An informative path planning framework for UAV-based terrain monitoring

Abstract

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.

A Bayesian Optimization Approach for Water Resources Monitoring Through an Autonomous Surface Vehicle: The Ypacarai Lake Case Study

“…The obtained results indicate that the Bayesian approach is useful whenever the number of measurements is limited. In [22], continuous 3D trajectories are generated to map an area using Gaussian Processes. Similarly, a path planning model is proposed in [23], where an interactive multiple model algorithm is used to update the belief space.…”

Monitoring Water Resources through a Bayesian Optimization-based Approach using Multiple Surface Vehicles: The Ypacarai Lake Case Study

“…Such is the case of the work found in [12], where an adaptive Informative Path Planning (IPP) system is proposed using a single ASV and a roadmap to multi-ASV implementation. In [13], a single Autonomous Aerial Vehicle (AAV) is used to monitor terrain environments using a covariance matrix adaptation evolution strategy as main algorithm and GPs as the underlying surrogate model. Using a single ASV for monitoring the Ypacarai Lake are proposed by several works.…”

Learning control for transmission and navigation with a mobile robot under unknown communication rates

“…This problem is often solved dynamically, to obtain so-called informative path planning, see e.g. Meera et al (2019) and Viseras et al (2016); Popovic et al (2018) provide a good overview of this field. Closer to the present work, Fink and Kumar (2010); Penumarthi et al (2017) learn a radio map with multiple mobile robots.…”