IPP) problem, which is the subject of much recent work. Therefore, paths need to be planned to maximize the information gathered about an unknown environment while satisfying the given budget constraint. A key challenge arises in that practical devices are subject to a finite quantity of sensing resources, such as energy, time, or travel distance, which limits the number of measurements that can be collected. The era of robotics-based monitoring has opened many interesting areas of research. 2014, 2017 Girdhar and Dudek 2015) applications, these devices are replacing traditional data acquisition campaigns based on static sensors, manual sampling, or conventional manned platforms, which can be unreliable, costly, and even dangerous (Dunbabin and Marques 2012 Manfreda et al. 2016 Colomina and Molina 2014) and aquatic (Hitz et al. In the past several decades, rapid technological advances have unlocked their potential as a flexible, cost-efficient tool enabling monitoring at unprecedented levels of resolution and autonomy. We also demonstrate its real-time application on a photorealistic mapping scenario using a publicly available dataset and a proof of concept for an agricultural monitoring task.Īutonomous mobile robots are increasingly employed to gather valuable scientific data about the Earth. Extensive simulations show that our approach is more efficient than existing methods. 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. 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. To address this issue, this article introduces a general informative path planning 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. ![]() To fully leverage their potential, a key challenge is planning missions for efficient data acquisition in complex environments. ![]() Unmanned aerial vehicles represent a new frontier in a wide range of monitoring and research applications. ![]()
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