An informative path planning framework for UAV-based terrain monitoring

• Published in: Autonomous robots Vol. 44; no. 6; pp. 889 - 911
• Main Authors: Popović, Marija, Vidal-Calleja, Teresa, Hitz, Gregory, Chung, Jen Jen, Sa, Inkyu, Siegwart, Roland, Nieto, Juan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2020; Springer Nature B.V
• Subjects: Artificial Intelligence; Computer Imaging; Continuity (mathematics); Control; Engineering; Mechatronics; Monitoring; Pattern Recognition and Graphics; Planning; Robotics; Robotics and Automation; Terrain mapping; Trajectory planning; Unmanned aerial vehicles; Vision; Informative path planning; Environmental monitoring; Aerial robotics; Remote sensing
• ISSN: 0929-5593; 1573-7527
• DOI: 10.1007/s10514-020-09903-2

Unmanned aerial vehicles 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 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 a proof of concept for an agricultural monitoring task.